From 3df9fb7ccab5c225db7617591623eefb0fa73611 Mon Sep 17 00:00:00 2001 From: Pei-Yun Sun Date: Mon, 19 Oct 2020 23:57:37 +1100 Subject: [PATCH] add web app (incomplete) --- .env | 3 + app.py | 23 + config.py | 50 + controller/__init__.py | 24 + controller/feedback_controller.py | 32 + controller/general_controller.py | 190 + controller/prediction_controller.py | 73 + deepface/DeepFaceLite.py | 101 + deepface/commons/functions.py | 1113 ++--- deepface/commons/functionsLite.py | 769 +++ deepface_lite.ipynb | 143 + extension/constants.py | 84 + extension/utilServices.py | 14 + font/FiraMono-Medium.otf | Bin 0 -> 127344 bytes font/SIL Open Font License.txt | 45 + models/__init__.py | 0 models/feedback.py | 15 + models/prediction.py | 8 + my_deepface.ipynb | 754 +++ static/css/bootstrap.css | 6757 +++++++++++++++++++++++++++ static/css/bootstrap.css.map | 1 + static/css/bootstrap.min.css | 6 + static/css/bootstrap.min.css.map | 1 + static/css/fileinput.css | 550 +++ static/css/fileinput.min.css | 12 + static/css/homePage.css | 59 + static/img/cat_smile.jpg | Bin 0 -> 67179 bytes static/img/dog_smile.jpg | Bin 0 -> 154540 bytes static/img/loading-sm.gif | Bin 0 -> 2670 bytes static/img/loading.gif | Bin 0 -> 847 bytes static/js/bootstrap.js | 2377 ++++++++++ static/js/bootstrap.min.js | 7 + static/js/fileinput.js | 5746 +++++++++++++++++++++++ static/js/fileinput.min.js | 13 + static/js/jQuery.min.js | 2 + templates/base.html | 62 + templates/breedPage.html | 32 + templates/homePage.html | 90 + templates/statisticalPage.html | 263 ++ views/__init__.py | 4 + views/public_route.py | 18 + yolov3_with_emo.py | 278 ++ 42 files changed, 19189 insertions(+), 530 deletions(-) create mode 100644 .env create mode 100644 app.py create mode 100644 config.py create mode 100644 controller/__init__.py create mode 100644 controller/feedback_controller.py create mode 100644 controller/general_controller.py create mode 100644 controller/prediction_controller.py create mode 100644 deepface/DeepFaceLite.py create mode 100644 deepface/commons/functionsLite.py create mode 100644 deepface_lite.ipynb create mode 100644 extension/constants.py create mode 100644 extension/utilServices.py create mode 100644 font/FiraMono-Medium.otf create mode 100644 font/SIL Open Font License.txt create mode 100644 models/__init__.py create mode 100644 models/feedback.py create mode 100644 models/prediction.py create mode 100644 my_deepface.ipynb create mode 100644 static/css/bootstrap.css create mode 100644 static/css/bootstrap.css.map create mode 100644 static/css/bootstrap.min.css create mode 100644 static/css/bootstrap.min.css.map create mode 100644 static/css/fileinput.css create mode 100644 static/css/fileinput.min.css create mode 100644 static/css/homePage.css create mode 100644 static/img/cat_smile.jpg create mode 100644 static/img/dog_smile.jpg create mode 100644 static/img/loading-sm.gif create mode 100644 static/img/loading.gif create mode 100644 static/js/bootstrap.js create mode 100644 static/js/bootstrap.min.js create mode 100644 static/js/fileinput.js create mode 100644 static/js/fileinput.min.js create mode 100644 static/js/jQuery.min.js create mode 100644 templates/base.html create mode 100644 templates/breedPage.html create mode 100644 templates/homePage.html create mode 100644 templates/statisticalPage.html create mode 100644 views/__init__.py create mode 100644 views/public_route.py create mode 100644 yolov3_with_emo.py diff --git a/.env b/.env new file mode 100644 index 0000000..1df68cc --- /dev/null +++ b/.env @@ -0,0 +1,3 @@ +export HAP_DB_NAME=hap +export HAP_DB_HOST=localhost +export HAP_DB_PORT=27017 \ No newline at end of file diff --git a/app.py b/app.py new file mode 100644 index 0000000..e4034cc --- /dev/null +++ b/app.py @@ -0,0 +1,23 @@ +from config import create_app, db +from views import public_route_bp +from controller import api_bp_new, api_bp_old + + +app = create_app() +""" +register blueprint +""" +app.register_blueprint(public_route_bp) +app.register_blueprint(api_bp_old) +app.register_blueprint(api_bp_new, url_prefix='/api') + +app.app_context().push() + + +def main(): + app.run(debug=True, host="0.0.0.0", port=5000) # RUNNING APP MAKE debug =FALSE for Production Env + # app.run(debug=True, host="localhost", port=8888) + + +if __name__ == "__main__": + main() diff --git a/config.py b/config.py new file mode 100644 index 0000000..36f5f05 --- /dev/null +++ b/config.py @@ -0,0 +1,50 @@ +from flask import Flask +from flask_mongoengine import MongoEngine +from deepface import DeepFaceLite +# import tensorflow as tf +# from tensorflow.python.keras.backend import set_session +import os + +""" +initialize the ML engine +""" +db = MongoEngine() +# sess = tf.Session() +# graph = tf.get_default_graph() +# set_session(sess) +deepface = DeepFaceLite() + +""" +database environment variable +""" +DB_NAME = os.environ.get('HAP_DB_NAME') +DB_HOST = os.environ.get('HAP_DB_HOST') +DB_PORT = os.environ.get('HAP_DB_PORT') +DB_USERNAME = os.environ.get('HAP_DB_USERNAME') +DB_PASSWORD = os.environ.get('HAP_DB_PASSWORD') + + +def create_app(): + """ + return app instance + :return: app instance + """ + app = Flask(__name__) + if DB_USERNAME is not None and DB_PASSWORD is not None: + app.config['MONGODB_SETTINGS'] = { + 'db': DB_NAME, + 'host': DB_HOST, + 'port': int(DB_PORT), + 'username': DB_USERNAME, + 'password': DB_PASSWORD + } + else: + app.config['MONGODB_SETTINGS'] = { + 'db': DB_NAME, + 'host': DB_HOST, + 'port': int(DB_PORT) + } + + db.init_app(app) + + return app diff --git a/controller/__init__.py b/controller/__init__.py new file mode 100644 index 0000000..3e4ec04 --- /dev/null +++ b/controller/__init__.py @@ -0,0 +1,24 @@ +from flask_restplus import Api +from flask import Blueprint +from controller.prediction_controller import api as predict_apis +from controller.feedback_controller import api as feedback_apis +from controller.general_controller import api as general_apis + + +# old api, will be discard later +api_bp_old = Blueprint('api_old', __name__) + +api = Api(api_bp_old) + +api.add_namespace(predict_apis) +api.add_namespace(general_apis) + +""" +new api with /api/ path +""" +api_bp_new = Blueprint('api_new', __name__) + +api_new = Api(api_bp_new) +api_new.add_namespace(general_apis) +api_new.add_namespace(predict_apis) +api_new.add_namespace(feedback_apis) diff --git a/controller/feedback_controller.py b/controller/feedback_controller.py new file mode 100644 index 0000000..d93909a --- /dev/null +++ b/controller/feedback_controller.py @@ -0,0 +1,32 @@ +from flask import request +from extension.utilServices import send_json_response +from flask_restplus import Namespace, Resource +import datetime +api = Namespace('feedback', path='/feedback', description='prediction related operations') + + +@api.route('') +class Feedback(Resource): + @api.doc('feedback related api') + def post(self): + """ + store user feedbacks to the database + :return: operation results + """ + from models.feedback import Feedback, FeedbackContent + + message = request.get_json(force=True) + + try: + new_feedback = Feedback() + new_feedback_content = [] + for each_content in message['data']: + feedback_content = FeedbackContent(**each_content) + new_feedback_content.append(feedback_content) + + new_feedback.content = new_feedback_content + new_feedback.date = datetime.datetime.now() + new_feedback.save() + return send_json_response({}, 200) + except Exception as error: + return send_json_response({'msg': str(error)}, 500) diff --git a/controller/general_controller.py b/controller/general_controller.py new file mode 100644 index 0000000..2c8c4ae --- /dev/null +++ b/controller/general_controller.py @@ -0,0 +1,190 @@ +from extension.utilServices import (send_json_response) +from extension.constants import CONSTANTS +import csv +import re +from flask_restplus import Namespace, Resource +import datetime +import copy + +api = Namespace('general', path='/', description='general information related to app') + + +# @api.route('/get-breed-info') +# class BreedList(Resource): +# @api.doc('get breed information from csv') +# def get(self): +# """ +# return the breed information, which gets from wikipedia +# :return: list of breed information +# """ +# breed_info = {} +# for animal_type in CONSTANTS['ANIMAL_TYPE']: +# with open('wikiFile/' + animal_type + '.csv') as csv_file: +# csv_reader = csv.reader(csv_file, delimiter=',') +# line_count = 0 +# json_title = [] +# animal_list = [] +# for row in csv_reader: +# if line_count == 0: +# json_title = row +# line_count += 1 +# else: +# info_obj = { +# json_title[0]: row[0], +# json_title[1]: row[1], +# json_title[2]: row[2], +# json_title[3]: row[3], +# json_title[4]: row[4], +# } +# animal_list.append(info_obj) +# line_count += 1 +# csv_file.close() +# breed_info[animal_type] = animal_list + +# return send_json_response(breed_info, 200) + + +@api.route('/get-app-info') +class AppInfo(Resource): + @api.doc('return the app developer information') + def get(self): + """ + return the footer information + :return: return the app related information + """ + app_info = { + 'developedBy': 'This app was developed by the Melbourne eResearch Group (www.eresearch.unimelb.edu.au) within the School of Computing and Information Systems (https://cis.unimelb.edu.au) at The University of Melbourne (www.unimelb.edu.au). ', + 'description': 'The app uses artificial intelligence (convolutional neural networks) that have been trained on dog/cat images to identify whether a dog/cat is in an image, and if so the species type (breed) and it\'s emotion.', + 'contact': 'https://eresearch.unimelb.edu.au', + 'developedByHTML': '

This app was developed by the Melbourne eResearch Group (www.eresearch.unimelb.edu.au) within the School of Computing and Information Systems (https://cis.unimelb.edu.au) at The University of Melbourne (www.unimelb.edu.au).

', + 'descriptionHTML': '

The app uses artificial intelligence (convolutional neural networks) that have been trained on dog/cat images to identify whether a dog/cat is in an image, and if so the species type (breed) and it\'s emotion.

', + 'contactHTML': '

Please contact us at: eresearch.unimelb.edu.au

' + } + + return send_json_response(app_info, 200) + + +@api.route('/get-statistical-results') +class StatisticalData(Resource): + @api.doc('return statistical data') + def get(self): + """ + return the statistical information + query across feedback and prediction model + :return: return statistical information + """ + from models.prediction import Prediction + from models.feedback import Feedback + + total_cat_breed = [] + total_dog_breed = [] + prediction_collection = Prediction._get_collection() + feedback_collection = Feedback._get_collection() + statistical_data = copy.deepcopy(CONSTANTS['STATISTICAL_DATA']) + + # initial statistical_data based on breed and alpha order + f = open("model_data/pet_classes.txt", "r") + for each_line in f: + first_char = each_line[0] + if first_char.isupper(): + total_cat_breed.append(re.sub(r"_", " ", each_line.rstrip(), flags=re.IGNORECASE).title()) + else: + total_dog_breed.append(re.sub(r"_", " ", each_line.rstrip(), flags=re.IGNORECASE).title()) + f.close() + total_cat_breed.sort() + total_dog_breed.sort() + + for each_breed in total_cat_breed: + statistical_data['Cat']['prediction_data']['breed'][each_breed] = 0 + statistical_data['Cat']['feedback_data']['breed']['wrong'][each_breed] = 0 + statistical_data['Cat']['feedback_data']['breed']['correct'][each_breed] = 0 + + for each_breed in total_dog_breed: + statistical_data['Dog']['prediction_data']['breed'][each_breed] = 0 + statistical_data['Dog']['feedback_data']['breed']['wrong'][each_breed] = 0 + statistical_data['Dog']['feedback_data']['breed']['correct'][each_breed] = 0 + + # photo by date + today = datetime.datetime.today().replace(hour=0, minute=0, second=0, microsecond=0) + total_number_of_photo_within_one_week = { + (today - datetime.timedelta(days=6)).strftime('%d/%m/%Y'): prediction_collection.count_documents( + {'date': {'$lt': today - datetime.timedelta(days=5), '$gte': today - datetime.timedelta(days=6)}}), + (today - datetime.timedelta(days=5)).strftime('%d/%m/%Y'): prediction_collection.count_documents( + {'date': {'$lt': today - datetime.timedelta(days=4), '$gte': today - datetime.timedelta(days=5)}}), + (today - datetime.timedelta(days=4)).strftime('%d/%m/%Y'): prediction_collection.count_documents( + {'date': {'$lt': today - datetime.timedelta(days=3), '$gte': today - datetime.timedelta(days=4)}}), + (today - datetime.timedelta(days=3)).strftime('%d/%m/%Y'): prediction_collection.count_documents( + {'date': {'$lt': today - datetime.timedelta(days=2), '$gte': today - datetime.timedelta(days=3)}}), + (today - datetime.timedelta(days=2)).strftime('%d/%m/%Y'): prediction_collection.count_documents( + {'date': {'$lt': today - datetime.timedelta(days=1), '$gte': today - datetime.timedelta(days=2)}}), + (today - datetime.timedelta(days=1)).strftime('%d/%m/%Y'): prediction_collection.count_documents( + {'date': {'$lt': today, '$gte': today - datetime.timedelta(days=1)}}), + today.strftime('%d/%m/%Y'): prediction_collection.count_documents( + {'date': {'$gte': today}}), + } + + # prediction + total_prediction = prediction_collection.find({}) + for each_prediction in total_prediction: + prediction_results = each_prediction['predictionResults'] + for each_result in prediction_results: + statistical_data[each_result['type']]['prediction_number'] = statistical_data[each_result['type']].get( + 'prediction_number', 0) + 1 + statistical_data[each_result['type']]['prediction_data']['breed'][each_result['breed']] = \ + statistical_data[each_result['type']]['prediction_data']['breed'].get(each_result['breed'], 0) + 1 + statistical_data[each_result['type']]['prediction_data']['emotion'][each_result['emotion']] = \ + statistical_data[each_result['type']]['prediction_data']['emotion'].get(each_result['emotion'], + 0) + 1 + # feedback + total_feedback = feedback_collection.find({}) + + for each_feedback in total_feedback: + feedback_content = each_feedback['content'] + for each_content in feedback_content: + statistical_data[each_content['type']]['feedback_number'] = statistical_data[each_content['type']].get( + 'feedback_number', 0) + 1 + + if not each_content['breedCorrectness']: + statistical_data[each_content['type']]['feedback_data']['breed']['wrong'][ + each_content['breedFeedback']] = \ + statistical_data[each_content['type']]['feedback_data']['breed'][ + 'wrong'].get(each_content['breedFeedback'], 0) + 1 + else: + statistical_data[each_content['type']]['feedback_data']['breed']['correct'][ + each_content['breedFeedback']] = \ + statistical_data[each_content['type']]['feedback_data']['breed']['correct'].get( + each_content['breedFeedback'], 0) + 1 + + if not each_content['emotionCorrectness']: + statistical_data[each_content['type']]['feedback_data']['emotion']['wrong'][ + each_content['emotionFeedback']] = \ + statistical_data[each_content['type']]['feedback_data']['emotion']['wrong'].get( + each_content['emotionFeedback'], 0) + 1 + else: + statistical_data[each_content['type']]['feedback_data']['emotion']['correct'][ + each_content['emotionFeedback']] = \ + statistical_data[each_content['type']]['feedback_data']['emotion']['correct'].get( + each_content['emotionFeedback'], 0) + 1 + + result = { + 'totalNumberOfPhotoUploaded': prediction_collection.count_documents({}), + 'totalNumberOfCatPrediction': statistical_data['Cat']['prediction_number'], + 'totalNumberOfDogPrediction': statistical_data['Dog']['prediction_number'], + 'totalNumberOfCatFeedback': statistical_data['Cat']['feedback_number'], + 'totalNumberOfDogFeedback': statistical_data['Dog']['feedback_number'], + 'totalNumberOfDogBreedPrediction': statistical_data['Dog']['prediction_data']['breed'], + 'totalNumberOfDogBreedCorrectFeedback': statistical_data['Dog']['feedback_data']['breed']['correct'], + 'totalNumberOfDogBreedWrongFeedback': statistical_data['Dog']['feedback_data']['breed']['wrong'], + 'totalNumberOfDogEmotionPrediction': statistical_data['Dog']['prediction_data']['emotion'], + 'totalNumberOfDogEmotionCorrectFeedback': statistical_data['Dog']['feedback_data']['emotion']['correct'], + 'totalNumberOfDogEmotionWrongFeedback': statistical_data['Dog']['feedback_data']['emotion']['wrong'], + 'totalNumberOfCatBreedPrediction': statistical_data['Cat']['prediction_data']['breed'], + 'totalNumberOfCatBreedCorrectFeedback': statistical_data['Cat']['feedback_data']['breed']['correct'], + 'totalNumberOfCatBreedWrongFeedback': statistical_data['Cat']['feedback_data']['breed']['wrong'], + 'totalNumberOfCatEmotionPrediction': statistical_data['Cat']['prediction_data']['emotion'], + 'totalNumberOfCatEmotionCorrectFeedback': statistical_data['Cat']['feedback_data']['emotion']['correct'], + 'totalNumberOfCatEmotionWrongFeedback': statistical_data['Cat']['feedback_data']['emotion']['wrong'], + 'numberOfPhotoByDate': total_number_of_photo_within_one_week + } + + return send_json_response(result, 200) diff --git a/controller/prediction_controller.py b/controller/prediction_controller.py new file mode 100644 index 0000000..ac522f5 --- /dev/null +++ b/controller/prediction_controller.py @@ -0,0 +1,73 @@ +from flask import request +import base64 +from PIL import Image +import io +import datetime +from extension.utilServices import send_json_response +from flask_restplus import Namespace, Resource + + +api = Namespace('predict', path='/predict', description='prediction related operations') + + +@api.route('') +class Prediction(Resource): + @api.doc('make prediction') + def post(self): + """ + return prediction results and save it to the database + :return: prediction results + """ + from models.prediction import Prediction + from config import deepface + message = request.get_json(force=True) + encoded = message['image'] + decoded = base64.b64decode(encoded) + image = Image.open(io.BytesIO(decoded)).convert('RGB') + + img, detections = deepface.analyze(image) + + # TODO: handle outputs + # encode image and jsonify detections + buffered = io.BytesIO() + img.save(buffered, format="JPEG") + img_str = base64.b64encode(buffered.getvalue()) + base64_string = img_str.decode('utf-8') + + result = { + 'img_str': base64_string, + 'results': detections, + 'message': '', + 'status': 'success' + } + + if len(detections) == 0: + result['message'] = "We’re not very sure of what this may be, could you try with another image", + result['status'] = 'failure' + elif len(detections) == 1: + result['isShowId'] = 'false' + + if len(detections) > 0: + formatted_prediction_results = [] + for each in detections: + age = each['age'] + gender = each['gender'] + emotion = each['emotion']['dominant'] + emotion_score = each['emotion']['dominant_score'] + formatted_prediction_results.append({ + 'age': age, + 'gender': gender, + 'emotion': emotion, + 'emotionScore': emotion_score + }) + + # store to db? + + # new_prediction = Prediction(**{ + # 'predictionResults': formatted_prediction_results, + # 'rawPredictionResults': detections, + # 'date': datetime.datetime.now(), + # }) + # new_prediction.save() + + return send_json_response(result, 200) diff --git a/deepface/DeepFaceLite.py b/deepface/DeepFaceLite.py new file mode 100644 index 0000000..6cabf8f --- /dev/null +++ b/deepface/DeepFaceLite.py @@ -0,0 +1,101 @@ +from keras.preprocessing import image +import warnings +warnings.filterwarnings("ignore") +import time +import os +from os import path +from pathlib import Path +import gdown +import numpy as np +import pandas as pd +from tqdm import tqdm +import json +import cv2 +from keras import backend as K +import keras +import tensorflow as tf +import pickle + +from deepface import DeepFace +from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID +from deepface.extendedmodels import Age, Gender, Race, Emotion +from deepface.commons import functionsLite, realtime, distance as dst + + +class DeepFaceLite(object): + + def __init__(self): + + functionsLite.initializeFolder() + + # init models + self.detector_backend = 'mtcnn' + self.emotion_model = Emotion.loadModel() + self.age_model = Age.loadModel() + self.gender_model = Gender.loadModel() + + # TODO: init detector + + + def analyze(self, img, enforce_detection = True, detector_backend = 'opencv'): + + # preprocess images + processed = functionsLite.preprocess_face(img, enforce_detection=enforce_detection, detector_backend=detector_backend) + imgs_224 = processed['processed'] + emotion_imgs = processed['gray'] + bbox_img = processed['bbox'] + # original_faces = processed['original'] + + resp_objects = [] + + # iterate through faces + for i in range(len(imgs_224)): + + resp_obj = {} + + # --- emotion --- + emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'] + + emotion_predictions = self.emotion_model.predict(emotion_imgs[i])[0,:] + + sum_of_predictions = emotion_predictions.sum() + + all_emotions = {} + + for i in range(0, len(emotion_labels)): + emotion_label = emotion_labels[i] + emotion_prediction = 100 * emotion_predictions[i] / sum_of_predictions + emotion[emotion_label] = emotion_prediction + + emotion = { + 'all': all_emotions, + 'dominant': emotion_labels[np.argmax(emotion_predictions)], + 'dominant_score': np.max(emotion_predictions) + } + + # --- age --- + age_predictions = self.age_model.predict(imgs_224[i])[0,:] + apparent_age = Age.findApparentAge(age_predictions) + + # --- gender --- + gender_prediction = self.gender_model.predict(imgs_224[i])[0,:] + + if np.argmax(gender_prediction) == 0: + gender = "Woman" + elif np.argmax(gender_prediction) == 1: + gender = "Man" + + # resp_obj = json.loads(resp_obj) + + resp_obj = { + 'age': apparent_age, + 'gender': gender, + 'emotion': emotion + } + + resp_objects.append(resp_obj) + + return bbox_img, resp_objects + + + diff --git a/deepface/commons/functions.py b/deepface/commons/functions.py index 135546d..a71193d 100644 --- a/deepface/commons/functions.py +++ b/deepface/commons/functions.py @@ -18,548 +18,601 @@ import tensorflow as tf import keras import bz2 from deepface.commons import distance -from mtcnn import MTCNN #0.1.0 +from mtcnn import MTCNN # 0.1.0 + def loadBase64Img(uri): - encoded_data = uri.split(',')[1] - nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8) - img = cv2.imdecode(nparr, cv2.IMREAD_COLOR) - return img + encoded_data = uri.split(',')[1] + nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8) + img = cv2.imdecode(nparr, cv2.IMREAD_COLOR) + return img + def initializeFolder(): - - home = str(Path.home()) - - if not os.path.exists(home+"/.deepface"): - os.mkdir(home+"/.deepface") - print("Directory ",home,"/.deepface created") - - if not os.path.exists(home+"/.deepface/weights"): - os.mkdir(home+"/.deepface/weights") - print("Directory ",home,"/.deepface/weights created") - + home = str(Path.home()) + + if not os.path.exists(home + "/.deepface"): + os.mkdir(home + "/.deepface") + print("Directory ", home, "/.deepface created") + + if not os.path.exists(home + "/.deepface/weights"): + os.mkdir(home + "/.deepface/weights") + print("Directory ", home, "/.deepface/weights created") + + def findThreshold(model_name, distance_metric): - - threshold = 0.40 - - if model_name == 'VGG-Face': - if distance_metric == 'cosine': - threshold = 0.40 - elif distance_metric == 'euclidean': - threshold = 0.55 - elif distance_metric == 'euclidean_l2': - threshold = 0.75 - - elif model_name == 'OpenFace': - if distance_metric == 'cosine': - threshold = 0.10 - elif distance_metric == 'euclidean': - threshold = 0.55 - elif distance_metric == 'euclidean_l2': - threshold = 0.55 - - elif model_name == 'Facenet': - if distance_metric == 'cosine': - threshold = 0.40 - elif distance_metric == 'euclidean': - threshold = 10 - elif distance_metric == 'euclidean_l2': - threshold = 0.80 - - elif model_name == 'DeepFace': - if distance_metric == 'cosine': - threshold = 0.23 - elif distance_metric == 'euclidean': - threshold = 64 - elif distance_metric == 'euclidean_l2': - threshold = 0.64 - - elif model_name == 'DeepID': - if distance_metric == 'cosine': - threshold = 0.015 - elif distance_metric == 'euclidean': - threshold = 45 - elif distance_metric == 'euclidean_l2': - threshold = 0.17 - - elif model_name == 'Dlib': - if distance_metric == 'cosine': - threshold = 0.07 - elif distance_metric == 'euclidean': - threshold = 0.60 - elif distance_metric == 'euclidean_l2': - threshold = 0.60 - - return threshold + threshold = 0.40 + + if model_name == 'VGG-Face': + if distance_metric == 'cosine': + threshold = 0.40 + elif distance_metric == 'euclidean': + threshold = 0.55 + elif distance_metric == 'euclidean_l2': + threshold = 0.75 + + elif model_name == 'OpenFace': + if distance_metric == 'cosine': + threshold = 0.10 + elif distance_metric == 'euclidean': + threshold = 0.55 + elif distance_metric == 'euclidean_l2': + threshold = 0.55 + + elif model_name == 'Facenet': + if distance_metric == 'cosine': + threshold = 0.40 + elif distance_metric == 'euclidean': + threshold = 10 + elif distance_metric == 'euclidean_l2': + threshold = 0.80 + + elif model_name == 'DeepFace': + if distance_metric == 'cosine': + threshold = 0.23 + elif distance_metric == 'euclidean': + threshold = 64 + elif distance_metric == 'euclidean_l2': + threshold = 0.64 + + elif model_name == 'DeepID': + if distance_metric == 'cosine': + threshold = 0.015 + elif distance_metric == 'euclidean': + threshold = 45 + elif distance_metric == 'euclidean_l2': + threshold = 0.17 + + elif model_name == 'Dlib': + if distance_metric == 'cosine': + threshold = 0.07 + elif distance_metric == 'euclidean': + threshold = 0.60 + elif distance_metric == 'euclidean_l2': + threshold = 0.60 + + return threshold + def get_opencv_path(): - opencv_home = cv2.__file__ - folders = opencv_home.split(os.path.sep)[0:-1] - - path = folders[0] - for folder in folders[1:]: - path = path + "/" + folder - - return path+"/data/" + opencv_home = cv2.__file__ + folders = opencv_home.split(os.path.sep)[0:-1] + + path = folders[0] + for folder in folders[1:]: + path = path + "/" + folder + + return path + "/data/" + def load_image(img): - - exact_image = False - if type(img).__module__ == np.__name__: - exact_image = True - - base64_img = False - if len(img) > 11 and img[0:11] == "data:image/": - base64_img = True - - #--------------------------- - - if base64_img == True: - img = loadBase64Img(img) - - elif exact_image != True: #image path passed as input - if os.path.isfile(img) != True: - raise ValueError("Confirm that ",img," exists") - - img = cv2.imread(img) - - return img - -def detect_face(img, detector_backend = 'opencv', grayscale = False, enforce_detection = True): - - home = str(Path.home()) - - if detector_backend == 'opencv': - - #get opencv configuration up first - opencv_path = get_opencv_path() - face_detector_path = opencv_path+"haarcascade_frontalface_default.xml" - - if os.path.isfile(face_detector_path) != True: - raise ValueError("Confirm that opencv is installed on your environment! Expected path ",face_detector_path," violated.") - - face_detector = cv2.CascadeClassifier(face_detector_path) - - #-------------------------- - - faces = [] - - try: - faces = face_detector.detectMultiScale(img, 1.3, 5) - except: - pass - - if len(faces) > 0: - x,y,w,h = faces[0] #focus on the 1st face found in the image - detected_face = img[int(y):int(y+h), int(x):int(x+w)] - return detected_face - - else: #if no face detected - - if enforce_detection != True: - return img - - else: - raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.") + exact_image = False + if type(img).__module__ == np.__name__: + exact_image = True + + base64_img = False + if len(img) > 11 and img[0:11] == "data:image/": + base64_img = True + + # --------------------------- + + if base64_img == True: + img = loadBase64Img(img) + + elif exact_image != True: # image path passed as input + if os.path.isfile(img) != True: + raise ValueError("Confirm that ", img, " exists") + + img = cv2.imread(img) + + return img + + +def detect_face(img, detector_backend='opencv', grayscale=False, enforce_detection=True): + home = str(Path.home()) + + if detector_backend == 'opencv': + + # get opencv configuration up first + opencv_path = get_opencv_path() + face_detector_path = opencv_path + "haarcascade_frontalface_default.xml" + + if os.path.isfile(face_detector_path) != True: + raise ValueError("Confirm that opencv is installed on your environment! Expected path ", face_detector_path, + " violated.") + + face_detector = cv2.CascadeClassifier(face_detector_path) + + # -------------------------- + + faces = [] + + try: + faces = face_detector.detectMultiScale(img, 1.3, 5) + except: + pass + + if len(faces) > 0: + detected_faces = [] + for face in faces: + x, y, w, h = face + detected_face = img[int(y):int(y + h), int(x):int(x + w)] + detected_faces.append(detected_face) + return detected_faces + + else: # if no face detected + + if enforce_detection != True: + return img + + else: + raise ValueError( + "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.") + + elif detector_backend == 'ssd': + + # --------------------------- + # check required ssd model exists in the home/.deepface/weights folder + + # model structure + if os.path.isfile(home + '/.deepface/weights/deploy.prototxt') != True: + print("deploy.prototxt will be downloaded...") + + url = "https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt" + + output = home + '/.deepface/weights/deploy.prototxt' + + gdown.download(url, output, quiet=False) + + # pre-trained weights + if os.path.isfile(home + '/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel') != True: + print("res10_300x300_ssd_iter_140000.caffemodel will be downloaded...") + + url = "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel" + + output = home + '/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel' + + gdown.download(url, output, quiet=False) + + # --------------------------- + + ssd_detector = cv2.dnn.readNetFromCaffe( + home + "/.deepface/weights/deploy.prototxt", + home + "/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel" + ) + + ssd_labels = ["img_id", "is_face", "confidence", "left", "top", "right", "bottom"] + + target_size = (300, 300) + + base_img = img.copy() # we will restore base_img to img later + + original_size = img.shape + + img = cv2.resize(img, target_size) + + aspect_ratio_x = (original_size[1] / target_size[1]) + aspect_ratio_y = (original_size[0] / target_size[0]) + + imageBlob = cv2.dnn.blobFromImage(image=img) + + ssd_detector.setInput(imageBlob) + detections = ssd_detector.forward() + + detections_df = pd.DataFrame(detections[0][0], columns=ssd_labels) + + detections_df = detections_df[detections_df['is_face'] == 1] # 0: background, 1: face + detections_df = detections_df[detections_df['confidence'] >= 0.90] + + detections_df['left'] = (detections_df['left'] * 300).astype(int) + detections_df['bottom'] = (detections_df['bottom'] * 300).astype(int) + detections_df['right'] = (detections_df['right'] * 300).astype(int) + detections_df['top'] = (detections_df['top'] * 300).astype(int) + + if detections_df.shape[0] > 0: + + # TODO: sort detections_df + + detected_faces = [] + + for i in range(0, len(detections_df)): + instance = detections_df.iloc[i] + left = instance["left"] + right = instance["right"] + bottom = instance["bottom"] + top = instance["top"] + + detected_face = base_img[int(top * aspect_ratio_y):int(bottom * aspect_ratio_y), int(left * aspect_ratio_x):int(right * aspect_ratio_x)] + detected_faces.append(detected_face) + return detected_faces + + # # get the first face in the image + # instance = detections_df.iloc[0] + # + # left = instance["left"] + # right = instance["right"] + # bottom = instance["bottom"] + # top = instance["top"] + # + # detected_face = base_img[int(top * aspect_ratio_y):int(bottom * aspect_ratio_y), + # int(left * aspect_ratio_x):int(right * aspect_ratio_x)] + # + # return detected_face + + else: # if no face detected + + if enforce_detection != True: + img = base_img.copy() + return img + + else: + raise ValueError( + "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.") + + elif detector_backend == 'dlib': + import dlib # this is not a must library within deepface. that's why, I didn't put this import to a global level. version: 19.20.0 + + detector = dlib.get_frontal_face_detector() + + detections = detector(img, 1) + + if len(detections) > 0: + + detected_faces = [] + for idx, d in enumerate(detections): + left = d.left(); + right = d.right() + top = d.top(); + bottom = d.bottom() + + detected_face = img[top:bottom, left:right] + detected_faces.append(detected_face) + return detected_faces + # left = d.left(); + # right = d.right() + # top = d.top(); + # bottom = d.bottom() + # + # detected_face = img[top:bottom, left:right] + # + # return detected_face + else: # if no face detected + + if enforce_detection != True: + return img + + else: + raise ValueError( + "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.") + + elif detector_backend == 'mtcnn': + + mtcnn_detector = MTCNN() + + detections = mtcnn_detector.detect_faces(img) + + if len(detections) > 0: + detected_faces = [] + for detection in detections: + x, y, w, h = detection["box"] + detected_face = img[int(y):int(y + h), int(x):int(x + w)] + detected_faces.append(detected_face) + return detected_faces + + else: # if no face detected + if enforce_detection != True: + return img + + else: + raise ValueError( + "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.") + + else: + detectors = ['opencv', 'ssd', 'dlib', 'mtcnn'] + raise ValueError("Valid backends are ", detectors, " but you passed ", detector_backend) + + return 0 - elif detector_backend == 'ssd': - - #--------------------------- - #check required ssd model exists in the home/.deepface/weights folder - - #model structure - if os.path.isfile(home+'/.deepface/weights/deploy.prototxt') != True: - - print("deploy.prototxt will be downloaded...") - - url = "https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt" - - output = home+'/.deepface/weights/deploy.prototxt' - - gdown.download(url, output, quiet=False) - - - #pre-trained weights - if os.path.isfile(home+'/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel') != True: - - print("res10_300x300_ssd_iter_140000.caffemodel will be downloaded...") - - url = "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel" - - output = home+'/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel' - - gdown.download(url, output, quiet=False) - - #--------------------------- - - ssd_detector = cv2.dnn.readNetFromCaffe( - home+"/.deepface/weights/deploy.prototxt", - home+"/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel" - ) - - ssd_labels = ["img_id", "is_face", "confidence", "left", "top", "right", "bottom"] - - target_size = (300, 300) - - base_img = img.copy() #we will restore base_img to img later - - original_size = img.shape - - img = cv2.resize(img, target_size) - - aspect_ratio_x = (original_size[1] / target_size[1]) - aspect_ratio_y = (original_size[0] / target_size[0]) - - imageBlob = cv2.dnn.blobFromImage(image = img) - - ssd_detector.setInput(imageBlob) - detections = ssd_detector.forward() - - detections_df = pd.DataFrame(detections[0][0], columns = ssd_labels) - - detections_df = detections_df[detections_df['is_face'] == 1] #0: background, 1: face - detections_df = detections_df[detections_df['confidence'] >= 0.90] - - detections_df['left'] = (detections_df['left'] * 300).astype(int) - detections_df['bottom'] = (detections_df['bottom'] * 300).astype(int) - detections_df['right'] = (detections_df['right'] * 300).astype(int) - detections_df['top'] = (detections_df['top'] * 300).astype(int) - - if detections_df.shape[0] > 0: - - #TODO: sort detections_df - - #get the first face in the image - instance = detections_df.iloc[0] - - left = instance["left"] - right = instance["right"] - bottom = instance["bottom"] - top = instance["top"] - - detected_face = base_img[int(top*aspect_ratio_y):int(bottom*aspect_ratio_y), int(left*aspect_ratio_x):int(right*aspect_ratio_x)] - - return detected_face - - else: #if no face detected - - if enforce_detection != True: - img = base_img.copy() - return img - - else: - raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.") - - elif detector_backend == 'dlib': - import dlib #this is not a must library within deepface. that's why, I didn't put this import to a global level. version: 19.20.0 - - detector = dlib.get_frontal_face_detector() - - detections = detector(img, 1) - - if len(detections) > 0: - - for idx, d in enumerate(detections): - left = d.left(); right = d.right() - top = d.top(); bottom = d.bottom() - - detected_face = img[top:bottom, left:right] - - return detected_face - - else: #if no face detected - - if enforce_detection != True: - return img - - else: - raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.") - - elif detector_backend == 'mtcnn': - - mtcnn_detector = MTCNN() - - detections = mtcnn_detector.detect_faces(img) - - if len(detections) > 0: - detection = detections[0] - x, y, w, h = detection["box"] - detected_face = img[int(y):int(y+h), int(x):int(x+w)] - return detected_face - - else: #if no face detected - if enforce_detection != True: - return img - - else: - raise ValueError("Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.") - - else: - detectors = ['opencv', 'ssd', 'dlib', 'mtcnn'] - raise ValueError("Valid backends are ", detectors," but you passed ", detector_backend) - - return 0 def alignment_procedure(img, left_eye, right_eye): - - #this function aligns given face in img based on left and right eye coordinates - - left_eye_x, left_eye_y = left_eye - right_eye_x, right_eye_y = right_eye - - #----------------------- - #find rotation direction - - if left_eye_y > right_eye_y: - point_3rd = (right_eye_x, left_eye_y) - direction = -1 #rotate same direction to clock - else: - point_3rd = (left_eye_x, right_eye_y) - direction = 1 #rotate inverse direction of clock - - #----------------------- - #find length of triangle edges - - a = distance.findEuclideanDistance(np.array(left_eye), np.array(point_3rd)) - b = distance.findEuclideanDistance(np.array(right_eye), np.array(point_3rd)) - c = distance.findEuclideanDistance(np.array(right_eye), np.array(left_eye)) - - #----------------------- - - #apply cosine rule - - if b != 0 and c != 0: #this multiplication causes division by zero in cos_a calculation - - cos_a = (b*b + c*c - a*a)/(2*b*c) - angle = np.arccos(cos_a) #angle in radian - angle = (angle * 180) / math.pi #radian to degree - - #----------------------- - #rotate base image - - if direction == -1: - angle = 90 - angle - - img = Image.fromarray(img) - img = np.array(img.rotate(direction * angle)) - - #----------------------- - - return img #return img anyway - -def align_face(img, detector_backend = 'opencv'): - - home = str(Path.home()) - - if (detector_backend == 'opencv') or (detector_backend == 'ssd'): - - opencv_path = get_opencv_path() - eye_detector_path = opencv_path+"haarcascade_eye.xml" - eye_detector = cv2.CascadeClassifier(eye_detector_path) - - detected_face_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #eye detector expects gray scale image - - eyes = eye_detector.detectMultiScale(detected_face_gray) - - if len(eyes) >= 2: - - #find the largest 2 eye - - base_eyes = eyes[:, 2] - - items = [] - for i in range(0, len(base_eyes)): - item = (base_eyes[i], i) - items.append(item) - - df = pd.DataFrame(items, columns = ["length", "idx"]).sort_values(by=['length'], ascending=False) - - eyes = eyes[df.idx.values[0:2]] #eyes variable stores the largest 2 eye - - #----------------------- - #decide left and right eye - - eye_1 = eyes[0]; eye_2 = eyes[1] - - if eye_1[0] < eye_2[0]: - left_eye = eye_1; right_eye = eye_2 - else: - left_eye = eye_2; right_eye = eye_1 - - #----------------------- - #find center of eyes - - left_eye = (int(left_eye[0] + (left_eye[2] / 2)), int(left_eye[1] + (left_eye[3] / 2))) - right_eye = (int(right_eye[0] + (right_eye[2]/2)), int(right_eye[1] + (right_eye[3]/2))) - - img = alignment_procedure(img, left_eye, right_eye) - - return img #return img anyway - - elif detector_backend == 'dlib': - - #check required file exists in the home/.deepface/weights folder - - if os.path.isfile(home+'/.deepface/weights/shape_predictor_5_face_landmarks.dat') != True: - - print("shape_predictor_5_face_landmarks.dat.bz2 is going to be downloaded") - - url = "http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2" - output = home+'/.deepface/weights/'+url.split("/")[-1] - - gdown.download(url, output, quiet=False) - - zipfile = bz2.BZ2File(output) - data = zipfile.read() - newfilepath = output[:-4] #discard .bz2 extension - open(newfilepath, 'wb').write(data) - - #------------------------------ - - import dlib #this is not a must dependency in deepface - - detector = dlib.get_frontal_face_detector() - sp = dlib.shape_predictor(home+"/.deepface/weights/shape_predictor_5_face_landmarks.dat") - - detections = detector(img, 1) - - if len(detections) > 0: - detected_face = detections[0] - img_shape = sp(img, detected_face) - img = dlib.get_face_chip(img, img_shape, size = img.shape[0]) - - return img #return img anyway - - elif detector_backend == 'mtcnn': - - mtcnn_detector = MTCNN() - detections = mtcnn_detector.detect_faces(img) - - if len(detections) > 0: - detection = detections[0] - - keypoints = detection["keypoints"] - left_eye = keypoints["left_eye"] - right_eye = keypoints["right_eye"] - - img = alignment_procedure(img, left_eye, right_eye) - - return img #return img anyway - -def preprocess_face(img, target_size=(224, 224), grayscale = False, enforce_detection = True, detector_backend = 'opencv'): - - #img might be path, base64 or numpy array. Convert it to numpy whatever it is. - img = load_image(img) - base_img = img.copy() - - img = detect_face(img = img, detector_backend = detector_backend, grayscale = grayscale, enforce_detection = enforce_detection) - - #-------------------------- - - if img.shape[0] > 0 and img.shape[1] > 0: - img = align_face(img = img, detector_backend = detector_backend) - else: - - if enforce_detection == True: - raise ValueError("Detected face shape is ", img.shape,". Consider to set enforce_detection argument to False.") - else: #restore base image - img = base_img.copy() - - #-------------------------- - - #post-processing - if grayscale == True: - img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) - - img = cv2.resize(img, target_size) - img_pixels = image.img_to_array(img) - img_pixels = np.expand_dims(img_pixels, axis = 0) - img_pixels /= 255 #normalize input in [0, 1] - - return img_pixels - + # this function aligns given face in img based on left and right eye coordinates + + left_eye_x, left_eye_y = left_eye + right_eye_x, right_eye_y = right_eye + + # ----------------------- + # find rotation direction + + if left_eye_y > right_eye_y: + point_3rd = (right_eye_x, left_eye_y) + direction = -1 # rotate same direction to clock + else: + point_3rd = (left_eye_x, right_eye_y) + direction = 1 # rotate inverse direction of clock + + # ----------------------- + # find length of triangle edges + + a = distance.findEuclideanDistance(np.array(left_eye), np.array(point_3rd)) + b = distance.findEuclideanDistance(np.array(right_eye), np.array(point_3rd)) + c = distance.findEuclideanDistance(np.array(right_eye), np.array(left_eye)) + + # ----------------------- + + # apply cosine rule + + if b != 0 and c != 0: # this multiplication causes division by zero in cos_a calculation + + cos_a = (b * b + c * c - a * a) / (2 * b * c) + angle = np.arccos(cos_a) # angle in radian + angle = (angle * 180) / math.pi # radian to degree + + # ----------------------- + # rotate base image + + if direction == -1: + angle = 90 - angle + + img = Image.fromarray(img) + img = np.array(img.rotate(direction * angle)) + + # ----------------------- + + return img # return img anyway + + +def align_face(img, detector_backend='opencv'): + home = str(Path.home()) + + if (detector_backend == 'opencv') or (detector_backend == 'ssd'): + + opencv_path = get_opencv_path() + eye_detector_path = opencv_path + "haarcascade_eye.xml" + eye_detector = cv2.CascadeClassifier(eye_detector_path) + + detected_face_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # eye detector expects gray scale image + + eyes = eye_detector.detectMultiScale(detected_face_gray) + + if len(eyes) >= 2: + + # find the largest 2 eye + + base_eyes = eyes[:, 2] + + items = [] + for i in range(0, len(base_eyes)): + item = (base_eyes[i], i) + items.append(item) + + df = pd.DataFrame(items, columns=["length", "idx"]).sort_values(by=['length'], ascending=False) + + eyes = eyes[df.idx.values[0:2]] # eyes variable stores the largest 2 eye + + # ----------------------- + # decide left and right eye + + eye_1 = eyes[0]; + eye_2 = eyes[1] + + if eye_1[0] < eye_2[0]: + left_eye = eye_1; + right_eye = eye_2 + else: + left_eye = eye_2; + right_eye = eye_1 + + # ----------------------- + # find center of eyes + + left_eye = (int(left_eye[0] + (left_eye[2] / 2)), int(left_eye[1] + (left_eye[3] / 2))) + right_eye = (int(right_eye[0] + (right_eye[2] / 2)), int(right_eye[1] + (right_eye[3] / 2))) + + img = alignment_procedure(img, left_eye, right_eye) + + return img # return img anyway + + elif detector_backend == 'dlib': + + # check required file exists in the home/.deepface/weights folder + + if os.path.isfile(home + '/.deepface/weights/shape_predictor_5_face_landmarks.dat') != True: + print("shape_predictor_5_face_landmarks.dat.bz2 is going to be downloaded") + + url = "http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2" + output = home + '/.deepface/weights/' + url.split("/")[-1] + + gdown.download(url, output, quiet=False) + + zipfile = bz2.BZ2File(output) + data = zipfile.read() + newfilepath = output[:-4] # discard .bz2 extension + open(newfilepath, 'wb').write(data) + + # ------------------------------ + + import dlib # this is not a must dependency in deepface + + detector = dlib.get_frontal_face_detector() + sp = dlib.shape_predictor(home + "/.deepface/weights/shape_predictor_5_face_landmarks.dat") + + detections = detector(img, 1) + + if len(detections) > 0: + detected_face = detections[0] + img_shape = sp(img, detected_face) + img = dlib.get_face_chip(img, img_shape, size=img.shape[0]) + + return img # return img anyway + + elif detector_backend == 'mtcnn': + + mtcnn_detector = MTCNN() + detections = mtcnn_detector.detect_faces(img) + + if len(detections) > 0: + detection = detections[0] + + keypoints = detection["keypoints"] + left_eye = keypoints["left_eye"] + right_eye = keypoints["right_eye"] + + img = alignment_procedure(img, left_eye, right_eye) + + return img # return img anyway + + +def preprocess_face(img, target_size=(224, 224), grayscale=False, enforce_detection=True, detector_backend='opencv'): + # img might be path, base64 or numpy array. Convert it to numpy whatever it is. + img = load_image(img) + base_img = img.copy() + + imgs = detect_face(img=img, detector_backend=detector_backend, grayscale=grayscale, + enforce_detection=enforce_detection) + orig = imgs.copy() + + # -------------------------- + + for i in range(len(imgs)): + + img = imgs[i] + + if img.shape[0] > 0 and img.shape[1] > 0: + imgs[i] = align_face(img=img, detector_backend=detector_backend) + else: + + if enforce_detection == True: + raise ValueError("Detected face shape is ", img.shape, + ". Consider to set enforce_detection argument to False.") + else: # restore base image + imgs[i] = base_img.copy() + + # -------------------------- + + # post-processing + + pixels = [] + + for img in imgs: + + if grayscale == True: + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + + img = cv2.resize(img, target_size) + img_pixels = image.img_to_array(img) + img_pixels = np.expand_dims(img_pixels, axis=0) + img_pixels /= 255 # normalize input in [0, 1] + + pixels.append(img_pixels) + + return {'processed': pixels, 'original': orig} + + def allocateMemory(): - - #find allocated memories - gpu_indexes = [] - memory_usage_percentages = []; available_memories = []; total_memories = []; utilizations = [] - power_usages = []; power_capacities = [] - - try: - result = subprocess.check_output(['nvidia-smi']) + # find allocated memories + gpu_indexes = [] + memory_usage_percentages = []; + available_memories = []; + total_memories = []; + utilizations = [] + power_usages = []; + power_capacities = [] - dashboard = result.decode("utf-8").split("=|") + try: + result = subprocess.check_output(['nvidia-smi']) - dashboard = dashboard[1].split("\n") - - gpu_idx = 0 - for line in dashboard: - if ("MiB" in line): - power_info = line.split("|")[1] - power_capacity = int(power_info.split("/")[-1].replace("W", "")) - power_usage = int((power_info.split("/")[-2]).strip().split(" ")[-1].replace("W", "")) - - power_usages.append(power_usage) - power_capacities.append(power_capacity) - - #---------------------------- - - memory_info = line.split("|")[2].replace("MiB","").split("/") - utilization_info = int(line.split("|")[3].split("%")[0]) - - allocated = int(memory_info[0]) - total_memory = int(memory_info[1]) - available_memory = total_memory - allocated - - total_memories.append(total_memory) - available_memories.append(available_memory) - memory_usage_percentages.append(round(100*int(allocated)/int(total_memory), 4)) - utilizations.append(utilization_info) - gpu_indexes.append(gpu_idx) - - gpu_idx = gpu_idx + 1 - - gpu_count = gpu_idx * 1 - - except Exception as err: - gpu_count = 0 - #print(str(err)) - - #------------------------------ - - df = pd.DataFrame(gpu_indexes, columns = ["gpu_index"]) - df["total_memories_in_mb"] = total_memories - df["available_memories_in_mb"] = available_memories - df["memory_usage_percentage"] = memory_usage_percentages - df["utilizations"] = utilizations - df["power_usages_in_watts"] = power_usages - df["power_capacities_in_watts"] = power_capacities - - df = df.sort_values(by = ["available_memories_in_mb"], ascending = False).reset_index(drop = True) - - #------------------------------ - - required_memory = 10000 #All deepface models require 9016 MiB - - if df.shape[0] > 0: #has gpu - if df.iloc[0].available_memories_in_mb > required_memory: - my_gpu = str(int(df.iloc[0].gpu_index)) - os.environ["CUDA_VISIBLE_DEVICES"] = my_gpu - - #------------------------------ - #tf allocates all memory by default - #this block avoids greedy approach - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - session = tf.Session(config=config) - keras.backend.set_session(session) - - print("DeepFace will run on GPU (gpu_", my_gpu,")") - else: - #this case has gpu but no enough memory to allocate - os.environ["CUDA_VISIBLE_DEVICES"] = "" #run it on cpu - print("Even though the system has GPUs, there is no enough space in memory to allocate.") - print("DeepFace will run on CPU") - else: - print("DeepFace will run on CPU") + dashboard = result.decode("utf-8").split("=|") + + dashboard = dashboard[1].split("\n") + + gpu_idx = 0 + for line in dashboard: + if ("MiB" in line): + power_info = line.split("|")[1] + power_capacity = int(power_info.split("/")[-1].replace("W", "")) + power_usage = int((power_info.split("/")[-2]).strip().split(" ")[-1].replace("W", "")) + + power_usages.append(power_usage) + power_capacities.append(power_capacity) + + # ---------------------------- + + memory_info = line.split("|")[2].replace("MiB", "").split("/") + utilization_info = int(line.split("|")[3].split("%")[0]) + + allocated = int(memory_info[0]) + total_memory = int(memory_info[1]) + available_memory = total_memory - allocated + + total_memories.append(total_memory) + available_memories.append(available_memory) + memory_usage_percentages.append(round(100 * int(allocated) / int(total_memory), 4)) + utilizations.append(utilization_info) + gpu_indexes.append(gpu_idx) + + gpu_idx = gpu_idx + 1 + + gpu_count = gpu_idx * 1 + + except Exception as err: + gpu_count = 0 + # print(str(err)) + + # ------------------------------ + + df = pd.DataFrame(gpu_indexes, columns=["gpu_index"]) + df["total_memories_in_mb"] = total_memories + df["available_memories_in_mb"] = available_memories + df["memory_usage_percentage"] = memory_usage_percentages + df["utilizations"] = utilizations + df["power_usages_in_watts"] = power_usages + df["power_capacities_in_watts"] = power_capacities + + df = df.sort_values(by=["available_memories_in_mb"], ascending=False).reset_index(drop=True) + + # ------------------------------ + + required_memory = 10000 # All deepface models require 9016 MiB + + if df.shape[0] > 0: # has gpu + if df.iloc[0].available_memories_in_mb > required_memory: + my_gpu = str(int(df.iloc[0].gpu_index)) + os.environ["CUDA_VISIBLE_DEVICES"] = my_gpu + + # ------------------------------ + # tf allocates all memory by default + # this block avoids greedy approach + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + session = tf.Session(config=config) + keras.backend.set_session(session) + + print("DeepFace will run on GPU (gpu_", my_gpu, ")") + else: + # this case has gpu but no enough memory to allocate + os.environ["CUDA_VISIBLE_DEVICES"] = "" # run it on cpu + print("Even though the system has GPUs, there is no enough space in memory to allocate.") + print("DeepFace will run on CPU") + else: + print("DeepFace will run on CPU") diff --git a/deepface/commons/functionsLite.py b/deepface/commons/functionsLite.py new file mode 100644 index 0000000..5cf42fb --- /dev/null +++ b/deepface/commons/functionsLite.py @@ -0,0 +1,769 @@ +import os +import numpy as np +import pandas as pd +from keras.preprocessing.image import load_img, save_img, img_to_array +from keras.applications.imagenet_utils import preprocess_input +from keras.preprocessing import image +import cv2 +from pathlib import Path +import gdown +import hashlib +import math +from PIL import Image +import copy +import base64 +import multiprocessing +import subprocess +import tensorflow as tf +import keras +import bz2 +from deepface.commons import distance +from mtcnn import MTCNN # 0.1.0 + +# draw bounding boxes +from PIL import Image, ImageFont, ImageDraw +import colorsys +import numpy as np + + +def loadBase64Img(uri): + encoded_data = uri.split(',')[1] + nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8) + img = cv2.imdecode(nparr, cv2.IMREAD_COLOR) + return img + + +def initializeFolder(): + home = str(Path.home()) + + if not os.path.exists(home + "/.deepface"): + os.mkdir(home + "/.deepface") + print("Directory ", home, "/.deepface created") + + if not os.path.exists(home + "/.deepface/weights"): + os.mkdir(home + "/.deepface/weights") + print("Directory ", home, "/.deepface/weights created") + + +def findThreshold(model_name, distance_metric): + threshold = 0.40 + + if model_name == 'VGG-Face': + if distance_metric == 'cosine': + threshold = 0.40 + elif distance_metric == 'euclidean': + threshold = 0.55 + elif distance_metric == 'euclidean_l2': + threshold = 0.75 + + elif model_name == 'OpenFace': + if distance_metric == 'cosine': + threshold = 0.10 + elif distance_metric == 'euclidean': + threshold = 0.55 + elif distance_metric == 'euclidean_l2': + threshold = 0.55 + + elif model_name == 'Facenet': + if distance_metric == 'cosine': + threshold = 0.40 + elif distance_metric == 'euclidean': + threshold = 10 + elif distance_metric == 'euclidean_l2': + threshold = 0.80 + + elif model_name == 'DeepFace': + if distance_metric == 'cosine': + threshold = 0.23 + elif distance_metric == 'euclidean': + threshold = 64 + elif distance_metric == 'euclidean_l2': + threshold = 0.64 + + elif model_name == 'DeepID': + if distance_metric == 'cosine': + threshold = 0.015 + elif distance_metric == 'euclidean': + threshold = 45 + elif distance_metric == 'euclidean_l2': + threshold = 0.17 + + elif model_name == 'Dlib': + if distance_metric == 'cosine': + threshold = 0.07 + elif distance_metric == 'euclidean': + threshold = 0.60 + elif distance_metric == 'euclidean_l2': + threshold = 0.60 + + return threshold + + +def get_opencv_path(): + opencv_home = cv2.__file__ + folders = opencv_home.split(os.path.sep)[0:-1] + + path = folders[0] + for folder in folders[1:]: + path = path + "/" + folder + + return path + "/data/" + + +def load_image(img): + exact_image = False + if type(img).__module__ == np.__name__: + exact_image = True + + base64_img = False + if len(img) > 11 and img[0:11] == "data:image/": + base64_img = True + + # --------------------------- + + if base64_img == True: + img = loadBase64Img(img) + + elif exact_image != True: # image path passed as input + if os.path.isfile(img) != True: + raise ValueError("Confirm that ", img, " exists") + + img = cv2.imread(img) + + return img + + +def detect_face(img, detector_backend='opencv', enforce_detection=True): + home = str(Path.home()) + + # drawing settings + font = ImageFont.truetype(font='font/FiraMono-Medium.otf', + size=np.floor(3e-2 * img.size[1] + 0.5).astype('int32')) + thickness = (img.size[0] + img.size[1]) // 300 + hsv_tuples = [(x / 50, 1., 1.) + for x in range(50)] + colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)) + colors = list( + map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors)) + np.random.shuffle(colors) # Shuffle colors to decorrelate adjacent classes. + + + # keep PIL image and cv2 image + pil_img = img + cv2_img = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR) + + + if detector_backend == 'opencv': + + # get opencv configuration up first + opencv_path = get_opencv_path() + face_detector_path = opencv_path + "haarcascade_frontalface_default.xml" + + if os.path.isfile(face_detector_path) != True: + raise ValueError("Confirm that opencv is installed on your environment! Expected path ", face_detector_path, + " violated.") + + face_detector = cv2.CascadeClassifier(face_detector_path) + + # -------------------------- + + faces = [] + + try: + faces = face_detector.detectMultiScale(cv2_img, 1.3, 5) + except: + pass + + if len(faces) > 0: + detected_faces = [] + + showid = True if len(faces) > 1 else False + + for i, face in enumerate(faces): + x, y, w, h = face + detected_face = cv2_img[int(y):int(y + h), int(x):int(x + w)] + detected_faces.append(detected_face) + + # bounding box corners + top, left, bottom, right = [int(y), int(x), int(y+h), int(x+w)] + top = max(0, np.floor(top + 0.5).astype('int32')) + left = max(0, np.floor(left + 0.5).astype('int32')) + bottom = min(pil_img.size[1], np.floor(bottom + 0.5).astype('int32')) + right = min(pil_img.size[0], np.floor(right + 0.5).astype('int32')) + + # label + label = 'ID: {}'.format(i) + draw = ImageDraw.Draw(pil_img) + label_size = draw.textsize(label, font=font) + + if top - label_size[1] >= 0: + text_origin = np.array([left, top - label_size[1]]) + else: + text_origin = np.array([left, top + 1]) + + # My kingdom for a good redistributable image drawing library. + for i in range(thickness): + draw.rectangle( + [left + i, top + i, right - i, bottom - i], + outline=colors[i]) + + if showid: + draw.rectangle( + [tuple(text_origin), tuple(text_origin + label_size)], + fill=colors[i] + ) + draw.text(text_origin, label, fill=(0, 0, 0), font=font) + del draw + + return pil_img, detected_faces + + else: # if no face detected + + if enforce_detection != True: + return pil_img, cv2_img + + else: + raise ValueError( + "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.") + + elif detector_backend == 'ssd': + + # --------------------------- + # check required ssd model exists in the home/.deepface/weights folder + + # model structure + if os.path.isfile(home + '/.deepface/weights/deploy.prototxt') != True: + print("deploy.prototxt will be downloaded...") + + url = "https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt" + + output = home + '/.deepface/weights/deploy.prototxt' + + gdown.download(url, output, quiet=False) + + # pre-trained weights + if os.path.isfile(home + '/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel') != True: + print("res10_300x300_ssd_iter_140000.caffemodel will be downloaded...") + + url = "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel" + + output = home + '/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel' + + gdown.download(url, output, quiet=False) + + # --------------------------- + + ssd_detector = cv2.dnn.readNetFromCaffe( + home + "/.deepface/weights/deploy.prototxt", + home + "/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel" + ) + + ssd_labels = ["img_id", "is_face", "confidence", "left", "top", "right", "bottom"] + + target_size = (300, 300) + + base_img = cv2_img.copy() # we will restore base_img to img later + + original_size = cv2_img.shape + + cv2_img = cv2.resize(cv2_img, target_size) + + aspect_ratio_x = (original_size[1] / target_size[1]) + aspect_ratio_y = (original_size[0] / target_size[0]) + + imageBlob = cv2.dnn.blobFromImage(image=cv2_img) + + ssd_detector.setInput(imageBlob) + detections = ssd_detector.forward() + + detections_df = pd.DataFrame(detections[0][0], columns=ssd_labels) + + detections_df = detections_df[detections_df['is_face'] == 1] # 0: background, 1: face + detections_df = detections_df[detections_df['confidence'] >= 0.90] + + detections_df['left'] = (detections_df['left'] * 300).astype(int) + detections_df['bottom'] = (detections_df['bottom'] * 300).astype(int) + detections_df['right'] = (detections_df['right'] * 300).astype(int) + detections_df['top'] = (detections_df['top'] * 300).astype(int) + + if detections_df.shape[0] > 0: + + showid = True if detections_df.shape[0] > 1 else False + + # TODO: sort detections_df + + detected_faces = [] + + for i in range(0, len(detections_df)): + instance = detections_df.iloc[i] + left = instance["left"] + right = instance["right"] + bottom = instance["bottom"] + top = instance["top"] + + detected_face = base_img[int(top * aspect_ratio_y):int(bottom * aspect_ratio_y), int(left * aspect_ratio_x):int(right * aspect_ratio_x)] + detected_faces.append(detected_face) + + # bounding box corners + top, left, bottom, right = [int(top * aspect_ratio_y), int(left * aspect_ratio_x), int(bottom * aspect_ratio_y), int(right * aspect_ratio_x)] + top = max(0, np.floor(top + 0.5).astype('int32')) + left = max(0, np.floor(left + 0.5).astype('int32')) + bottom = min(pil_img.size[1], np.floor(bottom + 0.5).astype('int32')) + right = min(pil_img.size[0], np.floor(right + 0.5).astype('int32')) + + # label + label = 'ID: {}'.format(i) + draw = ImageDraw.Draw(pil_img) + label_size = draw.textsize(label, font=font) + + if top - label_size[1] >= 0: + text_origin = np.array([left, top - label_size[1]]) + else: + text_origin = np.array([left, top + 1]) + + # My kingdom for a good redistributable image drawing library. + for i in range(thickness): + draw.rectangle( + [left + i, top + i, right - i, bottom - i], + outline=colors[i]) + + if showid: + draw.rectangle( + [tuple(text_origin), tuple(text_origin + label_size)], + fill=colors[i] + ) + draw.text(text_origin, label, fill=(0, 0, 0), font=font) + del draw + + + return pil_img, detected_faces + + else: # if no face detected + + if enforce_detection != True: + img = base_img.copy() + return pil_img, img + + else: + raise ValueError( + "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.") + + elif detector_backend == 'dlib': + import dlib # this is not a must library within deepface. that's why, I didn't put this import to a global level. version: 19.20.0 + + detector = dlib.get_frontal_face_detector() + + detections = detector(cv2_img, 1) + + if len(detections) > 0: + + showid = True if len(detections) > 1 else False + + detected_faces = [] + for i, d in enumerate(detections): + + left = d.left() + right = d.right() + top = d.top() + bottom = d.bottom() + + detected_face = cv2_img[top:bottom, left:right] + detected_faces.append(detected_face) + + + # bounding box corners + top = max(0, np.floor(top + 0.5).astype('int32')) + left = max(0, np.floor(left + 0.5).astype('int32')) + bottom = min(pil_img.size[1], np.floor(bottom + 0.5).astype('int32')) + right = min(pil_img.size[0], np.floor(right + 0.5).astype('int32')) + + # label + label = 'ID: {}'.format(i) + draw = ImageDraw.Draw(pil_img) + label_size = draw.textsize(label, font=font) + + if top - label_size[1] >= 0: + text_origin = np.array([left, top - label_size[1]]) + else: + text_origin = np.array([left, top + 1]) + + # My kingdom for a good redistributable image drawing library. + for i in range(thickness): + draw.rectangle( + [left + i, top + i, right - i, bottom - i], + outline=colors[i]) + + if showid: + draw.rectangle( + [tuple(text_origin), tuple(text_origin + label_size)], + fill=colors[i] + ) + draw.text(text_origin, label, fill=(0, 0, 0), font=font) + del draw + + + return pil_img, detected_faces + + + else: # if no face detected + + if enforce_detection != True: + return pil_img, img + + else: + raise ValueError( + "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.") + + elif detector_backend == 'mtcnn': + + mtcnn_detector = MTCNN() + + detections = mtcnn_detector.detect_faces(cv2_img) + + if len(detections) > 0: + + showid = True if len(detections) > 1 else False + + detected_faces = [] + for i, detection in enumerate(detections): + x, y, w, h = detection["box"] + detected_face = cv2_img[int(y):int(y + h), int(x):int(x + w)] + detected_faces.append(detected_face) + + # bounding box corners + top, left, bottom, right = [int(y), int(x), int(y+h), int(x+w)] + top = max(0, np.floor(top + 0.5).astype('int32')) + left = max(0, np.floor(left + 0.5).astype('int32')) + bottom = min(pil_img.size[1], np.floor(bottom + 0.5).astype('int32')) + right = min(pil_img.size[0], np.floor(right + 0.5).astype('int32')) + + # label + print(i) + label = 'ID: {}'.format(i) + draw = ImageDraw.Draw(pil_img) + label_size = draw.textsize(label, font=font) + + if top - label_size[1] >= 0: + text_origin = np.array([left, top - label_size[1]]) + else: + text_origin = np.array([left, top + 1]) + + # My kingdom for a good redistributable image drawing library. + for i in range(thickness): + draw.rectangle( + [left + i, top + i, right - i, bottom - i], + outline=colors[i]) + + if showid: + draw.rectangle( + [tuple(text_origin), tuple(text_origin + label_size)], + fill=colors[i] + ) + draw.text(text_origin, label, fill=(0, 0, 0), font=font) + del draw + + return pil_img, detected_faces + + else: # if no face detected + if enforce_detection != True: + return pil_img, img + + else: + raise ValueError( + "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False.") + + else: + detectors = ['opencv', 'ssd', 'dlib', 'mtcnn'] + raise ValueError("Valid backends are ", detectors, " but you passed ", detector_backend) + + return 0 + + +def alignment_procedure(img, left_eye, right_eye): + # this function aligns given face in img based on left and right eye coordinates + + left_eye_x, left_eye_y = left_eye + right_eye_x, right_eye_y = right_eye + + # ----------------------- + # find rotation direction + + if left_eye_y > right_eye_y: + point_3rd = (right_eye_x, left_eye_y) + direction = -1 # rotate same direction to clock + else: + point_3rd = (left_eye_x, right_eye_y) + direction = 1 # rotate inverse direction of clock + + # ----------------------- + # find length of triangle edges + + a = distance.findEuclideanDistance(np.array(left_eye), np.array(point_3rd)) + b = distance.findEuclideanDistance(np.array(right_eye), np.array(point_3rd)) + c = distance.findEuclideanDistance(np.array(right_eye), np.array(left_eye)) + + # ----------------------- + + # apply cosine rule + + if b != 0 and c != 0: # this multiplication causes division by zero in cos_a calculation + + cos_a = (b * b + c * c - a * a) / (2 * b * c) + angle = np.arccos(cos_a) # angle in radian + angle = (angle * 180) / math.pi # radian to degree + + # ----------------------- + # rotate base image + + if direction == -1: + angle = 90 - angle + + img = Image.fromarray(img) + img = np.array(img.rotate(direction * angle)) + + # ----------------------- + + return img # return img anyway + + +def align_face(img, detector_backend='opencv'): + home = str(Path.home()) + + if (detector_backend == 'opencv') or (detector_backend == 'ssd'): + + opencv_path = get_opencv_path() + eye_detector_path = opencv_path + "haarcascade_eye.xml" + eye_detector = cv2.CascadeClassifier(eye_detector_path) + + detected_face_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # eye detector expects gray scale image + + eyes = eye_detector.detectMultiScale(detected_face_gray) + + if len(eyes) >= 2: + + # find the largest 2 eye + + base_eyes = eyes[:, 2] + + items = [] + for i in range(0, len(base_eyes)): + item = (base_eyes[i], i) + items.append(item) + + df = pd.DataFrame(items, columns=["length", "idx"]).sort_values(by=['length'], ascending=False) + + eyes = eyes[df.idx.values[0:2]] # eyes variable stores the largest 2 eye + + # ----------------------- + # decide left and right eye + + eye_1 = eyes[0] + eye_2 = eyes[1] + + if eye_1[0] < eye_2[0]: + left_eye = eye_1 + right_eye = eye_2 + else: + left_eye = eye_2 + right_eye = eye_1 + + # ----------------------- + # find center of eyes + + left_eye = (int(left_eye[0] + (left_eye[2] / 2)), int(left_eye[1] + (left_eye[3] / 2))) + right_eye = (int(right_eye[0] + (right_eye[2] / 2)), int(right_eye[1] + (right_eye[3] / 2))) + + img = alignment_procedure(img, left_eye, right_eye) + + return img # return img anyway + + elif detector_backend == 'dlib': + + # check required file exists in the home/.deepface/weights folder + + if os.path.isfile(home + '/.deepface/weights/shape_predictor_5_face_landmarks.dat') != True: + print("shape_predictor_5_face_landmarks.dat.bz2 is going to be downloaded") + + url = "http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2" + output = home + '/.deepface/weights/' + url.split("/")[-1] + + gdown.download(url, output, quiet=False) + + zipfile = bz2.BZ2File(output) + data = zipfile.read() + newfilepath = output[:-4] # discard .bz2 extension + open(newfilepath, 'wb').write(data) + + # ------------------------------ + + import dlib # this is not a must dependency in deepface + + detector = dlib.get_frontal_face_detector() + sp = dlib.shape_predictor(home + "/.deepface/weights/shape_predictor_5_face_landmarks.dat") + + detections = detector(img, 1) + + if len(detections) > 0: + detected_face = detections[0] + img_shape = sp(img, detected_face) + img = dlib.get_face_chip(img, img_shape, size=img.shape[0]) + + return img # return img anyway + + elif detector_backend == 'mtcnn': + + mtcnn_detector = MTCNN() + detections = mtcnn_detector.detect_faces(img) + + if len(detections) > 0: + detection = detections[0] + + keypoints = detection["keypoints"] + left_eye = keypoints["left_eye"] + right_eye = keypoints["right_eye"] + + img = alignment_procedure(img, left_eye, right_eye) + + return img # return img anyway + + +def preprocess_face(base_img, enforce_detection=True, detector_backend='opencv'): + + cv2_img = cv2.cvtColor(np.asarray(base_img),cv2.COLOR_RGB2BGR) + bbox_img, faces = detect_face(img=base_img, detector_backend=detector_backend, + enforce_detection=enforce_detection) + orig_faces = faces.copy() + + # -------------------------- + + for i in range(len(faces)): + + face = faces[i] + + if face.shape[0] > 0 and face.shape[1] > 0: + faces[i] = align_face(img=face, detector_backend=detector_backend) + else: + + if enforce_detection == True: + raise ValueError("Detected face shape is ", face.shape, + ". Consider to set enforce_detection argument to False.") + else: # restore base image + faces[i] = cv2_img + + # -------------------------- + + # post-processing + pixels = [] + pixels_gray = [] + + for img in faces: + # RBG + target_size = (224, 224) + img_rgb = cv2.resize(img, target_size) + img_pixels = image.img_to_array(img_rgb) + img_pixels = np.expand_dims(img_pixels, axis=0) + img_pixels /= 255 # normalize input in [0, 1] + pixels.append(img_pixels) + + # gray scale + target_size = (48, 48) + img_gray = cv2.resize(img, target_size) + img_gray = cv2.cvtColor(img_gray, cv2.COLOR_BGR2GRAY) + img_pixels_gray = image.img_to_array(img_gray) + print(img_pixels_gray.shape) + img_pixels_gray = np.expand_dims(img_pixels_gray, axis=0) + img_pixels_gray /= 255 # normalize input in [0, 1] + pixels_gray.append(img_pixels_gray) + + return {'processed': pixels, 'original': orig_faces, + 'gray': pixels_gray, 'bbox': bbox_img} + + +def allocateMemory(): + # find allocated memories + gpu_indexes = [] + memory_usage_percentages = [] + available_memories = [] + total_memories = [] + utilizations = [] + power_usages = [] + power_capacities = [] + + try: + result = subprocess.check_output(['nvidia-smi']) + + dashboard = result.decode("utf-8").split("=|") + + dashboard = dashboard[1].split("\n") + + gpu_idx = 0 + for line in dashboard: + if ("MiB" in line): + power_info = line.split("|")[1] + power_capacity = int(power_info.split("/")[-1].replace("W", "")) + power_usage = int((power_info.split("/")[-2]).strip().split(" ")[-1].replace("W", "")) + + power_usages.append(power_usage) + power_capacities.append(power_capacity) + + # ---------------------------- + + memory_info = line.split("|")[2].replace("MiB", "").split("/") + utilization_info = int(line.split("|")[3].split("%")[0]) + + allocated = int(memory_info[0]) + total_memory = int(memory_info[1]) + available_memory = total_memory - allocated + + total_memories.append(total_memory) + available_memories.append(available_memory) + memory_usage_percentages.append(round(100 * int(allocated) / int(total_memory), 4)) + utilizations.append(utilization_info) + gpu_indexes.append(gpu_idx) + + gpu_idx = gpu_idx + 1 + + gpu_count = gpu_idx * 1 + + except Exception as err: + gpu_count = 0 + # print(str(err)) + + # ------------------------------ + + df = pd.DataFrame(gpu_indexes, columns=["gpu_index"]) + df["total_memories_in_mb"] = total_memories + df["available_memories_in_mb"] = available_memories + df["memory_usage_percentage"] = memory_usage_percentages + df["utilizations"] = utilizations + df["power_usages_in_watts"] = power_usages + df["power_capacities_in_watts"] = power_capacities + + df = df.sort_values(by=["available_memories_in_mb"], ascending=False).reset_index(drop=True) + + # ------------------------------ + + required_memory = 10000 # All deepface models require 9016 MiB + + if df.shape[0] > 0: # has gpu + if df.iloc[0].available_memories_in_mb > required_memory: + my_gpu = str(int(df.iloc[0].gpu_index)) + os.environ["CUDA_VISIBLE_DEVICES"] = my_gpu + + # ------------------------------ + # tf allocates all memory by default + # this block avoids greedy approach + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + session = tf.Session(config=config) + keras.backend.set_session(session) + + print("DeepFace will run on GPU (gpu_", my_gpu, ")") + else: + # this case has gpu but no enough memory to allocate + os.environ["CUDA_VISIBLE_DEVICES"] = "" # run it on cpu + print("Even though the system has GPUs, there is no enough space in memory to allocate.") + print("DeepFace will run on CPU") + else: + print("DeepFace will run on CPU") diff --git a/deepface_lite.ipynb b/deepface_lite.ipynb new file mode 100644 index 0000000..56be073 --- /dev/null +++ b/deepface_lite.ipynb @@ -0,0 +1,143 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from deepface.DeepFaceLite import DeepFaceLite\n", + "import os\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "from PIL import Image" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "output_type": "error", + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'test_imgs/'", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m<ipython-input-2-cd20a3f3900f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mimgs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mimg_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'test_imgs/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'jpg'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'jpeg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mimg_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'test_imgs/'" + ] + } + ], + "source": [ + "# read images\n", + "imgs = []\n", + "img_dir = 'test_imgs/'\n", + "for img in os.listdir(img_dir):\n", + " if img.endswith('jpg') or img.endswith('jpeg'):\n", + " img_path = os.path.join(img_dir, img)\n", + " imgs.append(img_path)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "name 'imgs' is not defined", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m<ipython-input-2-5961b116db92>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;31m# im.show()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'imgs' is not defined" + ] + } + ], + "source": [ + "im = Image.open(imgs[0])\n", + "# im.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "deepface = DeepFaceLite()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "img, responses = deepface.analyze(im, detector_backend='mtcnn')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "responses" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "name": "pycharm-f9dc5b7", + "language": "python", + "display_name": "PyCharm (deepface)" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "3.8.5-final" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/extension/constants.py b/extension/constants.py new file mode 100644 index 0000000..a89185b --- /dev/null +++ b/extension/constants.py @@ -0,0 +1,84 @@ +CONSTANTS = { + 'ANIMAL_TYPE': ['cat', 'dog'], + 'EMOTION': { + 'HAPPY': 'Happy', + 'NEUTRAL': 'Neutral', + 'ANXIOUS': 'Anxious', + 'SAD': 'Sad', + 'UNSETTLED': 'Unsettled' + }, + 'STATISTICAL_DATA': { + "Cat": { + "feedback_number": 0, + "prediction_number": 0, + "prediction_data": { + "breed": {}, + "emotion": { + "Happy": 0, + "Neutral": 0, + "Anxious": 0, + "Sad": 0, + "Unsettled": 0 + } + }, + "feedback_data": { + "breed": { + "wrong": {}, + "correct": {} + }, + "emotion": { + "wrong": { + "Happy": 0, + "Neutral": 0, + "Anxious": 0, + "Sad": 0, + "Unsettled": 0 + }, + "correct": { + "Happy": 0, + "Neutral": 0, + "Anxious": 0, + "Sad": 0, + "Unsettled": 0 + } + } + } + }, + "Dog": { + "feedback_number": 0, + "prediction_number": 0, + "prediction_data": { + "breed": {}, + "emotion": { + "Happy": 0, + "Neutral": 0, + "Anxious": 0, + "Sad": 0, + "Unsettled": 0 + } + }, + "feedback_data": { + "breed": { + "wrong": {}, + "correct": {} + }, + "emotion": { + "wrong": { + "Happy": 0, + "Neutral": 0, + "Anxious": 0, + "Sad": 0, + "Unsettled": 0 + }, + "correct": { + "Happy": 0, + "Neutral": 0, + "Anxious": 0, + "Sad": 0, + "Unsettled": 0 + } + } + } + } + } +} diff --git a/extension/utilServices.py b/extension/utilServices.py new file mode 100644 index 0000000..4a7ff45 --- /dev/null +++ b/extension/utilServices.py @@ -0,0 +1,14 @@ +from flask import (Response) +from json import dumps + + +def send_json_response(body, status): + """ + format json response + :param body: dic object going to send + :param status: response status + :return: formatted response + """ + return Response(response=dumps(body), + status=status, + mimetype="application/json") diff --git a/font/FiraMono-Medium.otf b/font/FiraMono-Medium.otf new file mode 100644 index 0000000000000000000000000000000000000000..4f208e907e89a6db85e52fb313b266e334a8232a GIT binary patch literal 127344 zcmd442Y6IP+s8d~&Y4Z`z3pzw0;0wwn8{m?_EL?lk-uZW&9yRn=VVd6{M0}8V%JzUBSavYn 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new file mode 100644 index 0000000..285151a --- /dev/null +++ b/font/SIL Open Font License.txt @@ -0,0 +1,45 @@ +Copyright (c) 2014, Mozilla Foundation https://mozilla.org/ with Reserved Font Name Fira Mono. + +Copyright (c) 2014, Telefonica S.A. + +This Font Software is licensed under the SIL Open Font License, Version 1.1. +This license is copied below, and is also available with a FAQ at: http://scripts.sil.org/OFL + +----------------------------------------------------------- +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +----------------------------------------------------------- + +PREAMBLE +The goals of the Open Font License (OFL) are to stimulate worldwide development of collaborative font projects, to support the font creation efforts of academic and linguistic communities, and to provide a free and open framework in which fonts may be shared and improved in partnership with others. + +The OFL allows the licensed fonts to be used, studied, modified and redistributed freely as long as they are not sold by themselves. The fonts, including any derivative works, can be bundled, embedded, redistributed and/or sold with any software provided that any reserved names are not used by derivative works. The fonts and derivatives, however, cannot be released under any other type of license. The requirement for fonts to remain under this license does not apply to any document created using the fonts or their derivatives. + +DEFINITIONS +"Font Software" refers to the set of files released by the Copyright Holder(s) under this license and clearly marked as such. This may include source files, build scripts and documentation. + +"Reserved Font Name" refers to any names specified as such after the copyright statement(s). + +"Original Version" refers to the collection of Font Software components as distributed by the Copyright Holder(s). + +"Modified Version" refers to any derivative made by adding to, deleting, or substituting -- in part or in whole -- any of the components of the Original Version, by changing formats or by porting the Font Software to a new environment. + +"Author" refers to any designer, engineer, programmer, technical writer or other person who contributed to the Font Software. + +PERMISSION & CONDITIONS +Permission is hereby granted, free of charge, to any person obtaining a copy of the Font Software, to use, study, copy, merge, embed, modify, redistribute, and sell modified and unmodified copies of the Font Software, subject to the following conditions: + +1) Neither the Font Software nor any of its individual components, in Original or Modified Versions, may be sold by itself. + +2) Original or Modified Versions of the Font Software may be bundled, redistributed and/or sold with any software, provided that each copy contains the above copyright notice and this license. These can be included either as stand-alone text files, human-readable headers or in the appropriate machine-readable metadata fields within text or binary files as long as those fields can be easily viewed by the user. + +3) No Modified Version of the Font Software may use the Reserved Font Name(s) unless explicit written permission is granted by the corresponding Copyright Holder. This restriction only applies to the primary font name as presented to the users. + +4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font Software shall not be used to promote, endorse or advertise any Modified Version, except to acknowledge the contribution(s) of the Copyright Holder(s) and the Author(s) or with their explicit written permission. + +5) The Font Software, modified or unmodified, in part or in whole, must be distributed entirely under this license, and must not be distributed under any other license. The requirement for fonts to remain under this license does not apply to any document created using the Font Software. + +TERMINATION +This license becomes null and void if any of the above conditions are not met. + +DISCLAIMER +THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM OTHER DEALINGS IN THE FONT SOFTWARE. \ No newline at end of file diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/models/feedback.py b/models/feedback.py new file mode 100644 index 0000000..1dc7030 --- /dev/null +++ b/models/feedback.py @@ -0,0 +1,15 @@ +from config import db + + +class FeedbackContent(db.EmbeddedDocument): + type = db.StringField(required=True) + breedFeedback = db.StringField(required=True) + breedCorrectness = db.BooleanField(required=True) + emotionFeedback = db.StringField(required=True) + emotionCorrectness = db.BooleanField(required=True) + + +class Feedback(db.Document): + meta = {'collection': 'feedbacks'} + content = db.EmbeddedDocumentListField('FeedbackContent', required=True) + date = db.DateTimeField(required=True) diff --git a/models/prediction.py b/models/prediction.py new file mode 100644 index 0000000..7ec0aea --- /dev/null +++ b/models/prediction.py @@ -0,0 +1,8 @@ +from config import db + + +class Prediction(db.Document): + meta = {'collection': 'predictions'} + predictionResults = db.ListField(required=True) + rawPredictionResults = db.ListField(required=True) + date = db.DateTimeField(required=True) diff --git a/my_deepface.ipynb b/my_deepface.ipynb new file mode 100644 index 0000000..c8b9ce7 --- /dev/null +++ b/my_deepface.ipynb @@ -0,0 +1,754 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from deepface import DeepFace\n", + "import os\n", + "import cv2\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "# read images\n", + "imgs = []\n", + "img_dir = 'test_imgs/'\n", + "for img in os.listdir(img_dir):\n", + " if img.endswith('jpg') or img.endswith('jpeg'):\n", + " img_path = os.path.join(img_dir, img)\n", + " imgs.append(img_path)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "img1 = plt.imread(imgs[0])\n", + "plt.imshow(img1)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "img2 = plt.imread(imgs[1])\n", + "plt.imshow(img2)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Action: emotion: 100%|██████████| 3/3 [00:14<00:00, 4.96s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:5 out of the last 17 calls to .predict_function at 0x7fe2ddd7caf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:6 out of the last 19 calls to .predict_function at 0x7fe2dd798ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 20 calls to .predict_function at 0x7fe2dd9e09d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 21 calls to .predict_function at 0x7fe2de88c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2de88c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2de88c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de00a5e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dd9e0f70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dd9e0f70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2dd9e0f70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2dd798160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2ddc954c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2dd9e01f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2dd9e01f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dd9e01f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2de88c1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2de88c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2de88c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 13 calls to .predict_function at 0x7fe2de88c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de00a1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de3ec790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe31d495e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe31d495e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe31d495e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfd92c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2df888940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfd92430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2dfd92430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2dfd92430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfcc9d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfcc93a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfcc93a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2dfcc93a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de5544c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e239f790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de554ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de554ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2de554ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e2147310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e2147ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2df888940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2df888940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2df888940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2de3ec160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2de00a8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2df888310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2df888310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2df888310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2de7e4670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2ddc95dc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e239fa60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 13 calls to .predict_function at 0x7fe2e239fa60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e239fa60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de840b80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2df2435e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dd510700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2dd510700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2dd510700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de5540d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfcc9670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de554c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2de554c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2de554c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e274cc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d12940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e43f94c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444c040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d5c280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e3d5c280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e3d5c280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e4faad30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444cc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444cc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e444cc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d121f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfcc91f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d5c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d5c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e3d5c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dd5109d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2df243820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d5c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e3d5c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e3d5c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de7e4670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ddc95040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ddc95040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2ddc95040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de00a040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ddc95280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ddc95280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ddc95280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de554af0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfd92550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e239f820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e239f820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e239f820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444cee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e274c820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e274c820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e274c820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e4a9d5e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e2828310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "[262 48 88 88]\n", + "[649 54 99 99]\n", + "[57 68 88 88]\n", + "[448 57 92 92]\n", + "[450 251 87 87]\n", + "[831 250 96 96]\n", + "[264 255 85 85]\n", + "[653 272 84 84]\n", + "[448 438 96 96]\n", + "[256 456 93 93]\n", + "[649 446 101 101]\n", + "[643 644 104 104]\n", + "[ 65 639 79 79]\n", + "[847 641 86 86]\n", + "[248 655 114 114]\n", + "[835 819 92 92]\n", + "[450 830 93 93]\n", + "[256 832 88 88]\n", + "[643 838 91 91]\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e659b160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e6572e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e659be50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + ] + } + ], + "source": [ + "demography, imgs = DeepFace.analyze(imgs[0], actions=['age', 'gender', 'emotion'],\n", + " detector_backend='mtcnn')\n", + "# print(\"Age: \", demography[\"age\"])\n", + "# print(\"Gender: \", demography[\"gender\"])\n", + "# print(\"Emotion: \", demography[\"dominant_emotion\"])" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Age: 34.399849031832304\n", + "Gender: Woman\n", + "Emotion: happy\n" + ] + }, + { + "data": { + "text/plain": "" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "demo = demography[0]\n", + "print(\"Age: \", demo[\"age\"])\n", + "print(\"Gender: \", demo[\"gender\"])\n", + "print(\"Emotion: \", demo[\"dominant_emotion\"])\n", + "plt.imshow(imgs[0][:,:,::-1])" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Age: 24.706228912787836\n", + "Gender: Man\n", + "Emotion: happy\n" + ] + }, + { + "data": { + "text/plain": "" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "demo = demography[1]\n", + "print(\"Age: \", demo[\"age\"])\n", + "print(\"Gender: \", demo[\"gender\"])\n", + "print(\"Emotion: \", demo[\"dominant_emotion\"])\n", + "plt.imshow(imgs[1][:,:,::-1])" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Age: 36.036305006492086\n", + "Gender: Man\n", + "Emotion: happy\n" + ] + }, + { + "data": { + "text/plain": "" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "demo = demography[2]\n", + "print(\"Age: \", demo[\"age\"])\n", + "print(\"Gender: \", demo[\"gender\"])\n", + "print(\"Emotion: \", demo[\"dominant_emotion\"])\n", + "plt.imshow(imgs[2][:,:,::-1])" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Age: 35.126786088918394\n", + "Gender: Man\n", + "Emotion: happy\n" + ] + }, + { + "data": { + "text/plain": "" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "demo = demography[3]\n", + "print(\"Age: \", demo[\"age\"])\n", + "print(\"Gender: \", demo[\"gender\"])\n", + "print(\"Emotion: \", demo[\"dominant_emotion\"])\n", + "plt.imshow(imgs[3][:,:,::-1])\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Age: 37.149563607061395\n", + "Gender: Man\n", + "Emotion: happy\n" + ] + }, + { + "data": { + "text/plain": "" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "demo = demography[4]\n", + "print(\"Age: \", demo[\"age\"])\n", + "print(\"Gender: \", demo[\"gender\"])\n", + "print(\"Emotion: \", demo[\"dominant_emotion\"])\n", + "plt.imshow(imgs[4][:,:,::-1])\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": "19" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(demography)\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:5 out of the last 13 calls to .predict_function at 0x7fe2e444ce50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:6 out of the last 14 calls to .predict_function at 0x7fe2e274cd30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 15 calls to .predict_function at 0x7fe2e43f9ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 16 calls to .predict_function at 0x7fe2e43f9ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 17 calls to .predict_function at 0x7fe2e43f9ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e4a9d9d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e65b71f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e76cf8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e649d5e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e659b8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e659b8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e659b8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e896b160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e87dcca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e80c9d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e80c9d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e80c9d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e913f9d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e9125940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e896baf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e896baf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e896baf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e921eb80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ea9b7550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ea373ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e896bdc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e9d5f280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e9d5f280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e9d5f280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e921ea60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ea9b7c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e913f1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e913f1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e913f1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e913f550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e896b430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e913f3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e913f3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e913f3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e649daf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e76cf940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e896b700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e896b700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e896b700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e659b670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e4a9d5e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e649d310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e649d310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e649d310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2e3d12940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2e43f9550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 12 calls to .predict_function at 0x7fe2e274cf70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e274cf70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e274cf70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2df243c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 13 calls to .predict_function at 0x7fe2de554700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e444c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e444c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e9d5f940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2ea373670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e3d5caf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d5caf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e3d5caf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ec5a4430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ec5a40d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ea373a60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2ea373a60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2ea373a60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2edd11d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ebd9fc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2eac0c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2eac0c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2eac0c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ee2afe50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2eda6ac10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444c940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e444c940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e444c940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ee208d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2eda6ac10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ebd9f550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2ebd9f550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2ebd9f550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d5c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2ec5a4430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2edd11430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2edd11430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2edd11430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2ec5a4e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 12 calls to .predict_function at 0x7fe2e6572af0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2df243c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2df243c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2df243c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2df243550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 12 calls to .predict_function at 0x7fe2e4a9d790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 13 calls to .predict_function at 0x7fe2e444cee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444cee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444cee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2e2828310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2e649da60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 12 calls to .predict_function at 0x7fe2e659b550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e659b550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e659b550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2e896b040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 12 calls to .predict_function at 0x7fe2e91253a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 13 calls to .predict_function at 0x7fe2e87dc3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e87dc3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e87dc3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e913fdc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e921e280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2e9d5f1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2e9d5fd30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + ] + } + ], + "source": [ + "faces = DeepFace.detectFace(img1, detector_backend='mtcnn')\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" 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content: "\e257"; +} +.glyphicon-menu-right:before { + content: "\e258"; +} +.glyphicon-menu-down:before { + content: "\e259"; +} +.glyphicon-menu-up:before { + content: "\e260"; +} +* { + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} +*:before, +*:after { + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} +html { + font-size: 10px; + + -webkit-tap-highlight-color: rgba(0, 0, 0, 0); +} +body { + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; + font-size: 14px; + line-height: 1.42857143; + color: #333; + background-color: #fff; +} +input, +button, +select, +textarea { + font-family: inherit; + font-size: inherit; + line-height: inherit; +} +a { + color: #337ab7; + text-decoration: none; +} +a:hover, +a:focus { + color: #23527c; + text-decoration: underline; +} +a:focus { + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} +figure { + margin: 0; +} +img { + vertical-align: middle; +} +.img-responsive, +.thumbnail > img, +.thumbnail a > img, +.carousel-inner > .item > img, +.carousel-inner > .item > a > img { + display: block; + max-width: 100%; + height: auto; +} +.img-rounded { + border-radius: 6px; +} +.img-thumbnail { + display: inline-block; + max-width: 100%; + height: auto; + padding: 4px; + line-height: 1.42857143; + background-color: #fff; + border: 1px solid #ddd; + border-radius: 4px; + -webkit-transition: all .2s ease-in-out; + -o-transition: all .2s ease-in-out; + transition: all .2s ease-in-out; +} +.img-circle { + border-radius: 50%; +} +hr { + margin-top: 20px; + margin-bottom: 20px; + border: 0; + border-top: 1px solid #eee; +} +.sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + border: 0; +} +.sr-only-focusable:active, +.sr-only-focusable:focus { + position: static; + width: auto; + height: auto; + margin: 0; + overflow: visible; + clip: auto; +} +[role="button"] { + cursor: pointer; +} +h1, +h2, +h3, +h4, +h5, +h6, +.h1, +.h2, +.h3, +.h4, +.h5, +.h6 { + font-family: inherit; + font-weight: 500; + line-height: 1.1; + color: inherit; +} +h1 small, +h2 small, +h3 small, +h4 small, +h5 small, +h6 small, +.h1 small, +.h2 small, +.h3 small, +.h4 small, +.h5 small, +.h6 small, +h1 .small, +h2 .small, +h3 .small, +h4 .small, +h5 .small, +h6 .small, +.h1 .small, +.h2 .small, +.h3 .small, +.h4 .small, +.h5 .small, +.h6 .small { + font-weight: normal; + line-height: 1; + color: #777; +} +h1, +.h1, +h2, +.h2, +h3, +.h3 { + margin-top: 20px; + margin-bottom: 10px; +} +h1 small, +.h1 small, +h2 small, +.h2 small, +h3 small, +.h3 small, +h1 .small, +.h1 .small, +h2 .small, +.h2 .small, +h3 .small, +.h3 .small { + font-size: 65%; +} +h4, +.h4, +h5, +.h5, +h6, +.h6 { + margin-top: 10px; + margin-bottom: 10px; +} +h4 small, +.h4 small, +h5 small, +.h5 small, +h6 small, +.h6 small, +h4 .small, +.h4 .small, +h5 .small, +.h5 .small, +h6 .small, +.h6 .small { + font-size: 75%; +} +h1, +.h1 { + font-size: 36px; +} +h2, +.h2 { + font-size: 30px; +} +h3, +.h3 { + font-size: 24px; +} +h4, +.h4 { + font-size: 18px; +} +h5, +.h5 { + font-size: 14px; +} +h6, +.h6 { + font-size: 12px; +} +p { + margin: 0 0 10px; +} +.lead { + margin-bottom: 20px; + font-size: 16px; + font-weight: 300; + line-height: 1.4; +} +@media (min-width: 768px) { + .lead { + font-size: 21px; + } +} +small, +.small { + font-size: 85%; +} +mark, +.mark { + padding: .2em; + background-color: #fcf8e3; +} +.text-left { + text-align: left; +} +.text-right { + text-align: right; +} +.text-center { + text-align: center; +} +.text-justify { + text-align: justify; +} +.text-nowrap { + white-space: nowrap; +} +.text-lowercase { + text-transform: lowercase; +} +.text-uppercase { + text-transform: uppercase; +} +.text-capitalize { + text-transform: capitalize; +} +.text-muted { + color: #777; +} +.text-primary { + color: #337ab7; +} +a.text-primary:hover, +a.text-primary:focus { + color: #286090; +} +.text-success { + color: #3c763d; +} +a.text-success:hover, +a.text-success:focus { + color: #2b542c; +} +.text-info { + color: #31708f; +} +a.text-info:hover, +a.text-info:focus { + color: #245269; +} +.text-warning { + color: #8a6d3b; +} +a.text-warning:hover, +a.text-warning:focus { + color: #66512c; +} +.text-danger { + color: #a94442; +} +a.text-danger:hover, +a.text-danger:focus { + color: #843534; +} +.bg-primary { + color: #fff; + background-color: #337ab7; +} +a.bg-primary:hover, +a.bg-primary:focus { + background-color: #286090; +} +.bg-success { + background-color: #dff0d8; +} +a.bg-success:hover, +a.bg-success:focus { + background-color: #c1e2b3; +} +.bg-info { + background-color: #d9edf7; +} +a.bg-info:hover, +a.bg-info:focus { + background-color: #afd9ee; +} +.bg-warning { + background-color: #fcf8e3; +} +a.bg-warning:hover, +a.bg-warning:focus { + background-color: #f7ecb5; +} +.bg-danger { + background-color: #f2dede; +} +a.bg-danger:hover, +a.bg-danger:focus { + background-color: #e4b9b9; +} +.page-header { + padding-bottom: 9px; + margin: 40px 0 20px; + border-bottom: 1px solid #eee; +} +ul, +ol { + margin-top: 0; + margin-bottom: 10px; +} +ul ul, +ol ul, +ul ol, +ol ol { + margin-bottom: 0; +} +.list-unstyled { + padding-left: 0; + list-style: none; +} +.list-inline { + padding-left: 0; + margin-left: -5px; + list-style: none; +} +.list-inline > li { + display: inline-block; + padding-right: 5px; + padding-left: 5px; +} +dl { + margin-top: 0; + margin-bottom: 20px; +} +dt, +dd { + line-height: 1.42857143; +} +dt { + font-weight: bold; +} +dd { + margin-left: 0; +} +@media (min-width: 768px) { + .dl-horizontal dt { + float: left; + width: 160px; + overflow: hidden; + clear: left; + text-align: right; + text-overflow: ellipsis; + white-space: nowrap; + } + .dl-horizontal dd { + margin-left: 180px; + } +} +abbr[title], +abbr[data-original-title] { + cursor: help; + border-bottom: 1px dotted #777; +} +.initialism { + font-size: 90%; + text-transform: uppercase; +} +blockquote { + padding: 10px 20px; + margin: 0 0 20px; + font-size: 17.5px; + border-left: 5px solid #eee; +} +blockquote p:last-child, +blockquote ul:last-child, +blockquote ol:last-child { + margin-bottom: 0; +} +blockquote footer, +blockquote small, +blockquote .small { + display: block; + font-size: 80%; + line-height: 1.42857143; + color: #777; +} +blockquote footer:before, +blockquote small:before, +blockquote .small:before { + content: '\2014 \00A0'; +} +.blockquote-reverse, +blockquote.pull-right { + padding-right: 15px; + padding-left: 0; + text-align: right; + border-right: 5px solid #eee; + border-left: 0; +} +.blockquote-reverse footer:before, +blockquote.pull-right footer:before, +.blockquote-reverse small:before, +blockquote.pull-right small:before, +.blockquote-reverse .small:before, +blockquote.pull-right .small:before { + content: ''; +} +.blockquote-reverse footer:after, +blockquote.pull-right footer:after, +.blockquote-reverse small:after, +blockquote.pull-right small:after, +.blockquote-reverse .small:after, +blockquote.pull-right .small:after { + content: '\00A0 \2014'; +} +address { + margin-bottom: 20px; + font-style: normal; + line-height: 1.42857143; +} +code, +kbd, +pre, +samp { + font-family: Menlo, Monaco, Consolas, "Courier New", monospace; +} +code { + padding: 2px 4px; + font-size: 90%; + color: #c7254e; + background-color: #f9f2f4; + border-radius: 4px; +} +kbd { + padding: 2px 4px; + font-size: 90%; + color: #fff; + background-color: #333; + border-radius: 3px; + -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .25); + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .25); +} +kbd kbd { + padding: 0; + font-size: 100%; + font-weight: bold; + -webkit-box-shadow: none; + box-shadow: none; +} +pre { + display: block; + padding: 9.5px; + margin: 0 0 10px; + font-size: 13px; + line-height: 1.42857143; + color: #333; + word-break: break-all; + word-wrap: break-word; + background-color: #f5f5f5; + border: 1px solid #ccc; + border-radius: 4px; +} +pre code { + padding: 0; + font-size: inherit; + color: inherit; + white-space: pre-wrap; + background-color: transparent; + border-radius: 0; +} +.pre-scrollable { + max-height: 340px; + overflow-y: scroll; +} +.container { + padding-right: 15px; + padding-left: 15px; + margin-right: auto; + margin-left: auto; +} +@media (min-width: 768px) { + .container { + width: 750px; + } +} +@media (min-width: 992px) { + .container { + width: 970px; + } +} +@media (min-width: 1200px) { + .container { + width: 1170px; + } +} +.container-fluid { + padding-right: 15px; + padding-left: 15px; + margin-right: auto; + margin-left: auto; +} +.row { + margin-right: -15px; + margin-left: -15px; +} +.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 { + position: relative; + min-height: 1px; + padding-right: 15px; + padding-left: 15px; +} +.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 { + float: left; +} +.col-xs-12 { + width: 100%; +} +.col-xs-11 { + width: 91.66666667%; +} +.col-xs-10 { + width: 83.33333333%; +} +.col-xs-9 { + width: 75%; +} +.col-xs-8 { + width: 66.66666667%; +} +.col-xs-7 { + width: 58.33333333%; +} +.col-xs-6 { + width: 50%; +} +.col-xs-5 { + width: 41.66666667%; +} +.col-xs-4 { + width: 33.33333333%; +} +.col-xs-3 { + width: 25%; +} +.col-xs-2 { + width: 16.66666667%; +} +.col-xs-1 { + width: 8.33333333%; +} +.col-xs-pull-12 { + right: 100%; +} +.col-xs-pull-11 { + right: 91.66666667%; +} +.col-xs-pull-10 { + right: 83.33333333%; +} +.col-xs-pull-9 { + right: 75%; +} +.col-xs-pull-8 { + right: 66.66666667%; +} +.col-xs-pull-7 { + right: 58.33333333%; +} +.col-xs-pull-6 { + right: 50%; +} +.col-xs-pull-5 { + right: 41.66666667%; +} +.col-xs-pull-4 { + right: 33.33333333%; +} +.col-xs-pull-3 { + right: 25%; +} +.col-xs-pull-2 { + right: 16.66666667%; +} +.col-xs-pull-1 { + right: 8.33333333%; +} +.col-xs-pull-0 { + right: auto; +} +.col-xs-push-12 { + left: 100%; +} +.col-xs-push-11 { + left: 91.66666667%; +} +.col-xs-push-10 { + left: 83.33333333%; +} +.col-xs-push-9 { + left: 75%; +} +.col-xs-push-8 { + left: 66.66666667%; +} +.col-xs-push-7 { + left: 58.33333333%; +} +.col-xs-push-6 { + left: 50%; +} +.col-xs-push-5 { + left: 41.66666667%; +} +.col-xs-push-4 { + left: 33.33333333%; +} +.col-xs-push-3 { + left: 25%; +} +.col-xs-push-2 { + left: 16.66666667%; +} +.col-xs-push-1 { + left: 8.33333333%; +} +.col-xs-push-0 { + left: auto; +} +.col-xs-offset-12 { + margin-left: 100%; +} +.col-xs-offset-11 { + margin-left: 91.66666667%; +} +.col-xs-offset-10 { + margin-left: 83.33333333%; +} +.col-xs-offset-9 { + margin-left: 75%; +} +.col-xs-offset-8 { + margin-left: 66.66666667%; +} +.col-xs-offset-7 { + margin-left: 58.33333333%; +} +.col-xs-offset-6 { + margin-left: 50%; +} +.col-xs-offset-5 { + margin-left: 41.66666667%; +} +.col-xs-offset-4 { + margin-left: 33.33333333%; +} +.col-xs-offset-3 { + margin-left: 25%; +} +.col-xs-offset-2 { + margin-left: 16.66666667%; +} +.col-xs-offset-1 { + margin-left: 8.33333333%; +} +.col-xs-offset-0 { + margin-left: 0; +} +@media (min-width: 768px) { + .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 { + float: left; + } + .col-sm-12 { + width: 100%; + } + .col-sm-11 { + width: 91.66666667%; + } + .col-sm-10 { + width: 83.33333333%; + } + .col-sm-9 { + width: 75%; + } + .col-sm-8 { + width: 66.66666667%; + } + .col-sm-7 { + width: 58.33333333%; + } + .col-sm-6 { + width: 50%; + } + .col-sm-5 { + width: 41.66666667%; + } + .col-sm-4 { + width: 33.33333333%; + } + .col-sm-3 { + width: 25%; + } + .col-sm-2 { + width: 16.66666667%; + } + .col-sm-1 { + width: 8.33333333%; + } + .col-sm-pull-12 { + right: 100%; + } + .col-sm-pull-11 { + right: 91.66666667%; + } + .col-sm-pull-10 { + right: 83.33333333%; + } + .col-sm-pull-9 { + right: 75%; + } + .col-sm-pull-8 { + right: 66.66666667%; + } + .col-sm-pull-7 { + right: 58.33333333%; + } + .col-sm-pull-6 { + right: 50%; + } + .col-sm-pull-5 { + right: 41.66666667%; + } + .col-sm-pull-4 { + right: 33.33333333%; + } + .col-sm-pull-3 { + right: 25%; + } + .col-sm-pull-2 { + right: 16.66666667%; + } + .col-sm-pull-1 { + right: 8.33333333%; + } + .col-sm-pull-0 { + right: auto; + } + .col-sm-push-12 { + left: 100%; + } + .col-sm-push-11 { + left: 91.66666667%; + } + .col-sm-push-10 { + left: 83.33333333%; + } + .col-sm-push-9 { + left: 75%; + } + .col-sm-push-8 { + left: 66.66666667%; + } + .col-sm-push-7 { + left: 58.33333333%; + } + .col-sm-push-6 { + left: 50%; + } + .col-sm-push-5 { + left: 41.66666667%; + } + .col-sm-push-4 { + left: 33.33333333%; + } + .col-sm-push-3 { + left: 25%; + } + .col-sm-push-2 { + left: 16.66666667%; + } + .col-sm-push-1 { + left: 8.33333333%; + } + .col-sm-push-0 { + left: auto; + } + .col-sm-offset-12 { + margin-left: 100%; + } + .col-sm-offset-11 { + margin-left: 91.66666667%; + } + .col-sm-offset-10 { + margin-left: 83.33333333%; + } + .col-sm-offset-9 { + margin-left: 75%; + } + .col-sm-offset-8 { + margin-left: 66.66666667%; + } + .col-sm-offset-7 { + margin-left: 58.33333333%; + } + .col-sm-offset-6 { + margin-left: 50%; + } + .col-sm-offset-5 { + margin-left: 41.66666667%; + } + .col-sm-offset-4 { + margin-left: 33.33333333%; + } + .col-sm-offset-3 { + margin-left: 25%; + } + .col-sm-offset-2 { + margin-left: 16.66666667%; + } + .col-sm-offset-1 { + margin-left: 8.33333333%; + } + .col-sm-offset-0 { + margin-left: 0; + } +} +@media (min-width: 992px) { + .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 { + float: left; + } + .col-md-12 { + width: 100%; + } + .col-md-11 { + width: 91.66666667%; + } + .col-md-10 { + width: 83.33333333%; + } + .col-md-9 { + width: 75%; + } + .col-md-8 { + width: 66.66666667%; + } + .col-md-7 { + width: 58.33333333%; + } + .col-md-6 { + width: 50%; + } + .col-md-5 { + width: 41.66666667%; + } + .col-md-4 { + width: 33.33333333%; + } + .col-md-3 { + width: 25%; + } + .col-md-2 { + width: 16.66666667%; + } + .col-md-1 { + width: 8.33333333%; + } + .col-md-pull-12 { + right: 100%; + } + .col-md-pull-11 { + right: 91.66666667%; + } + .col-md-pull-10 { + right: 83.33333333%; + } + .col-md-pull-9 { + right: 75%; + } + .col-md-pull-8 { + right: 66.66666667%; + } + .col-md-pull-7 { + right: 58.33333333%; + } + .col-md-pull-6 { + right: 50%; + } + .col-md-pull-5 { + right: 41.66666667%; + } + .col-md-pull-4 { + right: 33.33333333%; + } + .col-md-pull-3 { + right: 25%; + } + .col-md-pull-2 { + right: 16.66666667%; + } + .col-md-pull-1 { + right: 8.33333333%; + } + .col-md-pull-0 { + right: auto; + } + .col-md-push-12 { + left: 100%; + } + .col-md-push-11 { + left: 91.66666667%; + } + .col-md-push-10 { + left: 83.33333333%; + } + .col-md-push-9 { + left: 75%; + } + .col-md-push-8 { + left: 66.66666667%; + } + .col-md-push-7 { + left: 58.33333333%; + } + .col-md-push-6 { + left: 50%; + } + .col-md-push-5 { + left: 41.66666667%; + } + .col-md-push-4 { + left: 33.33333333%; + } + .col-md-push-3 { + left: 25%; + } + .col-md-push-2 { + left: 16.66666667%; + } + .col-md-push-1 { + left: 8.33333333%; + } + .col-md-push-0 { + left: auto; + } + .col-md-offset-12 { + margin-left: 100%; + } + .col-md-offset-11 { + margin-left: 91.66666667%; + } + .col-md-offset-10 { + margin-left: 83.33333333%; + } + .col-md-offset-9 { + margin-left: 75%; + } + .col-md-offset-8 { + margin-left: 66.66666667%; + } + .col-md-offset-7 { + margin-left: 58.33333333%; + } + .col-md-offset-6 { + margin-left: 50%; + } + .col-md-offset-5 { + margin-left: 41.66666667%; + } + .col-md-offset-4 { + margin-left: 33.33333333%; + } + .col-md-offset-3 { + margin-left: 25%; + } + .col-md-offset-2 { + margin-left: 16.66666667%; + } + .col-md-offset-1 { + margin-left: 8.33333333%; + } + .col-md-offset-0 { + margin-left: 0; + } +} +@media (min-width: 1200px) { + .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 { + float: left; + } + .col-lg-12 { + width: 100%; + } + .col-lg-11 { + width: 91.66666667%; + } + .col-lg-10 { + width: 83.33333333%; + } + .col-lg-9 { + width: 75%; + } + .col-lg-8 { + width: 66.66666667%; + } + .col-lg-7 { + width: 58.33333333%; + } + .col-lg-6 { + width: 50%; + } + .col-lg-5 { + width: 41.66666667%; + } + .col-lg-4 { + width: 33.33333333%; + } + .col-lg-3 { + width: 25%; + } + .col-lg-2 { + width: 16.66666667%; + } + .col-lg-1 { + width: 8.33333333%; + } + .col-lg-pull-12 { + right: 100%; + } + .col-lg-pull-11 { + right: 91.66666667%; + } + .col-lg-pull-10 { + right: 83.33333333%; + } + .col-lg-pull-9 { + right: 75%; + } + .col-lg-pull-8 { + right: 66.66666667%; + } + .col-lg-pull-7 { + right: 58.33333333%; + } + .col-lg-pull-6 { + right: 50%; + } + .col-lg-pull-5 { + right: 41.66666667%; + } + .col-lg-pull-4 { + right: 33.33333333%; + } + .col-lg-pull-3 { + right: 25%; + } + .col-lg-pull-2 { + right: 16.66666667%; + } + .col-lg-pull-1 { + right: 8.33333333%; + } + .col-lg-pull-0 { + right: auto; + } + .col-lg-push-12 { + left: 100%; + } + .col-lg-push-11 { + left: 91.66666667%; + } + .col-lg-push-10 { + left: 83.33333333%; + } + .col-lg-push-9 { + left: 75%; + } + .col-lg-push-8 { + left: 66.66666667%; + } + .col-lg-push-7 { + left: 58.33333333%; + } + .col-lg-push-6 { + left: 50%; + } + .col-lg-push-5 { + left: 41.66666667%; + } + .col-lg-push-4 { + left: 33.33333333%; + } + .col-lg-push-3 { + left: 25%; + } + .col-lg-push-2 { + left: 16.66666667%; + } + .col-lg-push-1 { + left: 8.33333333%; + } + .col-lg-push-0 { + left: auto; + } + .col-lg-offset-12 { + margin-left: 100%; + } + .col-lg-offset-11 { + margin-left: 91.66666667%; + } + .col-lg-offset-10 { + margin-left: 83.33333333%; + } + .col-lg-offset-9 { + margin-left: 75%; + } + .col-lg-offset-8 { + margin-left: 66.66666667%; + } + .col-lg-offset-7 { + margin-left: 58.33333333%; + } + .col-lg-offset-6 { + margin-left: 50%; + } + .col-lg-offset-5 { + margin-left: 41.66666667%; + } + .col-lg-offset-4 { + margin-left: 33.33333333%; + } + .col-lg-offset-3 { + margin-left: 25%; + } + .col-lg-offset-2 { + margin-left: 16.66666667%; + } + .col-lg-offset-1 { + margin-left: 8.33333333%; + } + .col-lg-offset-0 { + margin-left: 0; + } +} +table { + background-color: transparent; +} +caption { + padding-top: 8px; + padding-bottom: 8px; + color: #777; + text-align: left; +} +th { + text-align: left; +} +.table { + width: 100%; + max-width: 100%; + margin-bottom: 20px; +} +.table > thead > tr > th, +.table > tbody > tr > th, +.table > tfoot > tr > th, +.table > thead > tr > td, +.table > tbody > tr > td, +.table > tfoot > tr > td { + padding: 8px; + line-height: 1.42857143; + vertical-align: top; + border-top: 1px solid #ddd; +} +.table > thead > tr > th { + vertical-align: bottom; + border-bottom: 2px solid #ddd; +} +.table > caption + thead > tr:first-child > th, +.table > colgroup + thead > tr:first-child > th, +.table > thead:first-child > tr:first-child > th, +.table > caption + thead > tr:first-child > td, +.table > colgroup + thead > tr:first-child > td, +.table > thead:first-child > tr:first-child > td { + border-top: 0; +} +.table > tbody + tbody { + border-top: 2px solid #ddd; +} +.table .table { + background-color: #fff; +} +.table-condensed > thead > tr > th, +.table-condensed > tbody > tr > th, +.table-condensed > tfoot > tr > th, +.table-condensed > thead > tr > td, +.table-condensed > tbody > tr > td, +.table-condensed > tfoot > tr > td { + padding: 5px; +} +.table-bordered { + border: 1px solid #ddd; +} +.table-bordered > thead > tr > th, +.table-bordered > tbody > tr > th, +.table-bordered > tfoot > tr > th, +.table-bordered > thead > tr > td, +.table-bordered > tbody > tr > td, +.table-bordered > tfoot > tr > td { + border: 1px solid #ddd; +} +.table-bordered > thead > tr > th, +.table-bordered > thead > tr > td { + border-bottom-width: 2px; +} +.table-striped > tbody > tr:nth-of-type(odd) { + background-color: #f9f9f9; +} +.table-hover > tbody > tr:hover { + background-color: #f5f5f5; +} +table col[class*="col-"] { + position: static; + display: table-column; + float: none; +} +table td[class*="col-"], +table th[class*="col-"] { + position: static; + display: table-cell; + float: none; +} +.table > thead > tr > td.active, +.table > tbody > tr > td.active, +.table > tfoot > tr > td.active, +.table > thead > tr > th.active, +.table > tbody > tr > th.active, +.table > tfoot > tr > th.active, +.table > thead > tr.active > td, +.table > tbody > tr.active > td, +.table > tfoot > tr.active > td, +.table > thead > tr.active > th, +.table > tbody > tr.active > th, +.table > tfoot > tr.active > th { + background-color: #f5f5f5; +} +.table-hover > tbody > tr > td.active:hover, +.table-hover > tbody > tr > th.active:hover, +.table-hover > tbody > tr.active:hover > td, +.table-hover > tbody > tr:hover > .active, +.table-hover > tbody > tr.active:hover > th { + background-color: #e8e8e8; +} +.table > thead > tr > td.success, +.table > tbody > tr > td.success, +.table > tfoot > tr > td.success, +.table > thead > tr > th.success, +.table > tbody > tr > th.success, +.table > tfoot > tr > th.success, +.table > thead > tr.success > td, +.table > tbody > tr.success > td, +.table > tfoot > tr.success > td, +.table > thead > tr.success > th, +.table > tbody > tr.success > th, +.table > tfoot > tr.success > th { + background-color: #dff0d8; +} +.table-hover > tbody > tr > td.success:hover, +.table-hover > tbody > tr > th.success:hover, +.table-hover > tbody > tr.success:hover > td, +.table-hover > tbody > tr:hover > .success, +.table-hover > tbody > tr.success:hover > th { + background-color: #d0e9c6; +} +.table > thead > tr > td.info, +.table > tbody > tr > td.info, +.table > tfoot > tr > td.info, +.table > thead > tr > th.info, +.table > tbody > tr > th.info, +.table > tfoot > tr > th.info, +.table > thead > tr.info > td, +.table > tbody > tr.info > td, +.table > tfoot > tr.info > td, +.table > thead > tr.info > th, +.table > tbody > tr.info > th, +.table > tfoot > tr.info > th { + background-color: #d9edf7; +} +.table-hover > tbody > tr > td.info:hover, +.table-hover > tbody > tr > th.info:hover, +.table-hover > tbody > tr.info:hover > td, +.table-hover > tbody > tr:hover > .info, +.table-hover > tbody > tr.info:hover > th { + background-color: #c4e3f3; +} +.table > thead > tr > td.warning, +.table > tbody > tr > td.warning, +.table > tfoot > tr > td.warning, +.table > thead > tr > th.warning, +.table > tbody > tr > th.warning, +.table > tfoot > tr > th.warning, +.table > thead > tr.warning > td, +.table > tbody > tr.warning > td, +.table > tfoot > tr.warning > td, +.table > thead > tr.warning > th, +.table > tbody > tr.warning > th, +.table > tfoot > tr.warning > th { + background-color: #fcf8e3; +} +.table-hover > tbody > tr > td.warning:hover, +.table-hover > tbody > tr > th.warning:hover, +.table-hover > tbody > tr.warning:hover > td, +.table-hover > tbody > tr:hover > .warning, +.table-hover > tbody > tr.warning:hover > th { + background-color: #faf2cc; +} +.table > thead > tr > td.danger, +.table > tbody > tr > td.danger, +.table > tfoot > tr > td.danger, +.table > thead > tr > th.danger, +.table > tbody > tr > th.danger, +.table > tfoot > tr > th.danger, +.table > thead > tr.danger > td, +.table > tbody > tr.danger > td, +.table > tfoot > tr.danger > td, +.table > thead > tr.danger > th, +.table > tbody > tr.danger > th, +.table > tfoot > tr.danger > th { + background-color: #f2dede; +} +.table-hover > tbody > tr > td.danger:hover, +.table-hover > tbody > tr > th.danger:hover, +.table-hover > tbody > tr.danger:hover > td, +.table-hover > tbody > tr:hover > .danger, +.table-hover > tbody > tr.danger:hover > th { + background-color: #ebcccc; +} +.table-responsive { + min-height: .01%; + overflow-x: auto; +} +@media screen and (max-width: 767px) { + .table-responsive { + width: 100%; + margin-bottom: 15px; + overflow-y: hidden; + -ms-overflow-style: -ms-autohiding-scrollbar; + border: 1px solid #ddd; + } + .table-responsive > .table { + margin-bottom: 0; + } + .table-responsive > .table > thead > tr > th, + .table-responsive > .table > tbody > tr > th, + .table-responsive > .table > tfoot > tr > th, + .table-responsive > .table > thead > tr > td, + .table-responsive > .table > tbody > tr > td, + .table-responsive > .table > tfoot > tr > td { + white-space: nowrap; + } + .table-responsive > .table-bordered { + border: 0; + } + .table-responsive > .table-bordered > thead > tr > th:first-child, + .table-responsive > .table-bordered > tbody > tr > th:first-child, + .table-responsive > .table-bordered > tfoot > tr > th:first-child, + .table-responsive > .table-bordered > thead > tr > td:first-child, + .table-responsive > .table-bordered > tbody > tr > td:first-child, + .table-responsive > .table-bordered > tfoot > tr > td:first-child { + border-left: 0; + } + .table-responsive > .table-bordered > thead > tr > th:last-child, + .table-responsive > .table-bordered > tbody > tr > th:last-child, + .table-responsive > .table-bordered > tfoot > tr > th:last-child, + .table-responsive > .table-bordered > thead > tr > td:last-child, + .table-responsive > .table-bordered > tbody > tr > td:last-child, + .table-responsive > .table-bordered > tfoot > tr > td:last-child { + border-right: 0; + } + .table-responsive > .table-bordered > tbody > tr:last-child > th, + .table-responsive > .table-bordered > tfoot > tr:last-child > th, + .table-responsive > .table-bordered > tbody > tr:last-child > td, + .table-responsive > .table-bordered > tfoot > tr:last-child > td { + border-bottom: 0; + } +} +fieldset { + min-width: 0; + padding: 0; + margin: 0; + border: 0; +} +legend { + display: block; + width: 100%; + padding: 0; + margin-bottom: 20px; + font-size: 21px; + line-height: inherit; + color: #333; + border: 0; + border-bottom: 1px solid #e5e5e5; +} +label { + display: inline-block; + max-width: 100%; + margin-bottom: 5px; + font-weight: bold; +} +input[type="search"] { + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} +input[type="radio"], +input[type="checkbox"] { + margin: 4px 0 0; + margin-top: 1px \9; + line-height: normal; +} +input[type="file"] { + display: block; +} +input[type="range"] { + display: block; + width: 100%; +} +select[multiple], +select[size] { + height: auto; +} +input[type="file"]:focus, +input[type="radio"]:focus, +input[type="checkbox"]:focus { + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} +output { + display: block; + padding-top: 7px; + font-size: 14px; + line-height: 1.42857143; + color: #555; +} +.form-control { + display: block; + width: 100%; + height: 34px; + padding: 6px 12px; + font-size: 14px; + line-height: 1.42857143; + color: #555; + background-color: #fff; + background-image: none; + border: 1px solid #ccc; + border-radius: 4px; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); + -webkit-transition: border-color ease-in-out .15s, -webkit-box-shadow ease-in-out .15s; + -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; + transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; +} +.form-control:focus { + border-color: #66afe9; + outline: 0; + -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, .6); + box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, .6); +} +.form-control::-moz-placeholder { + color: #999; + opacity: 1; +} +.form-control:-ms-input-placeholder { + color: #999; +} +.form-control::-webkit-input-placeholder { + color: #999; +} +.form-control::-ms-expand { + background-color: transparent; + border: 0; +} +.form-control[disabled], +.form-control[readonly], +fieldset[disabled] .form-control { + background-color: #eee; + opacity: 1; +} +.form-control[disabled], +fieldset[disabled] .form-control { + cursor: not-allowed; +} +textarea.form-control { + height: auto; +} +input[type="search"] { + -webkit-appearance: none; +} +@media screen and (-webkit-min-device-pixel-ratio: 0) { + input[type="date"].form-control, + input[type="time"].form-control, + input[type="datetime-local"].form-control, + input[type="month"].form-control { + line-height: 34px; + } + input[type="date"].input-sm, + input[type="time"].input-sm, + input[type="datetime-local"].input-sm, + input[type="month"].input-sm, + .input-group-sm input[type="date"], + .input-group-sm input[type="time"], + .input-group-sm input[type="datetime-local"], + .input-group-sm input[type="month"] { + line-height: 30px; + } + input[type="date"].input-lg, + input[type="time"].input-lg, + input[type="datetime-local"].input-lg, + input[type="month"].input-lg, + .input-group-lg input[type="date"], + .input-group-lg input[type="time"], + .input-group-lg input[type="datetime-local"], + .input-group-lg input[type="month"] { + line-height: 46px; + } +} +.form-group { + margin-bottom: 15px; +} +.radio, +.checkbox { + position: relative; + display: block; + margin-top: 10px; + margin-bottom: 10px; +} +.radio label, +.checkbox label { + min-height: 20px; + padding-left: 20px; + margin-bottom: 0; + font-weight: normal; + cursor: pointer; +} +.radio input[type="radio"], +.radio-inline input[type="radio"], +.checkbox input[type="checkbox"], +.checkbox-inline input[type="checkbox"] { + position: absolute; + margin-top: 4px \9; + margin-left: -20px; +} +.radio + .radio, +.checkbox + .checkbox { + margin-top: -5px; +} +.radio-inline, +.checkbox-inline { + position: relative; + display: inline-block; + padding-left: 20px; + margin-bottom: 0; + font-weight: normal; + vertical-align: middle; + cursor: pointer; +} +.radio-inline + .radio-inline, +.checkbox-inline + .checkbox-inline { + margin-top: 0; + margin-left: 10px; +} +input[type="radio"][disabled], +input[type="checkbox"][disabled], +input[type="radio"].disabled, +input[type="checkbox"].disabled, +fieldset[disabled] input[type="radio"], +fieldset[disabled] input[type="checkbox"] { + cursor: not-allowed; +} +.radio-inline.disabled, +.checkbox-inline.disabled, +fieldset[disabled] .radio-inline, +fieldset[disabled] .checkbox-inline { + cursor: not-allowed; +} +.radio.disabled label, +.checkbox.disabled label, +fieldset[disabled] .radio label, +fieldset[disabled] .checkbox label { + cursor: not-allowed; +} +.form-control-static { + min-height: 34px; + padding-top: 7px; + padding-bottom: 7px; + margin-bottom: 0; +} +.form-control-static.input-lg, +.form-control-static.input-sm { + padding-right: 0; + padding-left: 0; +} +.input-sm { + height: 30px; + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 3px; +} +select.input-sm { + height: 30px; + line-height: 30px; +} +textarea.input-sm, +select[multiple].input-sm { + height: auto; +} +.form-group-sm .form-control { + height: 30px; + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 3px; +} +.form-group-sm select.form-control { + height: 30px; + line-height: 30px; +} +.form-group-sm textarea.form-control, +.form-group-sm select[multiple].form-control { + height: auto; +} +.form-group-sm .form-control-static { + height: 30px; + min-height: 32px; + padding: 6px 10px; + font-size: 12px; + line-height: 1.5; +} +.input-lg { + height: 46px; + padding: 10px 16px; + font-size: 18px; + line-height: 1.3333333; + border-radius: 6px; +} +select.input-lg { + height: 46px; + line-height: 46px; +} +textarea.input-lg, +select[multiple].input-lg { + height: auto; +} +.form-group-lg .form-control { + height: 46px; + padding: 10px 16px; + font-size: 18px; + line-height: 1.3333333; + border-radius: 6px; +} +.form-group-lg select.form-control { + height: 46px; + line-height: 46px; +} +.form-group-lg textarea.form-control, +.form-group-lg select[multiple].form-control { + height: auto; +} +.form-group-lg .form-control-static { + height: 46px; + min-height: 38px; + padding: 11px 16px; + font-size: 18px; + line-height: 1.3333333; +} +.has-feedback { + position: relative; +} +.has-feedback .form-control { + padding-right: 42.5px; +} +.form-control-feedback { + position: absolute; + top: 0; + right: 0; + z-index: 2; + display: block; + width: 34px; + height: 34px; + line-height: 34px; + text-align: center; + pointer-events: none; +} +.input-lg + .form-control-feedback, +.input-group-lg + .form-control-feedback, +.form-group-lg .form-control + .form-control-feedback { + width: 46px; + height: 46px; + line-height: 46px; +} +.input-sm + .form-control-feedback, +.input-group-sm + .form-control-feedback, +.form-group-sm .form-control + .form-control-feedback { + width: 30px; + height: 30px; + line-height: 30px; +} +.has-success .help-block, +.has-success .control-label, +.has-success .radio, +.has-success .checkbox, +.has-success .radio-inline, +.has-success .checkbox-inline, +.has-success.radio label, +.has-success.checkbox label, +.has-success.radio-inline label, +.has-success.checkbox-inline label { + color: #3c763d; +} +.has-success .form-control { + border-color: #3c763d; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); +} +.has-success .form-control:focus { + border-color: #2b542c; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #67b168; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #67b168; +} +.has-success .input-group-addon { + color: #3c763d; + background-color: #dff0d8; + border-color: #3c763d; +} +.has-success .form-control-feedback { + color: #3c763d; +} +.has-warning .help-block, +.has-warning .control-label, +.has-warning .radio, +.has-warning .checkbox, +.has-warning .radio-inline, +.has-warning .checkbox-inline, +.has-warning.radio label, +.has-warning.checkbox label, +.has-warning.radio-inline label, +.has-warning.checkbox-inline label { + color: #8a6d3b; +} +.has-warning .form-control { + border-color: #8a6d3b; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); +} +.has-warning .form-control:focus { + border-color: #66512c; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #c0a16b; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #c0a16b; +} +.has-warning .input-group-addon { + color: #8a6d3b; + background-color: #fcf8e3; + border-color: #8a6d3b; +} +.has-warning .form-control-feedback { + color: #8a6d3b; +} +.has-error .help-block, +.has-error .control-label, +.has-error .radio, +.has-error .checkbox, +.has-error .radio-inline, +.has-error .checkbox-inline, +.has-error.radio label, +.has-error.checkbox label, +.has-error.radio-inline label, +.has-error.checkbox-inline label { + color: #a94442; +} +.has-error .form-control { + border-color: #a94442; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075); +} +.has-error .form-control:focus { + border-color: #843534; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #ce8483; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px #ce8483; +} +.has-error .input-group-addon { + color: #a94442; + background-color: #f2dede; + border-color: #a94442; +} +.has-error .form-control-feedback { + color: #a94442; +} +.has-feedback label ~ .form-control-feedback { + top: 25px; +} +.has-feedback label.sr-only ~ .form-control-feedback { + top: 0; +} +.help-block { + display: block; + margin-top: 5px; + margin-bottom: 10px; + color: #737373; +} +@media (min-width: 768px) { + .form-inline .form-group { + display: inline-block; + margin-bottom: 0; + vertical-align: middle; + } + .form-inline .form-control { + display: inline-block; + width: auto; + vertical-align: middle; + } + .form-inline .form-control-static { + display: inline-block; + } + .form-inline .input-group { + display: inline-table; + vertical-align: middle; + } + .form-inline .input-group .input-group-addon, + .form-inline .input-group .input-group-btn, + .form-inline .input-group .form-control { + width: auto; + } + .form-inline .input-group > .form-control { + width: 100%; + } + .form-inline .control-label { + margin-bottom: 0; + vertical-align: middle; + } + .form-inline .radio, + .form-inline .checkbox { + display: inline-block; + margin-top: 0; + margin-bottom: 0; + vertical-align: middle; + } + .form-inline .radio label, + .form-inline .checkbox label { + padding-left: 0; + } + .form-inline .radio input[type="radio"], + .form-inline .checkbox input[type="checkbox"] { + position: relative; + margin-left: 0; + } + .form-inline .has-feedback .form-control-feedback { + top: 0; + } +} +.form-horizontal .radio, +.form-horizontal .checkbox, +.form-horizontal .radio-inline, +.form-horizontal .checkbox-inline { + padding-top: 7px; + margin-top: 0; + margin-bottom: 0; +} +.form-horizontal .radio, +.form-horizontal .checkbox { + min-height: 27px; +} +.form-horizontal .form-group { + margin-right: -15px; + margin-left: -15px; +} +@media (min-width: 768px) { + .form-horizontal .control-label { + padding-top: 7px; + margin-bottom: 0; + text-align: right; + } +} +.form-horizontal .has-feedback .form-control-feedback { + right: 15px; +} +@media (min-width: 768px) { + .form-horizontal .form-group-lg .control-label { + padding-top: 11px; + font-size: 18px; + } +} +@media (min-width: 768px) { + .form-horizontal .form-group-sm .control-label { + padding-top: 6px; + font-size: 12px; + } +} +.btn { + display: inline-block; + padding: 6px 12px; + margin-bottom: 0; + font-size: 14px; + font-weight: normal; + line-height: 1.42857143; + text-align: center; + white-space: nowrap; + vertical-align: middle; + -ms-touch-action: manipulation; + touch-action: manipulation; + cursor: pointer; + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; + background-image: none; + border: 1px solid transparent; + border-radius: 4px; +} +.btn:focus, +.btn:active:focus, +.btn.active:focus, +.btn.focus, +.btn:active.focus, +.btn.active.focus { + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} +.btn:hover, +.btn:focus, +.btn.focus { + color: #333; + text-decoration: none; +} +.btn:active, +.btn.active { + background-image: none; + outline: 0; + -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125); + box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125); +} +.btn.disabled, +.btn[disabled], +fieldset[disabled] .btn { + cursor: not-allowed; + filter: alpha(opacity=65); + -webkit-box-shadow: none; + box-shadow: none; + opacity: .65; +} +a.btn.disabled, +fieldset[disabled] a.btn { + pointer-events: none; +} +.btn-default { + color: #333; + background-color: #fff; + border-color: #ccc; +} +.btn-default:focus, +.btn-default.focus { + color: #333; + background-color: #e6e6e6; + border-color: #8c8c8c; +} +.btn-default:hover { + color: #333; + background-color: #e6e6e6; + border-color: #adadad; +} +.btn-default:active, +.btn-default.active, +.open > .dropdown-toggle.btn-default { + color: #333; + background-color: #e6e6e6; + border-color: #adadad; +} +.btn-default:active:hover, +.btn-default.active:hover, +.open > .dropdown-toggle.btn-default:hover, +.btn-default:active:focus, +.btn-default.active:focus, +.open > .dropdown-toggle.btn-default:focus, +.btn-default:active.focus, +.btn-default.active.focus, +.open > .dropdown-toggle.btn-default.focus { + color: #333; + background-color: #d4d4d4; + border-color: #8c8c8c; +} +.btn-default:active, +.btn-default.active, +.open > .dropdown-toggle.btn-default { + background-image: none; +} +.btn-default.disabled:hover, +.btn-default[disabled]:hover, +fieldset[disabled] .btn-default:hover, +.btn-default.disabled:focus, +.btn-default[disabled]:focus, +fieldset[disabled] .btn-default:focus, +.btn-default.disabled.focus, +.btn-default[disabled].focus, +fieldset[disabled] .btn-default.focus { + background-color: #fff; + border-color: #ccc; +} +.btn-default .badge { + color: #fff; + background-color: #333; +} +.btn-primary { + color: #fff; + background-color: #337ab7; + border-color: #2e6da4; +} +.btn-primary:focus, +.btn-primary.focus { + color: #fff; + background-color: #286090; + border-color: #122b40; +} +.btn-primary:hover { + color: #fff; + background-color: #286090; + border-color: #204d74; +} +.btn-primary:active, +.btn-primary.active, +.open > .dropdown-toggle.btn-primary { + color: #fff; + background-color: #286090; + border-color: #204d74; +} +.btn-primary:active:hover, +.btn-primary.active:hover, +.open > .dropdown-toggle.btn-primary:hover, +.btn-primary:active:focus, +.btn-primary.active:focus, +.open > .dropdown-toggle.btn-primary:focus, +.btn-primary:active.focus, +.btn-primary.active.focus, +.open > .dropdown-toggle.btn-primary.focus { + color: #fff; + background-color: #204d74; + border-color: #122b40; +} +.btn-primary:active, +.btn-primary.active, +.open > .dropdown-toggle.btn-primary { + background-image: none; +} +.btn-primary.disabled:hover, +.btn-primary[disabled]:hover, +fieldset[disabled] .btn-primary:hover, +.btn-primary.disabled:focus, +.btn-primary[disabled]:focus, +fieldset[disabled] .btn-primary:focus, +.btn-primary.disabled.focus, +.btn-primary[disabled].focus, +fieldset[disabled] .btn-primary.focus { + background-color: #337ab7; + border-color: #2e6da4; +} +.btn-primary .badge { + color: #337ab7; + background-color: #fff; +} +.btn-success { + color: #fff; + background-color: #5cb85c; + border-color: #4cae4c; +} +.btn-success:focus, +.btn-success.focus { + color: #fff; + background-color: #449d44; + border-color: #255625; +} +.btn-success:hover { + color: #fff; + background-color: #449d44; + border-color: #398439; +} +.btn-success:active, +.btn-success.active, +.open > .dropdown-toggle.btn-success { + color: #fff; + background-color: #449d44; + border-color: #398439; +} +.btn-success:active:hover, +.btn-success.active:hover, +.open > .dropdown-toggle.btn-success:hover, +.btn-success:active:focus, +.btn-success.active:focus, +.open > .dropdown-toggle.btn-success:focus, +.btn-success:active.focus, +.btn-success.active.focus, +.open > .dropdown-toggle.btn-success.focus { + color: #fff; + background-color: #398439; + border-color: #255625; +} +.btn-success:active, +.btn-success.active, +.open > .dropdown-toggle.btn-success { + background-image: none; +} +.btn-success.disabled:hover, +.btn-success[disabled]:hover, +fieldset[disabled] .btn-success:hover, +.btn-success.disabled:focus, +.btn-success[disabled]:focus, +fieldset[disabled] .btn-success:focus, +.btn-success.disabled.focus, +.btn-success[disabled].focus, +fieldset[disabled] .btn-success.focus { + background-color: #5cb85c; + border-color: #4cae4c; +} +.btn-success .badge { + color: #5cb85c; + background-color: #fff; +} +.btn-info { + color: #fff; + background-color: #5bc0de; + border-color: #46b8da; +} +.btn-info:focus, +.btn-info.focus { + color: #fff; + background-color: #31b0d5; + border-color: #1b6d85; +} +.btn-info:hover { + color: #fff; + background-color: #31b0d5; + border-color: #269abc; +} +.btn-info:active, +.btn-info.active, +.open > .dropdown-toggle.btn-info { + color: #fff; + background-color: #31b0d5; + border-color: #269abc; +} +.btn-info:active:hover, +.btn-info.active:hover, +.open > .dropdown-toggle.btn-info:hover, +.btn-info:active:focus, +.btn-info.active:focus, +.open > .dropdown-toggle.btn-info:focus, +.btn-info:active.focus, +.btn-info.active.focus, +.open > .dropdown-toggle.btn-info.focus { + color: #fff; + background-color: #269abc; + border-color: #1b6d85; +} +.btn-info:active, +.btn-info.active, +.open > .dropdown-toggle.btn-info { + background-image: none; +} +.btn-info.disabled:hover, +.btn-info[disabled]:hover, +fieldset[disabled] .btn-info:hover, +.btn-info.disabled:focus, +.btn-info[disabled]:focus, +fieldset[disabled] .btn-info:focus, +.btn-info.disabled.focus, +.btn-info[disabled].focus, +fieldset[disabled] .btn-info.focus { + background-color: #5bc0de; + border-color: #46b8da; +} +.btn-info .badge { + color: #5bc0de; + background-color: #fff; +} +.btn-warning { + color: #fff; + background-color: #f0ad4e; + border-color: #eea236; +} +.btn-warning:focus, +.btn-warning.focus { + color: #fff; + background-color: #ec971f; + border-color: #985f0d; +} +.btn-warning:hover { + color: #fff; + background-color: #ec971f; + border-color: #d58512; +} +.btn-warning:active, +.btn-warning.active, +.open > .dropdown-toggle.btn-warning { + color: #fff; + background-color: #ec971f; + border-color: #d58512; +} +.btn-warning:active:hover, +.btn-warning.active:hover, +.open > .dropdown-toggle.btn-warning:hover, +.btn-warning:active:focus, +.btn-warning.active:focus, +.open > .dropdown-toggle.btn-warning:focus, +.btn-warning:active.focus, +.btn-warning.active.focus, +.open > .dropdown-toggle.btn-warning.focus { + color: #fff; + background-color: #d58512; + border-color: #985f0d; +} +.btn-warning:active, +.btn-warning.active, +.open > .dropdown-toggle.btn-warning { + background-image: none; +} +.btn-warning.disabled:hover, +.btn-warning[disabled]:hover, +fieldset[disabled] .btn-warning:hover, +.btn-warning.disabled:focus, +.btn-warning[disabled]:focus, +fieldset[disabled] .btn-warning:focus, +.btn-warning.disabled.focus, +.btn-warning[disabled].focus, +fieldset[disabled] .btn-warning.focus { + background-color: #f0ad4e; + border-color: #eea236; +} +.btn-warning .badge { + color: #f0ad4e; + background-color: #fff; +} +.btn-danger { + color: #fff; + background-color: #d9534f; + border-color: #d43f3a; +} +.btn-danger:focus, +.btn-danger.focus { + color: #fff; + background-color: #c9302c; + border-color: #761c19; +} +.btn-danger:hover { + color: #fff; + background-color: #c9302c; + border-color: #ac2925; +} +.btn-danger:active, +.btn-danger.active, +.open > .dropdown-toggle.btn-danger { + color: #fff; + background-color: #c9302c; + border-color: #ac2925; +} +.btn-danger:active:hover, +.btn-danger.active:hover, +.open > .dropdown-toggle.btn-danger:hover, +.btn-danger:active:focus, +.btn-danger.active:focus, +.open > .dropdown-toggle.btn-danger:focus, +.btn-danger:active.focus, +.btn-danger.active.focus, +.open > .dropdown-toggle.btn-danger.focus { + color: #fff; + background-color: #ac2925; + border-color: #761c19; +} +.btn-danger:active, +.btn-danger.active, +.open > .dropdown-toggle.btn-danger { + background-image: none; +} +.btn-danger.disabled:hover, +.btn-danger[disabled]:hover, +fieldset[disabled] .btn-danger:hover, +.btn-danger.disabled:focus, +.btn-danger[disabled]:focus, +fieldset[disabled] .btn-danger:focus, +.btn-danger.disabled.focus, +.btn-danger[disabled].focus, +fieldset[disabled] .btn-danger.focus { + background-color: #d9534f; + border-color: #d43f3a; +} +.btn-danger .badge { + color: #d9534f; + background-color: #fff; +} +.btn-link { + font-weight: normal; + color: #337ab7; + border-radius: 0; +} +.btn-link, +.btn-link:active, +.btn-link.active, +.btn-link[disabled], +fieldset[disabled] .btn-link { + background-color: transparent; + -webkit-box-shadow: none; + box-shadow: none; +} +.btn-link, +.btn-link:hover, +.btn-link:focus, +.btn-link:active { + border-color: transparent; +} +.btn-link:hover, +.btn-link:focus { + color: #23527c; + text-decoration: underline; + background-color: transparent; +} +.btn-link[disabled]:hover, +fieldset[disabled] .btn-link:hover, +.btn-link[disabled]:focus, +fieldset[disabled] .btn-link:focus { + color: #777; + text-decoration: none; +} +.btn-lg, +.btn-group-lg > .btn { + padding: 10px 16px; + font-size: 18px; + line-height: 1.3333333; + border-radius: 6px; +} +.btn-sm, +.btn-group-sm > .btn { + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 3px; +} +.btn-xs, +.btn-group-xs > .btn { + padding: 1px 5px; + font-size: 12px; + line-height: 1.5; + border-radius: 3px; +} +.btn-block { + display: block; + width: 100%; +} +.btn-block + .btn-block { + margin-top: 5px; +} +input[type="submit"].btn-block, +input[type="reset"].btn-block, +input[type="button"].btn-block { + width: 100%; +} +.fade { + opacity: 0; + -webkit-transition: opacity .15s linear; + -o-transition: opacity .15s linear; + transition: opacity .15s linear; +} +.fade.in { + opacity: 1; +} +.collapse { + display: none; +} +.collapse.in { + display: block; +} +tr.collapse.in { + display: table-row; +} +tbody.collapse.in { + display: table-row-group; +} +.collapsing { + position: relative; + height: 0; + overflow: hidden; + -webkit-transition-timing-function: ease; + -o-transition-timing-function: ease; + transition-timing-function: ease; + -webkit-transition-duration: .35s; + -o-transition-duration: .35s; + transition-duration: .35s; + -webkit-transition-property: height, visibility; + -o-transition-property: height, visibility; + transition-property: height, visibility; +} +.caret { + display: inline-block; + width: 0; + height: 0; + margin-left: 2px; + vertical-align: middle; + border-top: 4px dashed; + border-top: 4px solid \9; + border-right: 4px solid transparent; + border-left: 4px solid transparent; +} +.dropup, +.dropdown { + position: relative; +} +.dropdown-toggle:focus { + outline: 0; +} +.dropdown-menu { + position: absolute; + top: 100%; + left: 0; + z-index: 1000; + display: none; + float: left; + min-width: 160px; + padding: 5px 0; + margin: 2px 0 0; + font-size: 14px; + text-align: left; + list-style: none; + background-color: #fff; + -webkit-background-clip: padding-box; + background-clip: padding-box; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, .15); + border-radius: 4px; + -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, .175); + box-shadow: 0 6px 12px rgba(0, 0, 0, .175); +} +.dropdown-menu.pull-right { + right: 0; + left: auto; +} +.dropdown-menu .divider { + height: 1px; + margin: 9px 0; + overflow: hidden; + background-color: #e5e5e5; +} +.dropdown-menu > li > a { + display: block; + padding: 3px 20px; + clear: both; + font-weight: normal; + line-height: 1.42857143; + color: #333; + white-space: nowrap; +} +.dropdown-menu > li > a:hover, +.dropdown-menu > li > a:focus { + color: #262626; + text-decoration: none; + background-color: #f5f5f5; +} +.dropdown-menu > .active > a, +.dropdown-menu > .active > a:hover, +.dropdown-menu > .active > a:focus { + color: #fff; + text-decoration: none; + background-color: #337ab7; + outline: 0; +} +.dropdown-menu > .disabled > a, +.dropdown-menu > .disabled > a:hover, +.dropdown-menu > .disabled > a:focus { + color: #777; +} +.dropdown-menu > .disabled > a:hover, +.dropdown-menu > .disabled > a:focus { + text-decoration: none; + cursor: not-allowed; + background-color: transparent; + background-image: none; + filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); +} +.open > .dropdown-menu { + display: block; +} +.open > a { + outline: 0; +} +.dropdown-menu-right { + right: 0; + left: auto; +} +.dropdown-menu-left { + right: auto; + left: 0; +} +.dropdown-header { + display: block; + padding: 3px 20px; + font-size: 12px; + line-height: 1.42857143; + color: #777; + white-space: nowrap; +} +.dropdown-backdrop { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 990; +} +.pull-right > .dropdown-menu { + right: 0; + left: auto; +} +.dropup .caret, +.navbar-fixed-bottom .dropdown .caret { + content: ""; + border-top: 0; + border-bottom: 4px dashed; + border-bottom: 4px solid \9; +} +.dropup .dropdown-menu, +.navbar-fixed-bottom .dropdown .dropdown-menu { + top: auto; + bottom: 100%; + margin-bottom: 2px; +} +@media (min-width: 768px) { + .navbar-right .dropdown-menu { + right: 0; + left: auto; + } + .navbar-right .dropdown-menu-left { + right: auto; + left: 0; + } +} +.btn-group, +.btn-group-vertical { + position: relative; + display: inline-block; + vertical-align: middle; +} +.btn-group > .btn, +.btn-group-vertical > .btn { + position: relative; + float: left; +} +.btn-group > .btn:hover, +.btn-group-vertical > .btn:hover, +.btn-group > .btn:focus, +.btn-group-vertical > .btn:focus, +.btn-group > .btn:active, +.btn-group-vertical > .btn:active, +.btn-group > .btn.active, +.btn-group-vertical > .btn.active { + z-index: 2; +} +.btn-group .btn + .btn, +.btn-group .btn + .btn-group, +.btn-group .btn-group + .btn, +.btn-group .btn-group + .btn-group { + margin-left: -1px; +} +.btn-toolbar { + margin-left: -5px; +} +.btn-toolbar .btn, +.btn-toolbar .btn-group, +.btn-toolbar .input-group { + float: left; +} +.btn-toolbar > .btn, +.btn-toolbar > .btn-group, +.btn-toolbar > .input-group { + margin-left: 5px; +} +.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) { + border-radius: 0; +} +.btn-group > .btn:first-child { + margin-left: 0; +} +.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.btn-group > .btn:last-child:not(:first-child), +.btn-group > .dropdown-toggle:not(:first-child) { + border-top-left-radius: 0; + border-bottom-left-radius: 0; +} +.btn-group > .btn-group { + float: left; +} +.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn { + border-radius: 0; +} +.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child, +.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child { + border-top-left-radius: 0; + border-bottom-left-radius: 0; +} +.btn-group .dropdown-toggle:active, +.btn-group.open .dropdown-toggle { + outline: 0; +} +.btn-group > .btn + .dropdown-toggle { + padding-right: 8px; + padding-left: 8px; +} +.btn-group > .btn-lg + .dropdown-toggle { + padding-right: 12px; + padding-left: 12px; +} +.btn-group.open .dropdown-toggle { + -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125); + box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125); +} +.btn-group.open .dropdown-toggle.btn-link { + -webkit-box-shadow: none; + box-shadow: none; +} +.btn .caret { + margin-left: 0; +} +.btn-lg .caret { + border-width: 5px 5px 0; + border-bottom-width: 0; +} +.dropup .btn-lg .caret { + border-width: 0 5px 5px; +} +.btn-group-vertical > .btn, +.btn-group-vertical > .btn-group, +.btn-group-vertical > .btn-group > .btn { + display: block; + float: none; + width: 100%; + max-width: 100%; +} +.btn-group-vertical > .btn-group > .btn { + float: none; +} +.btn-group-vertical > .btn + .btn, +.btn-group-vertical > .btn + .btn-group, +.btn-group-vertical > .btn-group + .btn, +.btn-group-vertical > .btn-group + .btn-group { + margin-top: -1px; + margin-left: 0; +} +.btn-group-vertical > .btn:not(:first-child):not(:last-child) { + border-radius: 0; +} +.btn-group-vertical > .btn:first-child:not(:last-child) { + border-top-left-radius: 4px; + border-top-right-radius: 4px; + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.btn-group-vertical > .btn:last-child:not(:first-child) { + border-top-left-radius: 0; + border-top-right-radius: 0; + border-bottom-right-radius: 4px; + border-bottom-left-radius: 4px; +} +.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn { + border-radius: 0; +} +.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child, +.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle { + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child { + border-top-left-radius: 0; + border-top-right-radius: 0; +} +.btn-group-justified { + display: table; + width: 100%; + table-layout: fixed; + border-collapse: separate; +} +.btn-group-justified > .btn, +.btn-group-justified > .btn-group { + display: table-cell; + float: none; + width: 1%; +} +.btn-group-justified > .btn-group .btn { + width: 100%; +} +.btn-group-justified > .btn-group .dropdown-menu { + left: auto; +} +[data-toggle="buttons"] > .btn input[type="radio"], +[data-toggle="buttons"] > .btn-group > .btn input[type="radio"], +[data-toggle="buttons"] > .btn input[type="checkbox"], +[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] { + position: absolute; + clip: rect(0, 0, 0, 0); + pointer-events: none; +} +.input-group { + position: relative; + display: table; + border-collapse: separate; +} +.input-group[class*="col-"] { + float: none; + padding-right: 0; + padding-left: 0; +} +.input-group .form-control { + position: relative; + z-index: 2; + float: left; + width: 100%; + margin-bottom: 0; +} +.input-group .form-control:focus { + z-index: 3; +} +.input-group-lg > .form-control, +.input-group-lg > .input-group-addon, +.input-group-lg > .input-group-btn > .btn { + height: 46px; + padding: 10px 16px; + font-size: 18px; + line-height: 1.3333333; + border-radius: 6px; +} +select.input-group-lg > .form-control, +select.input-group-lg > .input-group-addon, +select.input-group-lg > .input-group-btn > .btn { + height: 46px; + line-height: 46px; +} +textarea.input-group-lg > .form-control, +textarea.input-group-lg > .input-group-addon, +textarea.input-group-lg > .input-group-btn > .btn, +select[multiple].input-group-lg > .form-control, +select[multiple].input-group-lg > .input-group-addon, +select[multiple].input-group-lg > .input-group-btn > .btn { + height: auto; +} +.input-group-sm > .form-control, +.input-group-sm > .input-group-addon, +.input-group-sm > .input-group-btn > .btn { + height: 30px; + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 3px; +} +select.input-group-sm > .form-control, +select.input-group-sm > .input-group-addon, +select.input-group-sm > .input-group-btn > .btn { + height: 30px; + line-height: 30px; +} +textarea.input-group-sm > .form-control, +textarea.input-group-sm > .input-group-addon, +textarea.input-group-sm > .input-group-btn > .btn, +select[multiple].input-group-sm > .form-control, +select[multiple].input-group-sm > .input-group-addon, +select[multiple].input-group-sm > .input-group-btn > .btn { + height: auto; +} +.input-group-addon, +.input-group-btn, +.input-group .form-control { + display: table-cell; +} +.input-group-addon:not(:first-child):not(:last-child), +.input-group-btn:not(:first-child):not(:last-child), +.input-group .form-control:not(:first-child):not(:last-child) { + border-radius: 0; +} +.input-group-addon, +.input-group-btn { + width: 1%; + white-space: nowrap; + vertical-align: middle; +} +.input-group-addon { + padding: 6px 12px; + font-size: 14px; + font-weight: normal; + line-height: 1; + color: #555; + text-align: center; + background-color: #eee; + border: 1px solid #ccc; + border-radius: 4px; +} +.input-group-addon.input-sm { + padding: 5px 10px; + font-size: 12px; + border-radius: 3px; +} +.input-group-addon.input-lg { + padding: 10px 16px; + font-size: 18px; + border-radius: 6px; +} +.input-group-addon input[type="radio"], +.input-group-addon input[type="checkbox"] { + margin-top: 0; +} +.input-group .form-control:first-child, +.input-group-addon:first-child, +.input-group-btn:first-child > .btn, +.input-group-btn:first-child > .btn-group > .btn, +.input-group-btn:first-child > .dropdown-toggle, +.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle), +.input-group-btn:last-child > .btn-group:not(:last-child) > .btn { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.input-group-addon:first-child { + border-right: 0; +} +.input-group .form-control:last-child, +.input-group-addon:last-child, +.input-group-btn:last-child > .btn, +.input-group-btn:last-child > .btn-group > .btn, +.input-group-btn:last-child > .dropdown-toggle, +.input-group-btn:first-child > .btn:not(:first-child), +.input-group-btn:first-child > .btn-group:not(:first-child) > .btn { + border-top-left-radius: 0; + border-bottom-left-radius: 0; +} +.input-group-addon:last-child { + border-left: 0; +} +.input-group-btn { + position: relative; + font-size: 0; + white-space: nowrap; +} +.input-group-btn > .btn { + position: relative; +} +.input-group-btn > .btn + .btn { + margin-left: -1px; +} +.input-group-btn > .btn:hover, +.input-group-btn > .btn:focus, +.input-group-btn > .btn:active { + z-index: 2; +} +.input-group-btn:first-child > .btn, +.input-group-btn:first-child > .btn-group { + margin-right: -1px; +} +.input-group-btn:last-child > .btn, +.input-group-btn:last-child > .btn-group { + z-index: 2; + margin-left: -1px; +} +.nav { + padding-left: 0; + margin-bottom: 0; + list-style: none; +} +.nav > li { + position: relative; + display: block; +} +.nav > li > a { + position: relative; + display: block; + padding: 10px 15px; +} +.nav > li > a:hover, +.nav > li > a:focus { + text-decoration: none; + background-color: #eee; +} +.nav > li.disabled > a { + color: #777; +} +.nav > li.disabled > a:hover, +.nav > li.disabled > a:focus { + color: #777; + text-decoration: none; + cursor: not-allowed; + background-color: transparent; +} +.nav .open > a, +.nav .open > a:hover, +.nav .open > a:focus { + background-color: #eee; + border-color: #337ab7; +} +.nav .nav-divider { + height: 1px; + margin: 9px 0; + overflow: hidden; + background-color: #e5e5e5; +} +.nav > li > a > img { + max-width: none; +} +.nav-tabs { + border-bottom: 1px solid #ddd; +} +.nav-tabs > li { + float: left; + margin-bottom: -1px; +} +.nav-tabs > li > a { + margin-right: 2px; + line-height: 1.42857143; + border: 1px solid transparent; + border-radius: 4px 4px 0 0; +} +.nav-tabs > li > a:hover { + border-color: #eee #eee #ddd; +} +.nav-tabs > li.active > a, +.nav-tabs > li.active > a:hover, +.nav-tabs > li.active > a:focus { + color: #555; + cursor: default; + background-color: #fff; + border: 1px solid #ddd; + border-bottom-color: transparent; +} +.nav-tabs.nav-justified { + width: 100%; + border-bottom: 0; +} +.nav-tabs.nav-justified > li { + float: none; +} +.nav-tabs.nav-justified > li > a { + margin-bottom: 5px; + text-align: center; +} +.nav-tabs.nav-justified > .dropdown .dropdown-menu { + top: auto; + left: auto; +} +@media (min-width: 768px) { + .nav-tabs.nav-justified > li { + display: table-cell; + width: 1%; + } + .nav-tabs.nav-justified > li > a { + margin-bottom: 0; + } +} +.nav-tabs.nav-justified > li > a { + margin-right: 0; + border-radius: 4px; +} +.nav-tabs.nav-justified > .active > a, +.nav-tabs.nav-justified > .active > a:hover, +.nav-tabs.nav-justified > .active > a:focus { + border: 1px solid #ddd; +} +@media (min-width: 768px) { + .nav-tabs.nav-justified > li > a { + border-bottom: 1px solid #ddd; + border-radius: 4px 4px 0 0; + } + .nav-tabs.nav-justified > .active > a, + .nav-tabs.nav-justified > .active > a:hover, + .nav-tabs.nav-justified > .active > a:focus { + border-bottom-color: #fff; + } +} +.nav-pills > li { + float: left; +} +.nav-pills > li > a { + border-radius: 4px; +} +.nav-pills > li + li { + margin-left: 2px; +} +.nav-pills > li.active > a, +.nav-pills > li.active > a:hover, +.nav-pills > li.active > a:focus { + color: #fff; + background-color: #337ab7; +} +.nav-stacked > li { + float: none; +} +.nav-stacked > li + li { + margin-top: 2px; + margin-left: 0; +} +.nav-justified { + width: 100%; +} +.nav-justified > li { + float: none; +} +.nav-justified > li > a { + margin-bottom: 5px; + text-align: center; +} +.nav-justified > .dropdown .dropdown-menu { + top: auto; + left: auto; +} +@media (min-width: 768px) { + .nav-justified > li { + display: table-cell; + width: 1%; + } + .nav-justified > li > a { + margin-bottom: 0; + } +} +.nav-tabs-justified { + border-bottom: 0; +} +.nav-tabs-justified > li > a { + margin-right: 0; + border-radius: 4px; +} +.nav-tabs-justified > .active > a, +.nav-tabs-justified > .active > a:hover, +.nav-tabs-justified > .active > a:focus { + border: 1px solid #ddd; +} +@media (min-width: 768px) { + .nav-tabs-justified > li > a { + border-bottom: 1px solid #ddd; + border-radius: 4px 4px 0 0; + } + .nav-tabs-justified > .active > a, + .nav-tabs-justified > .active > a:hover, + .nav-tabs-justified > .active > a:focus { + border-bottom-color: #fff; + } +} +.tab-content > .tab-pane { + display: none; +} +.tab-content > .active { + display: block; +} +.nav-tabs .dropdown-menu { + margin-top: -1px; + border-top-left-radius: 0; + border-top-right-radius: 0; +} +.navbar { + position: relative; + min-height: 50px; + margin-bottom: 20px; + border: 1px solid transparent; +} +@media (min-width: 768px) { + .navbar { + border-radius: 4px; + } +} +@media (min-width: 768px) { + .navbar-header { + float: left; + } +} +.navbar-collapse { + padding-right: 15px; + padding-left: 15px; + overflow-x: visible; + -webkit-overflow-scrolling: touch; + border-top: 1px solid transparent; + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1); +} +.navbar-collapse.in { + overflow-y: auto; +} +@media (min-width: 768px) { + .navbar-collapse { + width: auto; + border-top: 0; + -webkit-box-shadow: none; + box-shadow: none; + } + .navbar-collapse.collapse { + display: block !important; + height: auto !important; + padding-bottom: 0; + overflow: visible !important; + } + .navbar-collapse.in { + overflow-y: visible; + } + .navbar-fixed-top .navbar-collapse, + .navbar-static-top .navbar-collapse, + .navbar-fixed-bottom .navbar-collapse { + padding-right: 0; + padding-left: 0; + } +} +.navbar-fixed-top .navbar-collapse, +.navbar-fixed-bottom .navbar-collapse { + max-height: 340px; +} +@media (max-device-width: 480px) and (orientation: landscape) { + .navbar-fixed-top .navbar-collapse, + .navbar-fixed-bottom .navbar-collapse { + max-height: 200px; + } +} +.container > .navbar-header, +.container-fluid > .navbar-header, +.container > .navbar-collapse, +.container-fluid > .navbar-collapse { + margin-right: -15px; + margin-left: -15px; +} +@media (min-width: 768px) { + .container > .navbar-header, + .container-fluid > .navbar-header, + .container > .navbar-collapse, + .container-fluid > .navbar-collapse { + margin-right: 0; + margin-left: 0; + } +} +.navbar-static-top { + z-index: 1000; + border-width: 0 0 1px; +} +@media (min-width: 768px) { + .navbar-static-top { + border-radius: 0; + } +} +.navbar-fixed-top, +.navbar-fixed-bottom { + position: fixed; + right: 0; + left: 0; + z-index: 1030; +} +@media (min-width: 768px) { + .navbar-fixed-top, + .navbar-fixed-bottom { + border-radius: 0; + } +} +.navbar-fixed-top { + top: 0; + border-width: 0 0 1px; +} +.navbar-fixed-bottom { + bottom: 0; + margin-bottom: 0; + border-width: 1px 0 0; +} +.navbar-brand { + float: left; + height: 50px; + padding: 15px 15px; + font-size: 18px; + line-height: 20px; +} +.navbar-brand:hover, +.navbar-brand:focus { + text-decoration: none; +} +.navbar-brand > img { + display: block; +} +@media (min-width: 768px) { + .navbar > .container .navbar-brand, + .navbar > .container-fluid .navbar-brand { + margin-left: -15px; + } +} +.navbar-toggle { + position: relative; + float: right; + padding: 9px 10px; + margin-top: 8px; + margin-right: 15px; + margin-bottom: 8px; + background-color: transparent; + background-image: none; + border: 1px solid transparent; + border-radius: 4px; +} +.navbar-toggle:focus { + outline: 0; +} +.navbar-toggle .icon-bar { + display: block; + width: 22px; + height: 2px; + border-radius: 1px; +} +.navbar-toggle .icon-bar + .icon-bar { + margin-top: 4px; +} +@media (min-width: 768px) { + .navbar-toggle { + display: none; + } +} +.navbar-nav { + margin: 7.5px -15px; +} +.navbar-nav > li > a { + padding-top: 10px; + padding-bottom: 10px; + line-height: 20px; +} +@media (max-width: 767px) { + .navbar-nav .open .dropdown-menu { + position: static; + float: none; + width: auto; + margin-top: 0; + background-color: transparent; + border: 0; + -webkit-box-shadow: none; + box-shadow: none; + } + .navbar-nav .open .dropdown-menu > li > a, + .navbar-nav .open .dropdown-menu .dropdown-header { + padding: 5px 15px 5px 25px; + } + .navbar-nav .open .dropdown-menu > li > a { + line-height: 20px; + } + .navbar-nav .open .dropdown-menu > li > a:hover, + .navbar-nav .open .dropdown-menu > li > a:focus { + background-image: none; + } +} +@media (min-width: 768px) { + .navbar-nav { + float: left; + margin: 0; + } + .navbar-nav > li { + float: left; + } + .navbar-nav > li > a { + padding-top: 15px; + padding-bottom: 15px; + } +} +.navbar-form { + padding: 10px 15px; + margin-top: 8px; + margin-right: -15px; + margin-bottom: 8px; + margin-left: -15px; + border-top: 1px solid transparent; + border-bottom: 1px solid transparent; + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1), 0 1px 0 rgba(255, 255, 255, .1); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1), 0 1px 0 rgba(255, 255, 255, .1); +} +@media (min-width: 768px) { + .navbar-form .form-group { + display: inline-block; + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .form-control { + display: inline-block; + width: auto; + vertical-align: middle; + } + .navbar-form .form-control-static { + display: inline-block; + } + .navbar-form .input-group { + display: inline-table; + vertical-align: middle; + } + .navbar-form .input-group .input-group-addon, + .navbar-form .input-group .input-group-btn, + .navbar-form .input-group .form-control { + width: auto; + } + .navbar-form .input-group > .form-control { + width: 100%; + } + .navbar-form .control-label { + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .radio, + .navbar-form .checkbox { + display: inline-block; + margin-top: 0; + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .radio label, + .navbar-form .checkbox label { + padding-left: 0; + } + .navbar-form .radio input[type="radio"], + .navbar-form .checkbox input[type="checkbox"] { + position: relative; + margin-left: 0; + } + .navbar-form .has-feedback .form-control-feedback { + top: 0; + } +} +@media (max-width: 767px) { + .navbar-form .form-group { + margin-bottom: 5px; + } + .navbar-form .form-group:last-child { + margin-bottom: 0; + } +} +@media (min-width: 768px) { + .navbar-form { + width: auto; + padding-top: 0; + padding-bottom: 0; + margin-right: 0; + margin-left: 0; + border: 0; + -webkit-box-shadow: none; + box-shadow: none; + } +} +.navbar-nav > li > .dropdown-menu { + margin-top: 0; + border-top-left-radius: 0; + border-top-right-radius: 0; +} +.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu { + margin-bottom: 0; + border-top-left-radius: 4px; + border-top-right-radius: 4px; + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.navbar-btn { + margin-top: 8px; + margin-bottom: 8px; +} +.navbar-btn.btn-sm { + margin-top: 10px; + margin-bottom: 10px; +} +.navbar-btn.btn-xs { + margin-top: 14px; + margin-bottom: 14px; +} +.navbar-text { + margin-top: 15px; + margin-bottom: 15px; +} +@media (min-width: 768px) { + .navbar-text { + float: left; + margin-right: 15px; + margin-left: 15px; + } +} +@media (min-width: 768px) { + .navbar-left { + float: left !important; + } + .navbar-right { + float: right !important; + margin-right: -15px; + } + .navbar-right ~ .navbar-right { + margin-right: 0; + } +} +.navbar-default { + background-color: #f8f8f8; + border-color: #e7e7e7; +} +.navbar-default .navbar-brand { + color: #777; +} +.navbar-default .navbar-brand:hover, +.navbar-default .navbar-brand:focus { + color: #5e5e5e; + background-color: transparent; +} +.navbar-default .navbar-text { + color: #777; +} +.navbar-default .navbar-nav > li > a { + color: #777; +} +.navbar-default .navbar-nav > li > a:hover, +.navbar-default .navbar-nav > li > a:focus { + color: #333; + background-color: transparent; +} +.navbar-default .navbar-nav > .active > a, +.navbar-default .navbar-nav > .active > a:hover, +.navbar-default .navbar-nav > .active > a:focus { + color: #555; + background-color: #e7e7e7; +} +.navbar-default .navbar-nav > .disabled > a, +.navbar-default .navbar-nav > .disabled > a:hover, +.navbar-default .navbar-nav > .disabled > a:focus { + color: #ccc; + background-color: transparent; +} +.navbar-default .navbar-toggle { + border-color: #ddd; +} +.navbar-default .navbar-toggle:hover, +.navbar-default .navbar-toggle:focus { + background-color: #ddd; +} +.navbar-default .navbar-toggle .icon-bar { + background-color: #888; +} +.navbar-default .navbar-collapse, +.navbar-default .navbar-form { + border-color: #e7e7e7; +} +.navbar-default .navbar-nav > .open > a, +.navbar-default .navbar-nav > .open > a:hover, +.navbar-default .navbar-nav > .open > a:focus { + color: #555; + background-color: #e7e7e7; +} +@media (max-width: 767px) { + .navbar-default .navbar-nav .open .dropdown-menu > li > a { + color: #777; + } + .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover, + .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus { + color: #333; + background-color: transparent; + } + .navbar-default .navbar-nav .open .dropdown-menu > .active > a, + .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover, + .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus { + color: #555; + background-color: #e7e7e7; + } + .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a, + .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover, + .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus { + color: #ccc; + background-color: transparent; + } +} +.navbar-default .navbar-link { + color: #777; +} +.navbar-default .navbar-link:hover { + color: #333; +} +.navbar-default .btn-link { + color: #777; +} +.navbar-default .btn-link:hover, +.navbar-default .btn-link:focus { + color: #333; +} +.navbar-default .btn-link[disabled]:hover, +fieldset[disabled] .navbar-default .btn-link:hover, +.navbar-default .btn-link[disabled]:focus, +fieldset[disabled] .navbar-default .btn-link:focus { + color: #ccc; +} +.navbar-inverse { + background-color: #222; + border-color: #080808; +} +.navbar-inverse .navbar-brand { + color: #9d9d9d; +} +.navbar-inverse .navbar-brand:hover, +.navbar-inverse .navbar-brand:focus { + color: #fff; + background-color: transparent; +} +.navbar-inverse .navbar-text { + color: #9d9d9d; +} +.navbar-inverse .navbar-nav > li > a { + color: #9d9d9d; +} +.navbar-inverse .navbar-nav > li > a:hover, +.navbar-inverse .navbar-nav > li > a:focus { + color: #fff; + background-color: transparent; +} +.navbar-inverse .navbar-nav > .active > a, +.navbar-inverse .navbar-nav > .active > a:hover, +.navbar-inverse .navbar-nav > .active > a:focus { + color: #fff; + background-color: #080808; +} +.navbar-inverse .navbar-nav > .disabled > a, +.navbar-inverse .navbar-nav > .disabled > a:hover, +.navbar-inverse .navbar-nav > .disabled > a:focus { + color: #444; + background-color: transparent; +} +.navbar-inverse .navbar-toggle { + border-color: #333; +} +.navbar-inverse .navbar-toggle:hover, +.navbar-inverse .navbar-toggle:focus { + background-color: #333; +} +.navbar-inverse .navbar-toggle .icon-bar { + background-color: #fff; +} +.navbar-inverse .navbar-collapse, +.navbar-inverse .navbar-form { + border-color: #101010; +} +.navbar-inverse .navbar-nav > .open > a, +.navbar-inverse .navbar-nav > .open > a:hover, +.navbar-inverse .navbar-nav > .open > a:focus { + color: #fff; + background-color: #080808; +} +@media (max-width: 767px) { + .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header { + border-color: #080808; + } + .navbar-inverse .navbar-nav .open .dropdown-menu .divider { + background-color: #080808; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > li > a { + color: #9d9d9d; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover, + .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus { + color: #fff; + background-color: transparent; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a, + .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover, + .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus { + color: #fff; + background-color: #080808; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a, + .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover, + .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus { + color: #444; + background-color: transparent; + } +} +.navbar-inverse .navbar-link { + color: #9d9d9d; +} +.navbar-inverse .navbar-link:hover { + color: #fff; +} +.navbar-inverse .btn-link { + color: #9d9d9d; +} +.navbar-inverse .btn-link:hover, +.navbar-inverse .btn-link:focus { + color: #fff; +} +.navbar-inverse .btn-link[disabled]:hover, +fieldset[disabled] .navbar-inverse .btn-link:hover, +.navbar-inverse .btn-link[disabled]:focus, +fieldset[disabled] .navbar-inverse .btn-link:focus { + color: #444; +} +.breadcrumb { + padding: 8px 15px; + margin-bottom: 20px; + list-style: none; + background-color: #f5f5f5; + border-radius: 4px; +} +.breadcrumb > li { + display: inline-block; +} +.breadcrumb > li + li:before { + padding: 0 5px; + color: #ccc; + content: "/\00a0"; +} +.breadcrumb > .active { + color: #777; +} +.pagination { + display: inline-block; + padding-left: 0; + margin: 20px 0; + border-radius: 4px; +} +.pagination > li { + display: inline; +} +.pagination > li > a, +.pagination > li > span { + position: relative; + float: left; + padding: 6px 12px; + margin-left: -1px; + line-height: 1.42857143; + color: #337ab7; + text-decoration: none; + background-color: #fff; + border: 1px solid #ddd; +} +.pagination > li:first-child > a, +.pagination > li:first-child > span { + margin-left: 0; + border-top-left-radius: 4px; + border-bottom-left-radius: 4px; +} +.pagination > li:last-child > a, +.pagination > li:last-child > span { + border-top-right-radius: 4px; + border-bottom-right-radius: 4px; +} +.pagination > li > a:hover, +.pagination > li > span:hover, +.pagination > li > a:focus, +.pagination > li > span:focus { + z-index: 2; + color: #23527c; + background-color: #eee; + border-color: #ddd; +} +.pagination > .active > a, +.pagination > .active > span, +.pagination > .active > a:hover, +.pagination > .active > span:hover, +.pagination > .active > a:focus, +.pagination > .active > span:focus { + z-index: 3; + color: #fff; + cursor: default; + background-color: #337ab7; + border-color: #337ab7; +} +.pagination > .disabled > span, +.pagination > .disabled > span:hover, +.pagination > .disabled > span:focus, +.pagination > .disabled > a, +.pagination > .disabled > a:hover, +.pagination > .disabled > a:focus { + color: #777; + cursor: not-allowed; + background-color: #fff; + border-color: #ddd; +} +.pagination-lg > li > a, +.pagination-lg > li > span { + padding: 10px 16px; + font-size: 18px; + line-height: 1.3333333; +} +.pagination-lg > li:first-child > a, +.pagination-lg > li:first-child > span { + border-top-left-radius: 6px; + border-bottom-left-radius: 6px; +} +.pagination-lg > li:last-child > a, +.pagination-lg > li:last-child > span { + border-top-right-radius: 6px; + border-bottom-right-radius: 6px; +} +.pagination-sm > li > a, +.pagination-sm > li > span { + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; +} +.pagination-sm > li:first-child > a, +.pagination-sm > li:first-child > span { + border-top-left-radius: 3px; + border-bottom-left-radius: 3px; +} +.pagination-sm > li:last-child > a, +.pagination-sm > li:last-child > span { + border-top-right-radius: 3px; + border-bottom-right-radius: 3px; +} +.pager { + padding-left: 0; + margin: 20px 0; + text-align: center; + list-style: none; +} +.pager li { + display: inline; +} +.pager li > a, +.pager li > span { + display: inline-block; + padding: 5px 14px; + background-color: #fff; + border: 1px solid #ddd; + border-radius: 15px; +} +.pager li > a:hover, +.pager li > a:focus { + text-decoration: none; + background-color: #eee; +} +.pager .next > a, +.pager .next > span { + float: right; +} +.pager .previous > a, +.pager .previous > span { + float: left; +} +.pager .disabled > a, +.pager .disabled > a:hover, +.pager .disabled > a:focus, +.pager .disabled > span { + color: #777; + cursor: not-allowed; + background-color: #fff; +} +.label { + display: inline; + padding: .2em .6em .3em; + font-size: 75%; + font-weight: bold; + line-height: 1; + color: #fff; + text-align: center; + white-space: nowrap; + vertical-align: baseline; + border-radius: .25em; +} +a.label:hover, +a.label:focus { + color: #fff; + text-decoration: none; + cursor: pointer; +} +.label:empty { + display: none; +} +.btn .label { + position: relative; + top: -1px; +} +.label-default { + background-color: #777; +} +.label-default[href]:hover, +.label-default[href]:focus { + background-color: #5e5e5e; +} +.label-primary { + background-color: #337ab7; +} +.label-primary[href]:hover, +.label-primary[href]:focus { + background-color: #286090; +} +.label-success { + background-color: #5cb85c; +} +.label-success[href]:hover, +.label-success[href]:focus { + background-color: #449d44; +} +.label-info { + background-color: #5bc0de; +} +.label-info[href]:hover, +.label-info[href]:focus { + background-color: #31b0d5; +} +.label-warning { + background-color: #f0ad4e; +} +.label-warning[href]:hover, +.label-warning[href]:focus { + background-color: #ec971f; +} +.label-danger { + background-color: #d9534f; +} +.label-danger[href]:hover, +.label-danger[href]:focus { + background-color: #c9302c; +} +.badge { + display: inline-block; + min-width: 10px; + padding: 3px 7px; + font-size: 12px; + font-weight: bold; + line-height: 1; + color: #fff; + text-align: center; + white-space: nowrap; + vertical-align: middle; + background-color: #777; + border-radius: 10px; +} +.badge:empty { + display: none; +} +.btn .badge { + position: relative; + top: -1px; +} +.btn-xs .badge, +.btn-group-xs > .btn .badge { + top: 0; + padding: 1px 5px; +} +a.badge:hover, +a.badge:focus { + color: #fff; + text-decoration: none; + cursor: pointer; +} +.list-group-item.active > .badge, +.nav-pills > .active > a > .badge { + color: #337ab7; + background-color: #fff; +} +.list-group-item > .badge { + float: right; +} +.list-group-item > .badge + .badge { + margin-right: 5px; +} +.nav-pills > li > a > .badge { + margin-left: 3px; +} +.jumbotron { + padding-top: 30px; + padding-bottom: 30px; + margin-bottom: 30px; + color: inherit; + background-color: #eee; +} +.jumbotron h1, +.jumbotron .h1 { + color: inherit; +} +.jumbotron p { + margin-bottom: 15px; + font-size: 21px; + font-weight: 200; +} +.jumbotron > hr { + border-top-color: #d5d5d5; +} +.container .jumbotron, +.container-fluid .jumbotron { + padding-right: 15px; + padding-left: 15px; + border-radius: 6px; +} +.jumbotron .container { + max-width: 100%; +} +@media screen and (min-width: 768px) { + .jumbotron { + padding-top: 48px; + padding-bottom: 48px; + } + .container .jumbotron, + .container-fluid .jumbotron { + padding-right: 60px; + padding-left: 60px; + } + .jumbotron h1, + .jumbotron .h1 { + font-size: 63px; + } +} +.thumbnail { + display: block; + padding: 4px; + margin-bottom: 20px; + line-height: 1.42857143; + background-color: #fff; + border: 1px solid #ddd; + border-radius: 4px; + -webkit-transition: border .2s ease-in-out; + -o-transition: border .2s ease-in-out; + transition: border .2s ease-in-out; +} +.thumbnail > img, +.thumbnail a > img { + margin-right: auto; + margin-left: auto; +} +a.thumbnail:hover, +a.thumbnail:focus, +a.thumbnail.active { + border-color: #337ab7; +} +.thumbnail .caption { + padding: 9px; + color: #333; +} +.alert { + padding: 15px; + margin-bottom: 20px; + border: 1px solid transparent; + border-radius: 4px; +} +.alert h4 { + margin-top: 0; + color: inherit; +} +.alert .alert-link { + font-weight: bold; +} +.alert > p, +.alert > ul { + margin-bottom: 0; +} +.alert > p + p { + margin-top: 5px; +} +.alert-dismissable, +.alert-dismissible { + padding-right: 35px; +} +.alert-dismissable .close, +.alert-dismissible .close { + position: relative; + top: -2px; + right: -21px; + color: inherit; +} +.alert-success { + color: #3c763d; + background-color: #dff0d8; + border-color: #d6e9c6; +} +.alert-success hr { + border-top-color: #c9e2b3; +} +.alert-success .alert-link { + color: #2b542c; +} +.alert-info { + color: #31708f; + background-color: #d9edf7; + border-color: #bce8f1; +} +.alert-info hr { + border-top-color: #a6e1ec; +} +.alert-info .alert-link { + color: #245269; +} +.alert-warning { + color: #8a6d3b; + background-color: #fcf8e3; + border-color: #faebcc; +} +.alert-warning hr { + border-top-color: #f7e1b5; +} +.alert-warning .alert-link { + color: #66512c; +} +.alert-danger { + color: #a94442; + background-color: #f2dede; + border-color: #ebccd1; +} +.alert-danger hr { + border-top-color: #e4b9c0; +} +.alert-danger .alert-link { + color: #843534; +} +@-webkit-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} +@-o-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} +@keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} +.progress { + height: 20px; + margin-bottom: 20px; + overflow: hidden; + background-color: #f5f5f5; + border-radius: 4px; + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, .1); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, .1); +} +.progress-bar { + float: left; + width: 0; + height: 100%; + font-size: 12px; + line-height: 20px; + color: #fff; + text-align: center; + background-color: #337ab7; + -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .15); + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .15); + -webkit-transition: width .6s ease; + -o-transition: width .6s ease; + transition: width .6s ease; +} +.progress-striped .progress-bar, +.progress-bar-striped { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); + -webkit-background-size: 40px 40px; + background-size: 40px 40px; +} +.progress.active .progress-bar, +.progress-bar.active { + -webkit-animation: progress-bar-stripes 2s linear infinite; + -o-animation: progress-bar-stripes 2s linear infinite; + animation: progress-bar-stripes 2s linear infinite; +} +.progress-bar-success { + background-color: #5cb85c; +} +.progress-striped .progress-bar-success { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); +} +.progress-bar-info { + background-color: #5bc0de; +} +.progress-striped .progress-bar-info { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); +} +.progress-bar-warning { + background-color: #f0ad4e; +} +.progress-striped .progress-bar-warning { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); +} +.progress-bar-danger { + background-color: #d9534f; +} +.progress-striped .progress-bar-danger { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent); +} +.media { + margin-top: 15px; +} +.media:first-child { + margin-top: 0; +} +.media, +.media-body { + overflow: hidden; + zoom: 1; +} +.media-body { + width: 10000px; +} +.media-object { + display: block; +} +.media-object.img-thumbnail { + max-width: none; +} +.media-right, +.media > .pull-right { + padding-left: 10px; +} +.media-left, +.media > .pull-left { + padding-right: 10px; +} +.media-left, +.media-right, +.media-body { + display: table-cell; + vertical-align: top; +} +.media-middle { + vertical-align: middle; +} +.media-bottom { + vertical-align: bottom; +} +.media-heading { + margin-top: 0; + margin-bottom: 5px; +} +.media-list { + padding-left: 0; + list-style: none; +} +.list-group { + padding-left: 0; + margin-bottom: 20px; +} +.list-group-item { + position: relative; + display: block; + padding: 10px 15px; + margin-bottom: -1px; + background-color: #fff; + border: 1px solid #ddd; +} +.list-group-item:first-child { + border-top-left-radius: 4px; + border-top-right-radius: 4px; +} +.list-group-item:last-child { + margin-bottom: 0; + border-bottom-right-radius: 4px; + border-bottom-left-radius: 4px; +} +a.list-group-item, +button.list-group-item { + color: #555; +} +a.list-group-item .list-group-item-heading, +button.list-group-item .list-group-item-heading { + color: #333; +} +a.list-group-item:hover, +button.list-group-item:hover, +a.list-group-item:focus, +button.list-group-item:focus { + color: #555; + text-decoration: none; + background-color: #f5f5f5; +} +button.list-group-item { + width: 100%; + text-align: left; +} +.list-group-item.disabled, +.list-group-item.disabled:hover, +.list-group-item.disabled:focus { + color: #777; + cursor: not-allowed; + background-color: #eee; +} +.list-group-item.disabled .list-group-item-heading, +.list-group-item.disabled:hover .list-group-item-heading, +.list-group-item.disabled:focus .list-group-item-heading { + color: inherit; +} +.list-group-item.disabled .list-group-item-text, +.list-group-item.disabled:hover .list-group-item-text, +.list-group-item.disabled:focus .list-group-item-text { + color: #777; +} +.list-group-item.active, +.list-group-item.active:hover, +.list-group-item.active:focus { + z-index: 2; + color: #fff; + background-color: #337ab7; + border-color: #337ab7; +} +.list-group-item.active .list-group-item-heading, +.list-group-item.active:hover .list-group-item-heading, +.list-group-item.active:focus .list-group-item-heading, +.list-group-item.active .list-group-item-heading > small, +.list-group-item.active:hover .list-group-item-heading > small, +.list-group-item.active:focus .list-group-item-heading > small, +.list-group-item.active .list-group-item-heading > .small, +.list-group-item.active:hover .list-group-item-heading > .small, +.list-group-item.active:focus .list-group-item-heading > .small { + color: inherit; +} +.list-group-item.active .list-group-item-text, +.list-group-item.active:hover .list-group-item-text, +.list-group-item.active:focus .list-group-item-text { + color: #c7ddef; +} +.list-group-item-success { + color: #3c763d; + background-color: #dff0d8; +} +a.list-group-item-success, +button.list-group-item-success { + color: #3c763d; +} +a.list-group-item-success .list-group-item-heading, +button.list-group-item-success .list-group-item-heading { + color: inherit; +} +a.list-group-item-success:hover, +button.list-group-item-success:hover, +a.list-group-item-success:focus, +button.list-group-item-success:focus { + color: #3c763d; + background-color: #d0e9c6; +} +a.list-group-item-success.active, +button.list-group-item-success.active, +a.list-group-item-success.active:hover, +button.list-group-item-success.active:hover, +a.list-group-item-success.active:focus, +button.list-group-item-success.active:focus { + color: #fff; + background-color: #3c763d; + border-color: #3c763d; +} +.list-group-item-info { + color: #31708f; + background-color: #d9edf7; +} +a.list-group-item-info, +button.list-group-item-info { + color: #31708f; +} +a.list-group-item-info .list-group-item-heading, +button.list-group-item-info .list-group-item-heading { + color: inherit; +} +a.list-group-item-info:hover, +button.list-group-item-info:hover, +a.list-group-item-info:focus, +button.list-group-item-info:focus { + color: #31708f; + background-color: #c4e3f3; +} +a.list-group-item-info.active, +button.list-group-item-info.active, +a.list-group-item-info.active:hover, +button.list-group-item-info.active:hover, +a.list-group-item-info.active:focus, +button.list-group-item-info.active:focus { + color: #fff; + background-color: #31708f; + border-color: #31708f; +} +.list-group-item-warning { + color: #8a6d3b; + background-color: #fcf8e3; +} +a.list-group-item-warning, +button.list-group-item-warning { + color: #8a6d3b; +} +a.list-group-item-warning .list-group-item-heading, +button.list-group-item-warning .list-group-item-heading { + color: inherit; +} +a.list-group-item-warning:hover, +button.list-group-item-warning:hover, +a.list-group-item-warning:focus, +button.list-group-item-warning:focus { + color: #8a6d3b; + background-color: #faf2cc; +} +a.list-group-item-warning.active, +button.list-group-item-warning.active, +a.list-group-item-warning.active:hover, +button.list-group-item-warning.active:hover, +a.list-group-item-warning.active:focus, +button.list-group-item-warning.active:focus { + color: #fff; + background-color: #8a6d3b; + border-color: #8a6d3b; +} +.list-group-item-danger { + color: #a94442; + background-color: #f2dede; +} +a.list-group-item-danger, +button.list-group-item-danger { + color: #a94442; +} +a.list-group-item-danger .list-group-item-heading, +button.list-group-item-danger .list-group-item-heading { + color: inherit; +} +a.list-group-item-danger:hover, +button.list-group-item-danger:hover, +a.list-group-item-danger:focus, +button.list-group-item-danger:focus { + color: #a94442; + background-color: #ebcccc; +} +a.list-group-item-danger.active, +button.list-group-item-danger.active, +a.list-group-item-danger.active:hover, +button.list-group-item-danger.active:hover, +a.list-group-item-danger.active:focus, +button.list-group-item-danger.active:focus { + color: #fff; + background-color: #a94442; + border-color: #a94442; +} +.list-group-item-heading { + margin-top: 0; + margin-bottom: 5px; +} +.list-group-item-text { + margin-bottom: 0; + line-height: 1.3; +} +.panel { + margin-bottom: 20px; + background-color: #fff; + border: 1px solid transparent; + border-radius: 4px; + -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, .05); + box-shadow: 0 1px 1px rgba(0, 0, 0, .05); +} +.panel-body { + padding: 15px; +} +.panel-heading { + padding: 10px 15px; + border-bottom: 1px solid transparent; + border-top-left-radius: 3px; + border-top-right-radius: 3px; +} +.panel-heading > .dropdown .dropdown-toggle { + color: inherit; +} +.panel-title { + margin-top: 0; + margin-bottom: 0; + font-size: 16px; + color: inherit; +} +.panel-title > a, +.panel-title > small, +.panel-title > .small, +.panel-title > small > a, +.panel-title > .small > a { + color: inherit; +} +.panel-footer { + padding: 10px 15px; + background-color: #f5f5f5; + border-top: 1px solid #ddd; + border-bottom-right-radius: 3px; + border-bottom-left-radius: 3px; +} +.panel > .list-group, +.panel > .panel-collapse > .list-group { + margin-bottom: 0; +} +.panel > .list-group .list-group-item, +.panel > .panel-collapse > .list-group .list-group-item { + border-width: 1px 0; + border-radius: 0; +} +.panel > .list-group:first-child .list-group-item:first-child, +.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child { + border-top: 0; + border-top-left-radius: 3px; + border-top-right-radius: 3px; +} +.panel > .list-group:last-child .list-group-item:last-child, +.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child { + border-bottom: 0; + border-bottom-right-radius: 3px; + border-bottom-left-radius: 3px; +} +.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child { + border-top-left-radius: 0; + border-top-right-radius: 0; +} +.panel-heading + .list-group .list-group-item:first-child { + border-top-width: 0; +} +.list-group + .panel-footer { + border-top-width: 0; +} +.panel > .table, +.panel > .table-responsive > .table, +.panel > .panel-collapse > .table { + margin-bottom: 0; +} +.panel > .table caption, +.panel > .table-responsive > .table caption, +.panel > .panel-collapse > .table caption { + padding-right: 15px; + padding-left: 15px; +} +.panel > .table:first-child, +.panel > .table-responsive:first-child > .table:first-child { + border-top-left-radius: 3px; + border-top-right-radius: 3px; +} +.panel > .table:first-child > thead:first-child > tr:first-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child, +.panel > .table:first-child > tbody:first-child > tr:first-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child { + border-top-left-radius: 3px; + border-top-right-radius: 3px; +} +.panel > .table:first-child > thead:first-child > tr:first-child td:first-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child, +.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child, +.panel > .table:first-child > thead:first-child > tr:first-child th:first-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child, +.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child { + border-top-left-radius: 3px; +} +.panel > .table:first-child > thead:first-child > tr:first-child td:last-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child, +.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child, +.panel > .table:first-child > thead:first-child > tr:first-child th:last-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child, +.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child { + border-top-right-radius: 3px; +} +.panel > .table:last-child, +.panel > .table-responsive:last-child > .table:last-child { + border-bottom-right-radius: 3px; + border-bottom-left-radius: 3px; +} +.panel > .table:last-child > tbody:last-child > tr:last-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child { + border-bottom-right-radius: 3px; + border-bottom-left-radius: 3px; +} +.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child, +.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child { + border-bottom-left-radius: 3px; +} +.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child, +.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child { + border-bottom-right-radius: 3px; +} +.panel > .panel-body + .table, +.panel > .panel-body + .table-responsive, +.panel > .table + .panel-body, +.panel > .table-responsive + .panel-body { + border-top: 1px solid #ddd; +} +.panel > .table > tbody:first-child > tr:first-child th, +.panel > .table > tbody:first-child > tr:first-child td { + border-top: 0; +} +.panel > .table-bordered, +.panel > .table-responsive > .table-bordered { + border: 0; +} +.panel > .table-bordered > thead > tr > th:first-child, +.panel > .table-responsive > .table-bordered > thead > tr > th:first-child, +.panel > .table-bordered > tbody > tr > th:first-child, +.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child, +.panel > .table-bordered > tfoot > tr > th:first-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child, +.panel > .table-bordered > thead > tr > td:first-child, +.panel > .table-responsive > .table-bordered > thead > tr > td:first-child, +.panel > .table-bordered > tbody > tr > td:first-child, +.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child, +.panel > .table-bordered > tfoot > tr > td:first-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child { + border-left: 0; +} +.panel > .table-bordered > thead > tr > th:last-child, +.panel > .table-responsive > .table-bordered > thead > tr > th:last-child, +.panel > .table-bordered > tbody > tr > th:last-child, +.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child, +.panel > .table-bordered > tfoot > tr > th:last-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child, +.panel > .table-bordered > thead > tr > td:last-child, +.panel > .table-responsive > .table-bordered > thead > tr > td:last-child, +.panel > .table-bordered > tbody > tr > td:last-child, +.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child, +.panel > .table-bordered > tfoot > tr > td:last-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child { + border-right: 0; +} +.panel > .table-bordered > thead > tr:first-child > td, +.panel > .table-responsive > .table-bordered > thead > tr:first-child > td, +.panel > .table-bordered > tbody > tr:first-child > td, +.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td, +.panel > .table-bordered > thead > tr:first-child > th, +.panel > .table-responsive > .table-bordered > thead > tr:first-child > th, +.panel > .table-bordered > tbody > tr:first-child > th, +.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th { + border-bottom: 0; +} +.panel > .table-bordered > tbody > tr:last-child > td, +.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td, +.panel > .table-bordered > tfoot > tr:last-child > td, +.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td, +.panel > .table-bordered > tbody > tr:last-child > th, +.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th, +.panel > .table-bordered > tfoot > tr:last-child > th, +.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th { + border-bottom: 0; +} +.panel > .table-responsive { + margin-bottom: 0; + border: 0; +} +.panel-group { + margin-bottom: 20px; +} +.panel-group .panel { + margin-bottom: 0; + border-radius: 4px; +} +.panel-group .panel + .panel { + margin-top: 5px; +} +.panel-group .panel-heading { + border-bottom: 0; +} +.panel-group .panel-heading + .panel-collapse > .panel-body, +.panel-group .panel-heading + .panel-collapse > .list-group { + border-top: 1px solid #ddd; +} +.panel-group .panel-footer { + border-top: 0; +} +.panel-group .panel-footer + .panel-collapse .panel-body { + border-bottom: 1px solid #ddd; +} +.panel-default { + border-color: #ddd; +} +.panel-default > .panel-heading { + color: #333; + background-color: #f5f5f5; + border-color: #ddd; +} +.panel-default > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #ddd; +} +.panel-default > .panel-heading .badge { + color: #f5f5f5; + background-color: #333; +} +.panel-default > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #ddd; +} +.panel-primary { + border-color: #337ab7; +} +.panel-primary > .panel-heading { + color: #fff; + background-color: #337ab7; + border-color: #337ab7; +} +.panel-primary > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #337ab7; +} +.panel-primary > .panel-heading .badge { + color: #337ab7; + background-color: #fff; +} +.panel-primary > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #337ab7; +} +.panel-success { + border-color: #d6e9c6; +} +.panel-success > .panel-heading { + color: #3c763d; + background-color: #dff0d8; + border-color: #d6e9c6; +} +.panel-success > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #d6e9c6; +} +.panel-success > .panel-heading .badge { + color: #dff0d8; + background-color: #3c763d; +} +.panel-success > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #d6e9c6; +} +.panel-info { + border-color: #bce8f1; +} +.panel-info > .panel-heading { + color: #31708f; + background-color: #d9edf7; + border-color: #bce8f1; +} +.panel-info > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #bce8f1; +} +.panel-info > .panel-heading .badge { + color: #d9edf7; + background-color: #31708f; +} +.panel-info > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #bce8f1; +} +.panel-warning { + border-color: #faebcc; +} +.panel-warning > .panel-heading { + color: #8a6d3b; + background-color: #fcf8e3; + border-color: #faebcc; +} +.panel-warning > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #faebcc; +} +.panel-warning > .panel-heading .badge { + color: #fcf8e3; + background-color: #8a6d3b; +} +.panel-warning > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #faebcc; +} +.panel-danger { + border-color: #ebccd1; +} +.panel-danger > .panel-heading { + color: #a94442; + background-color: #f2dede; + border-color: #ebccd1; +} +.panel-danger > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #ebccd1; +} +.panel-danger > .panel-heading .badge { + color: #f2dede; + background-color: #a94442; +} +.panel-danger > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #ebccd1; +} +.embed-responsive { + position: relative; + display: block; + height: 0; + padding: 0; + overflow: hidden; +} +.embed-responsive .embed-responsive-item, +.embed-responsive iframe, +.embed-responsive embed, +.embed-responsive object, +.embed-responsive video { + position: absolute; + top: 0; + bottom: 0; + left: 0; + width: 100%; + height: 100%; + border: 0; +} +.embed-responsive-16by9 { + padding-bottom: 56.25%; +} +.embed-responsive-4by3 { + padding-bottom: 75%; +} +.well { + min-height: 20px; + padding: 19px; + margin-bottom: 20px; + background-color: #f5f5f5; + border: 1px solid #e3e3e3; + border-radius: 4px; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .05); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, .05); +} +.well blockquote { + border-color: #ddd; + border-color: rgba(0, 0, 0, .15); +} +.well-lg { + padding: 24px; + border-radius: 6px; +} +.well-sm { + padding: 9px; + border-radius: 3px; +} +.close { + float: right; + font-size: 21px; + font-weight: bold; + line-height: 1; + color: #000; + text-shadow: 0 1px 0 #fff; + filter: alpha(opacity=20); + opacity: .2; +} +.close:hover, +.close:focus { + color: #000; + text-decoration: none; + cursor: pointer; + filter: alpha(opacity=50); + opacity: .5; +} +button.close { + -webkit-appearance: none; + padding: 0; + cursor: pointer; + background: transparent; + border: 0; +} +.modal-open { + overflow: hidden; +} +.modal { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1050; + display: none; + overflow: hidden; + -webkit-overflow-scrolling: touch; + outline: 0; +} +.modal.fade .modal-dialog { + -webkit-transition: -webkit-transform .3s ease-out; + -o-transition: -o-transform .3s ease-out; + transition: transform .3s ease-out; + -webkit-transform: translate(0, -25%); + -ms-transform: translate(0, -25%); + -o-transform: translate(0, -25%); + transform: translate(0, -25%); +} +.modal.in .modal-dialog { + -webkit-transform: translate(0, 0); + -ms-transform: translate(0, 0); + -o-transform: translate(0, 0); + transform: translate(0, 0); +} +.modal-open .modal { + overflow-x: hidden; + overflow-y: auto; +} +.modal-dialog { + position: relative; + width: auto; + margin: 10px; +} +.modal-content { + position: relative; + background-color: #fff; + -webkit-background-clip: padding-box; + background-clip: padding-box; + border: 1px solid #999; + border: 1px solid rgba(0, 0, 0, .2); + border-radius: 6px; + outline: 0; + -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, .5); + box-shadow: 0 3px 9px rgba(0, 0, 0, .5); +} +.modal-backdrop { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1040; + background-color: #000; +} +.modal-backdrop.fade { + filter: alpha(opacity=0); + opacity: 0; +} +.modal-backdrop.in { + filter: alpha(opacity=50); + opacity: .5; +} +.modal-header { + padding: 15px; + border-bottom: 1px solid #e5e5e5; +} +.modal-header .close { + margin-top: -2px; +} +.modal-title { + margin: 0; + line-height: 1.42857143; +} +.modal-body { + position: relative; + padding: 15px; +} +.modal-footer { + padding: 15px; + text-align: right; + border-top: 1px solid #e5e5e5; +} +.modal-footer .btn + .btn { + margin-bottom: 0; + margin-left: 5px; +} +.modal-footer .btn-group .btn + .btn { + margin-left: -1px; +} +.modal-footer .btn-block + .btn-block { + margin-left: 0; +} +.modal-scrollbar-measure { + position: absolute; + top: -9999px; + width: 50px; + height: 50px; + overflow: scroll; +} +@media (min-width: 768px) { + .modal-dialog { + width: 600px; + margin: 30px auto; + } + .modal-content { + -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, .5); + box-shadow: 0 5px 15px rgba(0, 0, 0, .5); + } + .modal-sm { + width: 300px; + } +} +@media (min-width: 992px) { + .modal-lg { + width: 900px; + } +} +.tooltip { + position: absolute; + z-index: 1070; + display: block; + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; + font-size: 12px; + font-style: normal; + font-weight: normal; + line-height: 1.42857143; + text-align: left; + text-align: start; + text-decoration: none; + text-shadow: none; + text-transform: none; + letter-spacing: normal; + word-break: normal; + word-spacing: normal; + word-wrap: normal; + white-space: normal; + filter: alpha(opacity=0); + opacity: 0; + + line-break: auto; +} +.tooltip.in { + filter: alpha(opacity=90); + opacity: .9; +} +.tooltip.top { + padding: 5px 0; + margin-top: -3px; +} +.tooltip.right { + padding: 0 5px; + margin-left: 3px; +} +.tooltip.bottom { + padding: 5px 0; + margin-top: 3px; +} +.tooltip.left { + padding: 0 5px; + margin-left: -3px; +} +.tooltip-inner { + max-width: 200px; + padding: 3px 8px; + color: #fff; + text-align: center; + background-color: #000; + border-radius: 4px; +} +.tooltip-arrow { + position: absolute; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; +} +.tooltip.top .tooltip-arrow { + bottom: 0; + left: 50%; + margin-left: -5px; + border-width: 5px 5px 0; + border-top-color: #000; +} +.tooltip.top-left .tooltip-arrow { + right: 5px; + bottom: 0; + margin-bottom: -5px; + border-width: 5px 5px 0; + border-top-color: #000; +} +.tooltip.top-right .tooltip-arrow { + bottom: 0; + left: 5px; + margin-bottom: -5px; + border-width: 5px 5px 0; + border-top-color: #000; +} +.tooltip.right .tooltip-arrow { + top: 50%; + left: 0; + margin-top: -5px; + border-width: 5px 5px 5px 0; + border-right-color: #000; +} +.tooltip.left .tooltip-arrow { + top: 50%; + right: 0; + margin-top: -5px; + border-width: 5px 0 5px 5px; + border-left-color: #000; +} +.tooltip.bottom .tooltip-arrow { + top: 0; + left: 50%; + margin-left: -5px; + border-width: 0 5px 5px; + border-bottom-color: #000; +} +.tooltip.bottom-left .tooltip-arrow { + top: 0; + right: 5px; + margin-top: -5px; + border-width: 0 5px 5px; + border-bottom-color: #000; +} +.tooltip.bottom-right .tooltip-arrow { + top: 0; + left: 5px; + margin-top: -5px; + border-width: 0 5px 5px; + border-bottom-color: #000; +} +.popover { + position: absolute; + top: 0; + left: 0; + z-index: 1060; + display: none; + max-width: 276px; + padding: 1px; + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; + font-size: 14px; + font-style: normal; + font-weight: normal; + line-height: 1.42857143; + text-align: left; + text-align: start; + text-decoration: none; + text-shadow: none; + text-transform: none; + letter-spacing: normal; + word-break: normal; + word-spacing: normal; + word-wrap: normal; + white-space: normal; + background-color: #fff; + -webkit-background-clip: padding-box; + background-clip: padding-box; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, .2); + border-radius: 6px; + -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, .2); + box-shadow: 0 5px 10px rgba(0, 0, 0, .2); + + line-break: auto; +} +.popover.top { + margin-top: -10px; +} +.popover.right { + margin-left: 10px; +} +.popover.bottom { + margin-top: 10px; +} +.popover.left { + margin-left: -10px; +} +.popover-title { + padding: 8px 14px; + margin: 0; + font-size: 14px; + background-color: #f7f7f7; + border-bottom: 1px solid #ebebeb; + border-radius: 5px 5px 0 0; +} +.popover-content { + padding: 9px 14px; +} +.popover > .arrow, +.popover > .arrow:after { + position: absolute; + display: block; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; +} +.popover > .arrow { + border-width: 11px; +} +.popover > .arrow:after { + content: ""; + border-width: 10px; +} +.popover.top > .arrow { + bottom: -11px; + left: 50%; + margin-left: -11px; + border-top-color: #999; + border-top-color: rgba(0, 0, 0, .25); + border-bottom-width: 0; +} +.popover.top > .arrow:after { + bottom: 1px; + margin-left: -10px; + content: " "; + border-top-color: #fff; + border-bottom-width: 0; +} +.popover.right > .arrow { + top: 50%; + left: -11px; + margin-top: -11px; + border-right-color: #999; + border-right-color: rgba(0, 0, 0, .25); + border-left-width: 0; +} +.popover.right > .arrow:after { + bottom: -10px; + left: 1px; + content: " "; + border-right-color: #fff; + border-left-width: 0; +} +.popover.bottom > .arrow { + top: -11px; + left: 50%; + margin-left: -11px; + border-top-width: 0; + border-bottom-color: #999; + border-bottom-color: rgba(0, 0, 0, .25); +} +.popover.bottom > .arrow:after { + top: 1px; + margin-left: -10px; + content: " "; + border-top-width: 0; + border-bottom-color: #fff; +} +.popover.left > .arrow { + top: 50%; + right: -11px; + margin-top: -11px; + border-right-width: 0; + border-left-color: #999; + border-left-color: rgba(0, 0, 0, .25); +} +.popover.left > .arrow:after { + right: 1px; + bottom: -10px; + content: " "; + border-right-width: 0; + border-left-color: #fff; +} +.carousel { + position: relative; +} +.carousel-inner { + position: relative; + width: 100%; + overflow: hidden; +} +.carousel-inner > .item { + position: relative; + display: none; + -webkit-transition: .6s ease-in-out left; + -o-transition: .6s ease-in-out left; + transition: .6s ease-in-out left; +} +.carousel-inner > .item > img, +.carousel-inner > .item > a > img { + line-height: 1; +} +@media all and (transform-3d), (-webkit-transform-3d) { + .carousel-inner > .item { + -webkit-transition: -webkit-transform .6s ease-in-out; + -o-transition: -o-transform .6s ease-in-out; + transition: transform .6s ease-in-out; + + -webkit-backface-visibility: hidden; + backface-visibility: hidden; + -webkit-perspective: 1000px; + perspective: 1000px; + } + .carousel-inner > .item.next, + .carousel-inner > .item.active.right { + left: 0; + -webkit-transform: translate3d(100%, 0, 0); + transform: translate3d(100%, 0, 0); + } + .carousel-inner > .item.prev, + .carousel-inner > .item.active.left { + left: 0; + -webkit-transform: translate3d(-100%, 0, 0); + transform: translate3d(-100%, 0, 0); + } + .carousel-inner > .item.next.left, + .carousel-inner > .item.prev.right, + .carousel-inner > .item.active { + left: 0; + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } +} +.carousel-inner > .active, +.carousel-inner > .next, +.carousel-inner > .prev { + display: block; +} +.carousel-inner > .active { + left: 0; +} +.carousel-inner > .next, +.carousel-inner > .prev { + position: absolute; + top: 0; + width: 100%; +} +.carousel-inner > .next { + left: 100%; +} +.carousel-inner > .prev { + left: -100%; +} +.carousel-inner > .next.left, +.carousel-inner > .prev.right { + left: 0; +} +.carousel-inner > .active.left { + left: -100%; +} +.carousel-inner > .active.right { + left: 100%; +} +.carousel-control { + position: absolute; + top: 0; + bottom: 0; + left: 0; + width: 15%; + font-size: 20px; + color: #fff; + text-align: center; + text-shadow: 0 1px 2px rgba(0, 0, 0, .6); + background-color: rgba(0, 0, 0, 0); + filter: alpha(opacity=50); + opacity: .5; +} +.carousel-control.left { + background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, .5) 0%, rgba(0, 0, 0, .0001) 100%); + background-image: -o-linear-gradient(left, rgba(0, 0, 0, .5) 0%, rgba(0, 0, 0, .0001) 100%); + background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, .5)), to(rgba(0, 0, 0, .0001))); + background-image: linear-gradient(to right, rgba(0, 0, 0, .5) 0%, rgba(0, 0, 0, .0001) 100%); + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1); + background-repeat: repeat-x; +} +.carousel-control.right { + right: 0; + left: auto; + background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, .0001) 0%, rgba(0, 0, 0, .5) 100%); + background-image: -o-linear-gradient(left, rgba(0, 0, 0, .0001) 0%, rgba(0, 0, 0, .5) 100%); + background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, .0001)), to(rgba(0, 0, 0, .5))); + background-image: linear-gradient(to right, rgba(0, 0, 0, .0001) 0%, rgba(0, 0, 0, .5) 100%); + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1); + background-repeat: repeat-x; +} +.carousel-control:hover, +.carousel-control:focus { + color: #fff; + text-decoration: none; + filter: alpha(opacity=90); + outline: 0; + opacity: .9; +} +.carousel-control .icon-prev, +.carousel-control .icon-next, +.carousel-control .glyphicon-chevron-left, +.carousel-control .glyphicon-chevron-right { + position: absolute; + top: 50%; + z-index: 5; + display: inline-block; + margin-top: -10px; +} +.carousel-control .icon-prev, +.carousel-control .glyphicon-chevron-left { + left: 50%; + margin-left: -10px; +} +.carousel-control .icon-next, +.carousel-control .glyphicon-chevron-right { + right: 50%; + margin-right: -10px; +} +.carousel-control .icon-prev, +.carousel-control .icon-next { + width: 20px; + height: 20px; + font-family: serif; + line-height: 1; +} +.carousel-control .icon-prev:before { + content: '\2039'; +} +.carousel-control .icon-next:before { + content: '\203a'; +} +.carousel-indicators { + position: absolute; + bottom: 10px; + left: 50%; + z-index: 15; + width: 60%; + padding-left: 0; + margin-left: -30%; + text-align: center; + list-style: none; +} +.carousel-indicators li { + display: inline-block; + width: 10px; + height: 10px; + margin: 1px; + text-indent: -999px; + cursor: pointer; + background-color: #000 \9; + background-color: rgba(0, 0, 0, 0); + border: 1px solid #fff; + border-radius: 10px; +} +.carousel-indicators .active { + width: 12px; + height: 12px; + margin: 0; + background-color: #fff; +} +.carousel-caption { + position: absolute; + right: 15%; + bottom: 20px; + left: 15%; + z-index: 10; + padding-top: 20px; + padding-bottom: 20px; + color: #fff; + text-align: center; + text-shadow: 0 1px 2px rgba(0, 0, 0, .6); +} +.carousel-caption .btn { + text-shadow: none; +} +@media screen and (min-width: 768px) { + .carousel-control .glyphicon-chevron-left, + .carousel-control .glyphicon-chevron-right, + .carousel-control .icon-prev, + .carousel-control .icon-next { + width: 30px; + height: 30px; + margin-top: -10px; + font-size: 30px; + } + .carousel-control .glyphicon-chevron-left, + .carousel-control .icon-prev { + margin-left: -10px; + } + .carousel-control .glyphicon-chevron-right, + .carousel-control .icon-next { + margin-right: -10px; + } + .carousel-caption { + right: 20%; + left: 20%; + padding-bottom: 30px; + } + .carousel-indicators { + bottom: 20px; + } +} +.clearfix:before, +.clearfix:after, +.dl-horizontal dd:before, +.dl-horizontal dd:after, +.container:before, +.container:after, +.container-fluid:before, +.container-fluid:after, +.row:before, +.row:after, +.form-horizontal .form-group:before, +.form-horizontal .form-group:after, +.btn-toolbar:before, +.btn-toolbar:after, +.btn-group-vertical > .btn-group:before, +.btn-group-vertical > .btn-group:after, +.nav:before, +.nav:after, +.navbar:before, +.navbar:after, +.navbar-header:before, +.navbar-header:after, +.navbar-collapse:before, +.navbar-collapse:after, +.pager:before, +.pager:after, +.panel-body:before, +.panel-body:after, +.modal-header:before, +.modal-header:after, +.modal-footer:before, +.modal-footer:after { + display: table; + content: " "; +} +.clearfix:after, +.dl-horizontal dd:after, +.container:after, +.container-fluid:after, +.row:after, +.form-horizontal .form-group:after, +.btn-toolbar:after, +.btn-group-vertical > .btn-group:after, +.nav:after, +.navbar:after, +.navbar-header:after, +.navbar-collapse:after, +.pager:after, +.panel-body:after, +.modal-header:after, +.modal-footer:after { + clear: both; +} +.center-block { + display: block; + margin-right: auto; + margin-left: auto; +} +.pull-right { + float: right !important; +} +.pull-left { + float: left !important; +} +.hide { + display: none !important; +} +.show { + display: block !important; +} +.invisible { + visibility: hidden; +} +.text-hide { + font: 0/0 a; + color: transparent; + text-shadow: none; + background-color: transparent; + border: 0; +} +.hidden { + display: none !important; +} +.affix { + position: fixed; +} +@-ms-viewport { + width: device-width; +} +.visible-xs, +.visible-sm, +.visible-md, +.visible-lg { + display: none !important; +} +.visible-xs-block, +.visible-xs-inline, +.visible-xs-inline-block, +.visible-sm-block, +.visible-sm-inline, +.visible-sm-inline-block, +.visible-md-block, +.visible-md-inline, +.visible-md-inline-block, +.visible-lg-block, +.visible-lg-inline, +.visible-lg-inline-block { + display: none !important; +} +@media (max-width: 767px) { + .visible-xs { + display: block !important; + } + table.visible-xs { + display: table !important; + } + tr.visible-xs { + display: table-row !important; + } + th.visible-xs, + td.visible-xs { + display: table-cell !important; + } +} +@media (max-width: 767px) { + .visible-xs-block { + display: block !important; + } +} +@media (max-width: 767px) { + .visible-xs-inline { + display: inline !important; + } +} +@media (max-width: 767px) { + .visible-xs-inline-block { + display: inline-block !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm { + display: block !important; + } + table.visible-sm { + display: table !important; + } + tr.visible-sm { + display: table-row !important; + } + th.visible-sm, + td.visible-sm { + display: table-cell !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm-block { + display: block !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm-inline { + display: inline !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm-inline-block { + display: inline-block !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md { + display: block !important; + } + table.visible-md { + display: table !important; + } + tr.visible-md { + display: table-row !important; + } + th.visible-md, + td.visible-md { + display: table-cell !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md-block { + display: block !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md-inline { + display: inline !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md-inline-block { + display: inline-block !important; + } +} +@media (min-width: 1200px) { + .visible-lg { + display: block !important; + } + table.visible-lg { + display: table !important; + } + tr.visible-lg { + display: table-row !important; + } + th.visible-lg, + td.visible-lg { + display: table-cell !important; + } +} +@media (min-width: 1200px) { + .visible-lg-block { + display: block !important; + } +} +@media (min-width: 1200px) { + .visible-lg-inline { + display: inline !important; + } +} +@media (min-width: 1200px) { + .visible-lg-inline-block { + display: inline-block !important; + } +} +@media (max-width: 767px) { + .hidden-xs { + display: none !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .hidden-sm { + display: none !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .hidden-md { + display: none !important; + } +} +@media (min-width: 1200px) { + .hidden-lg { + display: none !important; + } +} +.visible-print { + display: none !important; +} +@media print { + .visible-print { + display: block !important; + } + table.visible-print { + display: table !important; + } + tr.visible-print { + display: table-row !important; + } + th.visible-print, + td.visible-print { + display: table-cell !important; + } +} +.visible-print-block { + display: none !important; +} +@media print { + .visible-print-block { + display: block !important; + } +} +.visible-print-inline { + display: none !important; +} +@media print { + .visible-print-inline { + display: inline !important; + } +} +.visible-print-inline-block { + display: none !important; +} +@media print { + .visible-print-inline-block { + display: inline-block !important; + } +} +@media print { + .hidden-print { + display: none !important; + } +} +/*# sourceMappingURL=bootstrap.css.map */ diff --git a/static/css/bootstrap.css.map b/static/css/bootstrap.css.map new file mode 100644 index 0000000..f010c82 --- /dev/null +++ b/static/css/bootstrap.css.map @@ -0,0 +1 @@ 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{\n font-size: 80%;\n}\nsub,\nsup {\n font-size: 75%;\n line-height: 0;\n position: relative;\n vertical-align: baseline;\n}\nsup {\n top: -0.5em;\n}\nsub {\n bottom: -0.25em;\n}\nimg {\n border: 0;\n}\nsvg:not(:root) {\n overflow: hidden;\n}\nfigure {\n margin: 1em 40px;\n}\nhr {\n box-sizing: content-box;\n height: 0;\n}\npre {\n overflow: auto;\n}\ncode,\nkbd,\npre,\nsamp {\n font-family: monospace, monospace;\n font-size: 1em;\n}\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n color: inherit;\n font: inherit;\n margin: 0;\n}\nbutton {\n overflow: visible;\n}\nbutton,\nselect {\n text-transform: none;\n}\nbutton,\nhtml input[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n -webkit-appearance: button;\n cursor: pointer;\n}\nbutton[disabled],\nhtml input[disabled] {\n cursor: default;\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n border: 0;\n padding: 0;\n}\ninput {\n line-height: normal;\n}\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n box-sizing: border-box;\n padding: 0;\n}\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\ninput[type=\"search\"] {\n -webkit-appearance: textfield;\n box-sizing: content-box;\n}\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\nfieldset {\n border: 1px solid #c0c0c0;\n margin: 0 2px;\n padding: 0.35em 0.625em 0.75em;\n}\nlegend {\n border: 0;\n padding: 0;\n}\ntextarea {\n overflow: auto;\n}\noptgroup {\n font-weight: bold;\n}\ntable {\n border-collapse: collapse;\n border-spacing: 0;\n}\ntd,\nth {\n padding: 0;\n}\n/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n@media print {\n *,\n *:before,\n *:after {\n background: transparent !important;\n color: #000 !important;\n box-shadow: none !important;\n text-shadow: none !important;\n }\n a,\n a:visited {\n text-decoration: underline;\n }\n a[href]:after {\n content: \" (\" attr(href) \")\";\n }\n abbr[title]:after {\n content: \" (\" attr(title) \")\";\n }\n a[href^=\"#\"]:after,\n a[href^=\"javascript:\"]:after {\n content: \"\";\n }\n pre,\n blockquote {\n border: 1px solid #999;\n page-break-inside: avoid;\n }\n thead {\n display: table-header-group;\n }\n tr,\n img {\n page-break-inside: avoid;\n }\n img {\n max-width: 100% !important;\n }\n p,\n h2,\n h3 {\n orphans: 3;\n widows: 3;\n }\n h2,\n h3 {\n page-break-after: avoid;\n }\n .navbar {\n display: none;\n }\n .btn > .caret,\n .dropup > .btn > .caret {\n border-top-color: #000 !important;\n }\n .label {\n border: 1px solid #000;\n }\n .table {\n border-collapse: collapse !important;\n }\n .table td,\n .table th {\n background-color: #fff !important;\n }\n .table-bordered th,\n .table-bordered td {\n border: 1px solid #ddd !important;\n }\n}\n@font-face {\n font-family: 'Glyphicons Halflings';\n src: url('../fonts/glyphicons-halflings-regular.eot');\n src: url('../fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../fonts/glyphicons-halflings-regular.woff') format('woff'), url('../fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');\n}\n.glyphicon {\n position: relative;\n top: 1px;\n display: inline-block;\n font-family: 'Glyphicons Halflings';\n font-style: normal;\n font-weight: normal;\n line-height: 1;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n}\n.glyphicon-asterisk:before {\n content: \"\\002a\";\n}\n.glyphicon-plus:before {\n content: \"\\002b\";\n}\n.glyphicon-euro:before,\n.glyphicon-eur:before {\n content: \"\\20ac\";\n}\n.glyphicon-minus:before {\n content: \"\\2212\";\n}\n.glyphicon-cloud:before {\n content: \"\\2601\";\n}\n.glyphicon-envelope:before {\n content: \"\\2709\";\n}\n.glyphicon-pencil:before {\n content: \"\\270f\";\n}\n.glyphicon-glass:before {\n content: \"\\e001\";\n}\n.glyphicon-music:before {\n content: \"\\e002\";\n}\n.glyphicon-search:before {\n content: \"\\e003\";\n}\n.glyphicon-heart:before {\n content: \"\\e005\";\n}\n.glyphicon-star:before {\n content: \"\\e006\";\n}\n.glyphicon-star-empty:before {\n content: \"\\e007\";\n}\n.glyphicon-user:before {\n content: \"\\e008\";\n}\n.glyphicon-film:before {\n content: \"\\e009\";\n}\n.glyphicon-th-large:before {\n content: \"\\e010\";\n}\n.glyphicon-th:before {\n content: \"\\e011\";\n}\n.glyphicon-th-list:before {\n content: \"\\e012\";\n}\n.glyphicon-ok:before {\n content: \"\\e013\";\n}\n.glyphicon-remove:before {\n content: \"\\e014\";\n}\n.glyphicon-zoom-in:before {\n content: \"\\e015\";\n}\n.glyphicon-zoom-out:before {\n content: \"\\e016\";\n}\n.glyphicon-off:before {\n content: \"\\e017\";\n}\n.glyphicon-signal:before {\n content: \"\\e018\";\n}\n.glyphicon-cog:before {\n content: \"\\e019\";\n}\n.glyphicon-trash:before {\n content: \"\\e020\";\n}\n.glyphicon-home:before {\n content: \"\\e021\";\n}\n.glyphicon-file:before {\n content: \"\\e022\";\n}\n.glyphicon-time:before {\n content: \"\\e023\";\n}\n.glyphicon-road:before {\n content: \"\\e024\";\n}\n.glyphicon-download-alt:before {\n content: \"\\e025\";\n}\n.glyphicon-download:before {\n content: \"\\e026\";\n}\n.glyphicon-upload:before {\n content: \"\\e027\";\n}\n.glyphicon-inbox:before {\n content: \"\\e028\";\n}\n.glyphicon-play-circle:before {\n content: \"\\e029\";\n}\n.glyphicon-repeat:before {\n content: \"\\e030\";\n}\n.glyphicon-refresh:before {\n content: \"\\e031\";\n}\n.glyphicon-list-alt:before {\n content: \"\\e032\";\n}\n.glyphicon-lock:before {\n content: \"\\e033\";\n}\n.glyphicon-flag:before {\n content: \"\\e034\";\n}\n.glyphicon-headphones:before {\n content: \"\\e035\";\n}\n.glyphicon-volume-off:before {\n content: \"\\e036\";\n}\n.glyphicon-volume-down:before {\n content: \"\\e037\";\n}\n.glyphicon-volume-up:before {\n content: \"\\e038\";\n}\n.glyphicon-qrcode:before {\n content: \"\\e039\";\n}\n.glyphicon-barcode:before {\n content: \"\\e040\";\n}\n.glyphicon-tag:before {\n content: \"\\e041\";\n}\n.glyphicon-tags:before {\n content: \"\\e042\";\n}\n.glyphicon-book:before {\n content: \"\\e043\";\n}\n.glyphicon-bookmark:before {\n content: \"\\e044\";\n}\n.glyphicon-print:before {\n content: \"\\e045\";\n}\n.glyphicon-camera:before {\n content: \"\\e046\";\n}\n.glyphicon-font:before {\n content: \"\\e047\";\n}\n.glyphicon-bold:before {\n content: \"\\e048\";\n}\n.glyphicon-italic:before {\n content: \"\\e049\";\n}\n.glyphicon-text-height:before {\n content: \"\\e050\";\n}\n.glyphicon-text-width:before {\n content: \"\\e051\";\n}\n.glyphicon-align-left:before {\n content: \"\\e052\";\n}\n.glyphicon-align-center:before {\n content: \"\\e053\";\n}\n.glyphicon-align-right:before {\n content: \"\\e054\";\n}\n.glyphicon-align-justify:before {\n content: \"\\e055\";\n}\n.glyphicon-list:before {\n content: \"\\e056\";\n}\n.glyphicon-indent-left:before {\n content: \"\\e057\";\n}\n.glyphicon-indent-right:before {\n content: \"\\e058\";\n}\n.glyphicon-facetime-video:before {\n content: \"\\e059\";\n}\n.glyphicon-picture:before {\n content: \"\\e060\";\n}\n.glyphicon-map-marker:before {\n content: \"\\e062\";\n}\n.glyphicon-adjust:before {\n content: \"\\e063\";\n}\n.glyphicon-tint:before {\n content: \"\\e064\";\n}\n.glyphicon-edit:before {\n content: \"\\e065\";\n}\n.glyphicon-share:before {\n content: \"\\e066\";\n}\n.glyphicon-check:before {\n content: \"\\e067\";\n}\n.glyphicon-move:before {\n content: \"\\e068\";\n}\n.glyphicon-step-backward:before {\n content: \"\\e069\";\n}\n.glyphicon-fast-backward:before {\n content: \"\\e070\";\n}\n.glyphicon-backward:before {\n content: \"\\e071\";\n}\n.glyphicon-play:before {\n content: \"\\e072\";\n}\n.glyphicon-pause:before {\n content: \"\\e073\";\n}\n.glyphicon-stop:before {\n content: \"\\e074\";\n}\n.glyphicon-forward:before {\n content: \"\\e075\";\n}\n.glyphicon-fast-forward:before {\n content: \"\\e076\";\n}\n.glyphicon-step-forward:before {\n content: \"\\e077\";\n}\n.glyphicon-eject:before {\n content: \"\\e078\";\n}\n.glyphicon-chevron-left:before {\n content: \"\\e079\";\n}\n.glyphicon-chevron-right:before {\n content: \"\\e080\";\n}\n.glyphicon-plus-sign:before {\n content: \"\\e081\";\n}\n.glyphicon-minus-sign:before {\n content: \"\\e082\";\n}\n.glyphicon-remove-sign:before {\n content: \"\\e083\";\n}\n.glyphicon-ok-sign:before {\n content: \"\\e084\";\n}\n.glyphicon-question-sign:before {\n content: \"\\e085\";\n}\n.glyphicon-info-sign:before {\n content: \"\\e086\";\n}\n.glyphicon-screenshot:before {\n content: \"\\e087\";\n}\n.glyphicon-remove-circle:before {\n content: \"\\e088\";\n}\n.glyphicon-ok-circle:before {\n content: \"\\e089\";\n}\n.glyphicon-ban-circle:before {\n content: \"\\e090\";\n}\n.glyphicon-arrow-left:before {\n content: \"\\e091\";\n}\n.glyphicon-arrow-right:before {\n content: \"\\e092\";\n}\n.glyphicon-arrow-up:before {\n content: \"\\e093\";\n}\n.glyphicon-arrow-down:before {\n content: \"\\e094\";\n}\n.glyphicon-share-alt:before {\n content: \"\\e095\";\n}\n.glyphicon-resize-full:before {\n content: \"\\e096\";\n}\n.glyphicon-resize-small:before {\n content: \"\\e097\";\n}\n.glyphicon-exclamation-sign:before {\n content: \"\\e101\";\n}\n.glyphicon-gift:before {\n content: \"\\e102\";\n}\n.glyphicon-leaf:before {\n content: \"\\e103\";\n}\n.glyphicon-fire:before {\n content: \"\\e104\";\n}\n.glyphicon-eye-open:before {\n content: \"\\e105\";\n}\n.glyphicon-eye-close:before {\n content: \"\\e106\";\n}\n.glyphicon-warning-sign:before {\n content: \"\\e107\";\n}\n.glyphicon-plane:before {\n content: \"\\e108\";\n}\n.glyphicon-calendar:before {\n content: \"\\e109\";\n}\n.glyphicon-random:before {\n content: \"\\e110\";\n}\n.glyphicon-comment:before {\n content: \"\\e111\";\n}\n.glyphicon-magnet:before {\n content: \"\\e112\";\n}\n.glyphicon-chevron-up:before {\n content: \"\\e113\";\n}\n.glyphicon-chevron-down:before {\n content: \"\\e114\";\n}\n.glyphicon-retweet:before {\n content: \"\\e115\";\n}\n.glyphicon-shopping-cart:before {\n content: \"\\e116\";\n}\n.glyphicon-folder-close:before {\n content: \"\\e117\";\n}\n.glyphicon-folder-open:before {\n content: \"\\e118\";\n}\n.glyphicon-resize-vertical:before {\n content: \"\\e119\";\n}\n.glyphicon-resize-horizontal:before {\n content: \"\\e120\";\n}\n.glyphicon-hdd:before {\n content: \"\\e121\";\n}\n.glyphicon-bullhorn:before {\n content: \"\\e122\";\n}\n.glyphicon-bell:before {\n content: \"\\e123\";\n}\n.glyphicon-certificate:before {\n content: \"\\e124\";\n}\n.glyphicon-thumbs-up:before {\n content: \"\\e125\";\n}\n.glyphicon-thumbs-down:before {\n content: \"\\e126\";\n}\n.glyphicon-hand-right:before {\n content: \"\\e127\";\n}\n.glyphicon-hand-left:before {\n content: \"\\e128\";\n}\n.glyphicon-hand-up:before {\n content: \"\\e129\";\n}\n.glyphicon-hand-down:before {\n content: \"\\e130\";\n}\n.glyphicon-circle-arrow-right:before {\n content: \"\\e131\";\n}\n.glyphicon-circle-arrow-left:before {\n content: \"\\e132\";\n}\n.glyphicon-circle-arrow-up:before {\n content: \"\\e133\";\n}\n.glyphicon-circle-arrow-down:before {\n content: \"\\e134\";\n}\n.glyphicon-globe:before {\n content: \"\\e135\";\n}\n.glyphicon-wrench:before {\n content: \"\\e136\";\n}\n.glyphicon-tasks:before {\n content: \"\\e137\";\n}\n.glyphicon-filter:before {\n content: \"\\e138\";\n}\n.glyphicon-briefcase:before {\n content: \"\\e139\";\n}\n.glyphicon-fullscreen:before {\n content: \"\\e140\";\n}\n.glyphicon-dashboard:before {\n content: \"\\e141\";\n}\n.glyphicon-paperclip:before {\n content: \"\\e142\";\n}\n.glyphicon-heart-empty:before {\n content: \"\\e143\";\n}\n.glyphicon-link:before {\n content: \"\\e144\";\n}\n.glyphicon-phone:before {\n content: \"\\e145\";\n}\n.glyphicon-pushpin:before {\n content: \"\\e146\";\n}\n.glyphicon-usd:before {\n content: \"\\e148\";\n}\n.glyphicon-gbp:before {\n content: \"\\e149\";\n}\n.glyphicon-sort:before {\n content: \"\\e150\";\n}\n.glyphicon-sort-by-alphabet:before {\n content: \"\\e151\";\n}\n.glyphicon-sort-by-alphabet-alt:before {\n content: \"\\e152\";\n}\n.glyphicon-sort-by-order:before {\n content: \"\\e153\";\n}\n.glyphicon-sort-by-order-alt:before {\n content: \"\\e154\";\n}\n.glyphicon-sort-by-attributes:before {\n content: \"\\e155\";\n}\n.glyphicon-sort-by-attributes-alt:before {\n content: \"\\e156\";\n}\n.glyphicon-unchecked:before {\n content: \"\\e157\";\n}\n.glyphicon-expand:before {\n content: \"\\e158\";\n}\n.glyphicon-collapse-down:before {\n content: \"\\e159\";\n}\n.glyphicon-collapse-up:before {\n content: \"\\e160\";\n}\n.glyphicon-log-in:before {\n content: \"\\e161\";\n}\n.glyphicon-flash:before {\n content: \"\\e162\";\n}\n.glyphicon-log-out:before {\n content: \"\\e163\";\n}\n.glyphicon-new-window:before {\n content: \"\\e164\";\n}\n.glyphicon-record:before {\n content: \"\\e165\";\n}\n.glyphicon-save:before {\n content: \"\\e166\";\n}\n.glyphicon-open:before {\n content: \"\\e167\";\n}\n.glyphicon-saved:before {\n content: \"\\e168\";\n}\n.glyphicon-import:before {\n content: \"\\e169\";\n}\n.glyphicon-export:before {\n content: \"\\e170\";\n}\n.glyphicon-send:before {\n content: \"\\e171\";\n}\n.glyphicon-floppy-disk:before {\n content: \"\\e172\";\n}\n.glyphicon-floppy-saved:before {\n content: \"\\e173\";\n}\n.glyphicon-floppy-remove:before {\n content: \"\\e174\";\n}\n.glyphicon-floppy-save:before {\n content: \"\\e175\";\n}\n.glyphicon-floppy-open:before {\n content: \"\\e176\";\n}\n.glyphicon-credit-card:before {\n content: \"\\e177\";\n}\n.glyphicon-transfer:before {\n content: \"\\e178\";\n}\n.glyphicon-cutlery:before {\n content: \"\\e179\";\n}\n.glyphicon-header:before {\n content: \"\\e180\";\n}\n.glyphicon-compressed:before {\n content: \"\\e181\";\n}\n.glyphicon-earphone:before {\n content: \"\\e182\";\n}\n.glyphicon-phone-alt:before {\n content: \"\\e183\";\n}\n.glyphicon-tower:before {\n content: \"\\e184\";\n}\n.glyphicon-stats:before {\n content: \"\\e185\";\n}\n.glyphicon-sd-video:before {\n content: \"\\e186\";\n}\n.glyphicon-hd-video:before {\n content: \"\\e187\";\n}\n.glyphicon-subtitles:before {\n content: \"\\e188\";\n}\n.glyphicon-sound-stereo:before {\n content: \"\\e189\";\n}\n.glyphicon-sound-dolby:before {\n content: \"\\e190\";\n}\n.glyphicon-sound-5-1:before {\n content: \"\\e191\";\n}\n.glyphicon-sound-6-1:before {\n content: \"\\e192\";\n}\n.glyphicon-sound-7-1:before {\n content: \"\\e193\";\n}\n.glyphicon-copyright-mark:before {\n content: \"\\e194\";\n}\n.glyphicon-registration-mark:before {\n content: \"\\e195\";\n}\n.glyphicon-cloud-download:before {\n content: \"\\e197\";\n}\n.glyphicon-cloud-upload:before {\n content: \"\\e198\";\n}\n.glyphicon-tree-conifer:before {\n content: \"\\e199\";\n}\n.glyphicon-tree-deciduous:before {\n content: \"\\e200\";\n}\n.glyphicon-cd:before {\n content: \"\\e201\";\n}\n.glyphicon-save-file:before {\n content: \"\\e202\";\n}\n.glyphicon-open-file:before {\n content: \"\\e203\";\n}\n.glyphicon-level-up:before {\n content: \"\\e204\";\n}\n.glyphicon-copy:before {\n content: \"\\e205\";\n}\n.glyphicon-paste:before {\n content: \"\\e206\";\n}\n.glyphicon-alert:before {\n content: \"\\e209\";\n}\n.glyphicon-equalizer:before {\n content: \"\\e210\";\n}\n.glyphicon-king:before {\n content: \"\\e211\";\n}\n.glyphicon-queen:before {\n content: \"\\e212\";\n}\n.glyphicon-pawn:before {\n content: \"\\e213\";\n}\n.glyphicon-bishop:before {\n content: \"\\e214\";\n}\n.glyphicon-knight:before {\n content: \"\\e215\";\n}\n.glyphicon-baby-formula:before {\n content: \"\\e216\";\n}\n.glyphicon-tent:before {\n content: \"\\26fa\";\n}\n.glyphicon-blackboard:before {\n content: \"\\e218\";\n}\n.glyphicon-bed:before {\n content: \"\\e219\";\n}\n.glyphicon-apple:before {\n content: \"\\f8ff\";\n}\n.glyphicon-erase:before {\n content: \"\\e221\";\n}\n.glyphicon-hourglass:before {\n content: \"\\231b\";\n}\n.glyphicon-lamp:before {\n content: \"\\e223\";\n}\n.glyphicon-duplicate:before {\n content: \"\\e224\";\n}\n.glyphicon-piggy-bank:before {\n content: \"\\e225\";\n}\n.glyphicon-scissors:before {\n content: \"\\e226\";\n}\n.glyphicon-bitcoin:before {\n content: \"\\e227\";\n}\n.glyphicon-btc:before {\n content: \"\\e227\";\n}\n.glyphicon-xbt:before {\n content: \"\\e227\";\n}\n.glyphicon-yen:before {\n content: \"\\00a5\";\n}\n.glyphicon-jpy:before {\n content: \"\\00a5\";\n}\n.glyphicon-ruble:before {\n content: \"\\20bd\";\n}\n.glyphicon-rub:before {\n content: \"\\20bd\";\n}\n.glyphicon-scale:before {\n content: \"\\e230\";\n}\n.glyphicon-ice-lolly:before {\n content: \"\\e231\";\n}\n.glyphicon-ice-lolly-tasted:before {\n content: \"\\e232\";\n}\n.glyphicon-education:before {\n content: \"\\e233\";\n}\n.glyphicon-option-horizontal:before {\n content: \"\\e234\";\n}\n.glyphicon-option-vertical:before {\n content: \"\\e235\";\n}\n.glyphicon-menu-hamburger:before {\n content: \"\\e236\";\n}\n.glyphicon-modal-window:before {\n content: \"\\e237\";\n}\n.glyphicon-oil:before {\n content: \"\\e238\";\n}\n.glyphicon-grain:before {\n content: \"\\e239\";\n}\n.glyphicon-sunglasses:before {\n content: \"\\e240\";\n}\n.glyphicon-text-size:before {\n content: \"\\e241\";\n}\n.glyphicon-text-color:before {\n content: \"\\e242\";\n}\n.glyphicon-text-background:before {\n content: \"\\e243\";\n}\n.glyphicon-object-align-top:before {\n content: \"\\e244\";\n}\n.glyphicon-object-align-bottom:before {\n content: \"\\e245\";\n}\n.glyphicon-object-align-horizontal:before {\n content: \"\\e246\";\n}\n.glyphicon-object-align-left:before {\n content: \"\\e247\";\n}\n.glyphicon-object-align-vertical:before {\n content: \"\\e248\";\n}\n.glyphicon-object-align-right:before {\n content: \"\\e249\";\n}\n.glyphicon-triangle-right:before {\n content: \"\\e250\";\n}\n.glyphicon-triangle-left:before {\n content: \"\\e251\";\n}\n.glyphicon-triangle-bottom:before {\n content: \"\\e252\";\n}\n.glyphicon-triangle-top:before {\n content: \"\\e253\";\n}\n.glyphicon-console:before {\n content: \"\\e254\";\n}\n.glyphicon-superscript:before {\n content: \"\\e255\";\n}\n.glyphicon-subscript:before {\n content: \"\\e256\";\n}\n.glyphicon-menu-left:before {\n content: \"\\e257\";\n}\n.glyphicon-menu-right:before {\n content: \"\\e258\";\n}\n.glyphicon-menu-down:before {\n content: \"\\e259\";\n}\n.glyphicon-menu-up:before {\n content: \"\\e260\";\n}\n* {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\n*:before,\n*:after {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\nhtml {\n font-size: 10px;\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\nbody {\n font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n font-size: 14px;\n line-height: 1.42857143;\n color: #333333;\n background-color: #fff;\n}\ninput,\nbutton,\nselect,\ntextarea {\n font-family: inherit;\n font-size: inherit;\n line-height: inherit;\n}\na {\n color: #337ab7;\n text-decoration: none;\n}\na:hover,\na:focus {\n color: #23527c;\n text-decoration: underline;\n}\na:focus {\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\nfigure {\n margin: 0;\n}\nimg {\n vertical-align: middle;\n}\n.img-responsive,\n.thumbnail > img,\n.thumbnail a > img,\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n display: block;\n max-width: 100%;\n height: auto;\n}\n.img-rounded {\n border-radius: 6px;\n}\n.img-thumbnail {\n padding: 4px;\n line-height: 1.42857143;\n background-color: #fff;\n border: 1px solid #ddd;\n border-radius: 4px;\n -webkit-transition: all 0.2s ease-in-out;\n -o-transition: all 0.2s ease-in-out;\n transition: all 0.2s ease-in-out;\n display: inline-block;\n max-width: 100%;\n height: auto;\n}\n.img-circle {\n border-radius: 50%;\n}\nhr {\n margin-top: 20px;\n margin-bottom: 20px;\n border: 0;\n border-top: 1px solid #eeeeee;\n}\n.sr-only {\n position: absolute;\n width: 1px;\n height: 1px;\n margin: -1px;\n padding: 0;\n overflow: hidden;\n clip: rect(0, 0, 0, 0);\n border: 0;\n}\n.sr-only-focusable:active,\n.sr-only-focusable:focus {\n position: static;\n width: auto;\n height: auto;\n margin: 0;\n overflow: visible;\n clip: auto;\n}\n[role=\"button\"] {\n cursor: pointer;\n}\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\n.h1,\n.h2,\n.h3,\n.h4,\n.h5,\n.h6 {\n font-family: inherit;\n font-weight: 500;\n line-height: 1.1;\n color: inherit;\n}\nh1 small,\nh2 small,\nh3 small,\nh4 small,\nh5 small,\nh6 small,\n.h1 small,\n.h2 small,\n.h3 small,\n.h4 small,\n.h5 small,\n.h6 small,\nh1 .small,\nh2 .small,\nh3 .small,\nh4 .small,\nh5 .small,\nh6 .small,\n.h1 .small,\n.h2 .small,\n.h3 .small,\n.h4 .small,\n.h5 .small,\n.h6 .small {\n font-weight: normal;\n line-height: 1;\n color: #777777;\n}\nh1,\n.h1,\nh2,\n.h2,\nh3,\n.h3 {\n margin-top: 20px;\n margin-bottom: 10px;\n}\nh1 small,\n.h1 small,\nh2 small,\n.h2 small,\nh3 small,\n.h3 small,\nh1 .small,\n.h1 .small,\nh2 .small,\n.h2 .small,\nh3 .small,\n.h3 .small {\n font-size: 65%;\n}\nh4,\n.h4,\nh5,\n.h5,\nh6,\n.h6 {\n margin-top: 10px;\n margin-bottom: 10px;\n}\nh4 small,\n.h4 small,\nh5 small,\n.h5 small,\nh6 small,\n.h6 small,\nh4 .small,\n.h4 .small,\nh5 .small,\n.h5 .small,\nh6 .small,\n.h6 .small {\n font-size: 75%;\n}\nh1,\n.h1 {\n font-size: 36px;\n}\nh2,\n.h2 {\n font-size: 30px;\n}\nh3,\n.h3 {\n font-size: 24px;\n}\nh4,\n.h4 {\n font-size: 18px;\n}\nh5,\n.h5 {\n font-size: 14px;\n}\nh6,\n.h6 {\n font-size: 12px;\n}\np {\n margin: 0 0 10px;\n}\n.lead {\n margin-bottom: 20px;\n font-size: 16px;\n font-weight: 300;\n line-height: 1.4;\n}\n@media (min-width: 768px) {\n .lead {\n font-size: 21px;\n }\n}\nsmall,\n.small {\n font-size: 85%;\n}\nmark,\n.mark {\n background-color: #fcf8e3;\n padding: .2em;\n}\n.text-left {\n text-align: left;\n}\n.text-right {\n text-align: right;\n}\n.text-center {\n text-align: center;\n}\n.text-justify {\n text-align: justify;\n}\n.text-nowrap {\n white-space: nowrap;\n}\n.text-lowercase {\n text-transform: lowercase;\n}\n.text-uppercase {\n text-transform: uppercase;\n}\n.text-capitalize {\n text-transform: capitalize;\n}\n.text-muted {\n color: #777777;\n}\n.text-primary {\n color: #337ab7;\n}\na.text-primary:hover,\na.text-primary:focus {\n color: #286090;\n}\n.text-success {\n color: #3c763d;\n}\na.text-success:hover,\na.text-success:focus {\n color: #2b542c;\n}\n.text-info {\n color: #31708f;\n}\na.text-info:hover,\na.text-info:focus {\n color: #245269;\n}\n.text-warning {\n color: #8a6d3b;\n}\na.text-warning:hover,\na.text-warning:focus {\n color: #66512c;\n}\n.text-danger {\n color: #a94442;\n}\na.text-danger:hover,\na.text-danger:focus {\n color: #843534;\n}\n.bg-primary {\n color: #fff;\n background-color: #337ab7;\n}\na.bg-primary:hover,\na.bg-primary:focus {\n background-color: #286090;\n}\n.bg-success {\n background-color: #dff0d8;\n}\na.bg-success:hover,\na.bg-success:focus {\n background-color: #c1e2b3;\n}\n.bg-info {\n background-color: #d9edf7;\n}\na.bg-info:hover,\na.bg-info:focus {\n background-color: #afd9ee;\n}\n.bg-warning {\n background-color: #fcf8e3;\n}\na.bg-warning:hover,\na.bg-warning:focus {\n background-color: #f7ecb5;\n}\n.bg-danger {\n background-color: #f2dede;\n}\na.bg-danger:hover,\na.bg-danger:focus {\n background-color: #e4b9b9;\n}\n.page-header {\n padding-bottom: 9px;\n margin: 40px 0 20px;\n border-bottom: 1px solid #eeeeee;\n}\nul,\nol {\n margin-top: 0;\n margin-bottom: 10px;\n}\nul ul,\nol ul,\nul ol,\nol ol {\n margin-bottom: 0;\n}\n.list-unstyled {\n padding-left: 0;\n list-style: none;\n}\n.list-inline {\n padding-left: 0;\n list-style: none;\n margin-left: -5px;\n}\n.list-inline > li {\n display: inline-block;\n padding-left: 5px;\n padding-right: 5px;\n}\ndl {\n margin-top: 0;\n margin-bottom: 20px;\n}\ndt,\ndd {\n line-height: 1.42857143;\n}\ndt {\n font-weight: bold;\n}\ndd {\n margin-left: 0;\n}\n@media (min-width: 768px) {\n .dl-horizontal dt {\n float: left;\n width: 160px;\n clear: left;\n text-align: right;\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n }\n .dl-horizontal dd {\n margin-left: 180px;\n }\n}\nabbr[title],\nabbr[data-original-title] {\n cursor: help;\n border-bottom: 1px dotted #777777;\n}\n.initialism {\n font-size: 90%;\n text-transform: uppercase;\n}\nblockquote {\n padding: 10px 20px;\n margin: 0 0 20px;\n font-size: 17.5px;\n border-left: 5px solid #eeeeee;\n}\nblockquote p:last-child,\nblockquote ul:last-child,\nblockquote ol:last-child {\n margin-bottom: 0;\n}\nblockquote footer,\nblockquote small,\nblockquote .small {\n display: block;\n font-size: 80%;\n line-height: 1.42857143;\n color: #777777;\n}\nblockquote footer:before,\nblockquote small:before,\nblockquote .small:before {\n content: '\\2014 \\00A0';\n}\n.blockquote-reverse,\nblockquote.pull-right {\n padding-right: 15px;\n padding-left: 0;\n border-right: 5px solid #eeeeee;\n border-left: 0;\n text-align: right;\n}\n.blockquote-reverse footer:before,\nblockquote.pull-right footer:before,\n.blockquote-reverse small:before,\nblockquote.pull-right small:before,\n.blockquote-reverse .small:before,\nblockquote.pull-right .small:before {\n content: '';\n}\n.blockquote-reverse footer:after,\nblockquote.pull-right footer:after,\n.blockquote-reverse small:after,\nblockquote.pull-right small:after,\n.blockquote-reverse .small:after,\nblockquote.pull-right .small:after {\n content: '\\00A0 \\2014';\n}\naddress {\n margin-bottom: 20px;\n font-style: normal;\n line-height: 1.42857143;\n}\ncode,\nkbd,\npre,\nsamp {\n font-family: Menlo, Monaco, Consolas, \"Courier New\", monospace;\n}\ncode {\n padding: 2px 4px;\n font-size: 90%;\n color: #c7254e;\n background-color: #f9f2f4;\n border-radius: 4px;\n}\nkbd {\n padding: 2px 4px;\n font-size: 90%;\n color: #fff;\n background-color: #333;\n border-radius: 3px;\n box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\nkbd kbd {\n padding: 0;\n font-size: 100%;\n font-weight: bold;\n box-shadow: none;\n}\npre {\n display: block;\n padding: 9.5px;\n margin: 0 0 10px;\n font-size: 13px;\n line-height: 1.42857143;\n word-break: break-all;\n word-wrap: break-word;\n color: #333333;\n background-color: #f5f5f5;\n border: 1px solid #ccc;\n border-radius: 4px;\n}\npre code {\n padding: 0;\n font-size: inherit;\n color: inherit;\n white-space: pre-wrap;\n background-color: transparent;\n border-radius: 0;\n}\n.pre-scrollable {\n max-height: 340px;\n overflow-y: scroll;\n}\n.container {\n margin-right: auto;\n margin-left: auto;\n padding-left: 15px;\n padding-right: 15px;\n}\n@media (min-width: 768px) {\n .container {\n width: 750px;\n }\n}\n@media (min-width: 992px) {\n .container {\n width: 970px;\n }\n}\n@media (min-width: 1200px) {\n .container {\n width: 1170px;\n }\n}\n.container-fluid {\n margin-right: auto;\n margin-left: auto;\n padding-left: 15px;\n padding-right: 15px;\n}\n.row {\n margin-left: -15px;\n margin-right: -15px;\n}\n.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {\n position: relative;\n min-height: 1px;\n padding-left: 15px;\n padding-right: 15px;\n}\n.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {\n float: left;\n}\n.col-xs-12 {\n width: 100%;\n}\n.col-xs-11 {\n width: 91.66666667%;\n}\n.col-xs-10 {\n width: 83.33333333%;\n}\n.col-xs-9 {\n width: 75%;\n}\n.col-xs-8 {\n width: 66.66666667%;\n}\n.col-xs-7 {\n width: 58.33333333%;\n}\n.col-xs-6 {\n width: 50%;\n}\n.col-xs-5 {\n width: 41.66666667%;\n}\n.col-xs-4 {\n width: 33.33333333%;\n}\n.col-xs-3 {\n width: 25%;\n}\n.col-xs-2 {\n width: 16.66666667%;\n}\n.col-xs-1 {\n width: 8.33333333%;\n}\n.col-xs-pull-12 {\n right: 100%;\n}\n.col-xs-pull-11 {\n right: 91.66666667%;\n}\n.col-xs-pull-10 {\n right: 83.33333333%;\n}\n.col-xs-pull-9 {\n right: 75%;\n}\n.col-xs-pull-8 {\n right: 66.66666667%;\n}\n.col-xs-pull-7 {\n right: 58.33333333%;\n}\n.col-xs-pull-6 {\n right: 50%;\n}\n.col-xs-pull-5 {\n right: 41.66666667%;\n}\n.col-xs-pull-4 {\n right: 33.33333333%;\n}\n.col-xs-pull-3 {\n right: 25%;\n}\n.col-xs-pull-2 {\n right: 16.66666667%;\n}\n.col-xs-pull-1 {\n right: 8.33333333%;\n}\n.col-xs-pull-0 {\n right: auto;\n}\n.col-xs-push-12 {\n left: 100%;\n}\n.col-xs-push-11 {\n left: 91.66666667%;\n}\n.col-xs-push-10 {\n left: 83.33333333%;\n}\n.col-xs-push-9 {\n left: 75%;\n}\n.col-xs-push-8 {\n left: 66.66666667%;\n}\n.col-xs-push-7 {\n left: 58.33333333%;\n}\n.col-xs-push-6 {\n left: 50%;\n}\n.col-xs-push-5 {\n left: 41.66666667%;\n}\n.col-xs-push-4 {\n left: 33.33333333%;\n}\n.col-xs-push-3 {\n left: 25%;\n}\n.col-xs-push-2 {\n left: 16.66666667%;\n}\n.col-xs-push-1 {\n left: 8.33333333%;\n}\n.col-xs-push-0 {\n left: auto;\n}\n.col-xs-offset-12 {\n margin-left: 100%;\n}\n.col-xs-offset-11 {\n margin-left: 91.66666667%;\n}\n.col-xs-offset-10 {\n margin-left: 83.33333333%;\n}\n.col-xs-offset-9 {\n margin-left: 75%;\n}\n.col-xs-offset-8 {\n margin-left: 66.66666667%;\n}\n.col-xs-offset-7 {\n margin-left: 58.33333333%;\n}\n.col-xs-offset-6 {\n margin-left: 50%;\n}\n.col-xs-offset-5 {\n margin-left: 41.66666667%;\n}\n.col-xs-offset-4 {\n margin-left: 33.33333333%;\n}\n.col-xs-offset-3 {\n margin-left: 25%;\n}\n.col-xs-offset-2 {\n margin-left: 16.66666667%;\n}\n.col-xs-offset-1 {\n margin-left: 8.33333333%;\n}\n.col-xs-offset-0 {\n margin-left: 0%;\n}\n@media (min-width: 768px) {\n .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {\n float: left;\n }\n .col-sm-12 {\n width: 100%;\n }\n .col-sm-11 {\n width: 91.66666667%;\n }\n .col-sm-10 {\n width: 83.33333333%;\n }\n .col-sm-9 {\n width: 75%;\n }\n .col-sm-8 {\n width: 66.66666667%;\n }\n .col-sm-7 {\n width: 58.33333333%;\n }\n .col-sm-6 {\n width: 50%;\n }\n .col-sm-5 {\n width: 41.66666667%;\n }\n .col-sm-4 {\n width: 33.33333333%;\n }\n .col-sm-3 {\n width: 25%;\n }\n .col-sm-2 {\n width: 16.66666667%;\n }\n .col-sm-1 {\n width: 8.33333333%;\n }\n .col-sm-pull-12 {\n right: 100%;\n }\n .col-sm-pull-11 {\n right: 91.66666667%;\n }\n .col-sm-pull-10 {\n right: 83.33333333%;\n }\n .col-sm-pull-9 {\n right: 75%;\n }\n .col-sm-pull-8 {\n right: 66.66666667%;\n }\n .col-sm-pull-7 {\n right: 58.33333333%;\n }\n .col-sm-pull-6 {\n right: 50%;\n }\n .col-sm-pull-5 {\n right: 41.66666667%;\n }\n .col-sm-pull-4 {\n right: 33.33333333%;\n }\n .col-sm-pull-3 {\n right: 25%;\n }\n .col-sm-pull-2 {\n right: 16.66666667%;\n }\n .col-sm-pull-1 {\n right: 8.33333333%;\n }\n .col-sm-pull-0 {\n right: auto;\n }\n .col-sm-push-12 {\n left: 100%;\n }\n .col-sm-push-11 {\n left: 91.66666667%;\n }\n .col-sm-push-10 {\n left: 83.33333333%;\n }\n .col-sm-push-9 {\n left: 75%;\n }\n .col-sm-push-8 {\n left: 66.66666667%;\n }\n .col-sm-push-7 {\n left: 58.33333333%;\n }\n .col-sm-push-6 {\n left: 50%;\n }\n .col-sm-push-5 {\n left: 41.66666667%;\n }\n .col-sm-push-4 {\n left: 33.33333333%;\n }\n .col-sm-push-3 {\n left: 25%;\n }\n .col-sm-push-2 {\n left: 16.66666667%;\n }\n .col-sm-push-1 {\n left: 8.33333333%;\n }\n .col-sm-push-0 {\n left: auto;\n }\n .col-sm-offset-12 {\n margin-left: 100%;\n }\n .col-sm-offset-11 {\n margin-left: 91.66666667%;\n }\n .col-sm-offset-10 {\n margin-left: 83.33333333%;\n }\n .col-sm-offset-9 {\n margin-left: 75%;\n }\n .col-sm-offset-8 {\n margin-left: 66.66666667%;\n }\n .col-sm-offset-7 {\n margin-left: 58.33333333%;\n }\n .col-sm-offset-6 {\n margin-left: 50%;\n }\n .col-sm-offset-5 {\n margin-left: 41.66666667%;\n }\n .col-sm-offset-4 {\n margin-left: 33.33333333%;\n }\n .col-sm-offset-3 {\n margin-left: 25%;\n }\n .col-sm-offset-2 {\n margin-left: 16.66666667%;\n }\n .col-sm-offset-1 {\n margin-left: 8.33333333%;\n }\n .col-sm-offset-0 {\n margin-left: 0%;\n }\n}\n@media (min-width: 992px) {\n .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {\n float: left;\n }\n .col-md-12 {\n width: 100%;\n }\n .col-md-11 {\n width: 91.66666667%;\n }\n .col-md-10 {\n width: 83.33333333%;\n }\n .col-md-9 {\n width: 75%;\n }\n .col-md-8 {\n width: 66.66666667%;\n }\n .col-md-7 {\n width: 58.33333333%;\n }\n .col-md-6 {\n width: 50%;\n }\n .col-md-5 {\n width: 41.66666667%;\n }\n .col-md-4 {\n width: 33.33333333%;\n }\n .col-md-3 {\n width: 25%;\n }\n .col-md-2 {\n width: 16.66666667%;\n }\n .col-md-1 {\n width: 8.33333333%;\n }\n .col-md-pull-12 {\n right: 100%;\n }\n .col-md-pull-11 {\n right: 91.66666667%;\n }\n .col-md-pull-10 {\n right: 83.33333333%;\n }\n .col-md-pull-9 {\n right: 75%;\n }\n .col-md-pull-8 {\n right: 66.66666667%;\n }\n .col-md-pull-7 {\n right: 58.33333333%;\n }\n .col-md-pull-6 {\n right: 50%;\n }\n .col-md-pull-5 {\n right: 41.66666667%;\n }\n .col-md-pull-4 {\n right: 33.33333333%;\n }\n .col-md-pull-3 {\n right: 25%;\n }\n .col-md-pull-2 {\n right: 16.66666667%;\n }\n .col-md-pull-1 {\n right: 8.33333333%;\n }\n .col-md-pull-0 {\n right: auto;\n }\n .col-md-push-12 {\n left: 100%;\n }\n .col-md-push-11 {\n left: 91.66666667%;\n }\n .col-md-push-10 {\n left: 83.33333333%;\n }\n .col-md-push-9 {\n left: 75%;\n }\n .col-md-push-8 {\n left: 66.66666667%;\n }\n .col-md-push-7 {\n left: 58.33333333%;\n }\n .col-md-push-6 {\n left: 50%;\n }\n .col-md-push-5 {\n left: 41.66666667%;\n }\n .col-md-push-4 {\n left: 33.33333333%;\n }\n .col-md-push-3 {\n left: 25%;\n }\n .col-md-push-2 {\n left: 16.66666667%;\n }\n .col-md-push-1 {\n left: 8.33333333%;\n }\n .col-md-push-0 {\n left: auto;\n }\n .col-md-offset-12 {\n margin-left: 100%;\n }\n .col-md-offset-11 {\n margin-left: 91.66666667%;\n }\n .col-md-offset-10 {\n margin-left: 83.33333333%;\n }\n .col-md-offset-9 {\n margin-left: 75%;\n }\n .col-md-offset-8 {\n margin-left: 66.66666667%;\n }\n .col-md-offset-7 {\n margin-left: 58.33333333%;\n }\n .col-md-offset-6 {\n margin-left: 50%;\n }\n .col-md-offset-5 {\n margin-left: 41.66666667%;\n }\n .col-md-offset-4 {\n margin-left: 33.33333333%;\n }\n .col-md-offset-3 {\n margin-left: 25%;\n }\n .col-md-offset-2 {\n margin-left: 16.66666667%;\n }\n .col-md-offset-1 {\n margin-left: 8.33333333%;\n }\n .col-md-offset-0 {\n margin-left: 0%;\n }\n}\n@media (min-width: 1200px) {\n .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {\n float: left;\n }\n .col-lg-12 {\n width: 100%;\n }\n .col-lg-11 {\n width: 91.66666667%;\n }\n .col-lg-10 {\n width: 83.33333333%;\n }\n .col-lg-9 {\n width: 75%;\n }\n .col-lg-8 {\n width: 66.66666667%;\n }\n .col-lg-7 {\n width: 58.33333333%;\n }\n .col-lg-6 {\n width: 50%;\n }\n .col-lg-5 {\n width: 41.66666667%;\n }\n .col-lg-4 {\n width: 33.33333333%;\n }\n .col-lg-3 {\n width: 25%;\n }\n .col-lg-2 {\n width: 16.66666667%;\n }\n .col-lg-1 {\n width: 8.33333333%;\n }\n .col-lg-pull-12 {\n right: 100%;\n }\n .col-lg-pull-11 {\n right: 91.66666667%;\n }\n .col-lg-pull-10 {\n right: 83.33333333%;\n }\n .col-lg-pull-9 {\n right: 75%;\n }\n .col-lg-pull-8 {\n right: 66.66666667%;\n }\n .col-lg-pull-7 {\n right: 58.33333333%;\n }\n .col-lg-pull-6 {\n right: 50%;\n }\n .col-lg-pull-5 {\n right: 41.66666667%;\n }\n .col-lg-pull-4 {\n right: 33.33333333%;\n }\n .col-lg-pull-3 {\n right: 25%;\n }\n .col-lg-pull-2 {\n right: 16.66666667%;\n }\n .col-lg-pull-1 {\n right: 8.33333333%;\n }\n .col-lg-pull-0 {\n right: auto;\n }\n .col-lg-push-12 {\n left: 100%;\n }\n .col-lg-push-11 {\n left: 91.66666667%;\n }\n .col-lg-push-10 {\n left: 83.33333333%;\n }\n .col-lg-push-9 {\n left: 75%;\n }\n .col-lg-push-8 {\n left: 66.66666667%;\n }\n .col-lg-push-7 {\n left: 58.33333333%;\n }\n .col-lg-push-6 {\n left: 50%;\n }\n .col-lg-push-5 {\n left: 41.66666667%;\n }\n .col-lg-push-4 {\n left: 33.33333333%;\n }\n .col-lg-push-3 {\n left: 25%;\n }\n .col-lg-push-2 {\n left: 16.66666667%;\n }\n .col-lg-push-1 {\n left: 8.33333333%;\n }\n .col-lg-push-0 {\n left: auto;\n }\n .col-lg-offset-12 {\n margin-left: 100%;\n }\n .col-lg-offset-11 {\n margin-left: 91.66666667%;\n }\n .col-lg-offset-10 {\n margin-left: 83.33333333%;\n }\n .col-lg-offset-9 {\n margin-left: 75%;\n }\n .col-lg-offset-8 {\n margin-left: 66.66666667%;\n }\n .col-lg-offset-7 {\n margin-left: 58.33333333%;\n }\n .col-lg-offset-6 {\n margin-left: 50%;\n }\n .col-lg-offset-5 {\n margin-left: 41.66666667%;\n }\n .col-lg-offset-4 {\n margin-left: 33.33333333%;\n }\n .col-lg-offset-3 {\n margin-left: 25%;\n }\n .col-lg-offset-2 {\n margin-left: 16.66666667%;\n }\n .col-lg-offset-1 {\n margin-left: 8.33333333%;\n }\n .col-lg-offset-0 {\n margin-left: 0%;\n }\n}\ntable {\n background-color: transparent;\n}\ncaption {\n padding-top: 8px;\n padding-bottom: 8px;\n color: #777777;\n text-align: left;\n}\nth {\n text-align: left;\n}\n.table {\n width: 100%;\n max-width: 100%;\n margin-bottom: 20px;\n}\n.table > thead > tr > th,\n.table > tbody > tr > th,\n.table > tfoot > tr > th,\n.table > thead > tr > td,\n.table > tbody > tr > td,\n.table > tfoot > tr > td {\n padding: 8px;\n line-height: 1.42857143;\n vertical-align: top;\n border-top: 1px solid #ddd;\n}\n.table > thead > tr > th {\n vertical-align: bottom;\n border-bottom: 2px solid #ddd;\n}\n.table > caption + thead > tr:first-child > th,\n.table > colgroup + thead > tr:first-child > th,\n.table > thead:first-child > tr:first-child > th,\n.table > caption + thead > tr:first-child > td,\n.table > colgroup + thead > tr:first-child > td,\n.table > thead:first-child > tr:first-child > td {\n border-top: 0;\n}\n.table > tbody + tbody {\n border-top: 2px solid #ddd;\n}\n.table .table {\n background-color: #fff;\n}\n.table-condensed > thead > tr > th,\n.table-condensed > tbody > tr > th,\n.table-condensed > tfoot > tr > th,\n.table-condensed > thead > tr > td,\n.table-condensed > tbody > tr > td,\n.table-condensed > tfoot > tr > td {\n padding: 5px;\n}\n.table-bordered {\n border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > tbody > tr > th,\n.table-bordered > tfoot > tr > th,\n.table-bordered > thead > tr > td,\n.table-bordered > tbody > tr > td,\n.table-bordered > tfoot > tr > td {\n border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > thead > tr > td {\n border-bottom-width: 2px;\n}\n.table-striped > tbody > tr:nth-of-type(odd) {\n background-color: #f9f9f9;\n}\n.table-hover > tbody > tr:hover {\n background-color: #f5f5f5;\n}\ntable col[class*=\"col-\"] {\n position: static;\n float: none;\n display: table-column;\n}\ntable td[class*=\"col-\"],\ntable th[class*=\"col-\"] {\n position: static;\n float: none;\n display: table-cell;\n}\n.table > thead > tr > td.active,\n.table > tbody > tr > td.active,\n.table > tfoot > tr > td.active,\n.table > thead > tr > th.active,\n.table > tbody > tr > th.active,\n.table > tfoot > tr > th.active,\n.table > thead > tr.active > td,\n.table > tbody > tr.active > td,\n.table > tfoot > tr.active > td,\n.table > thead > tr.active > th,\n.table > tbody > tr.active > th,\n.table > tfoot > tr.active > th {\n background-color: #f5f5f5;\n}\n.table-hover > tbody > tr > td.active:hover,\n.table-hover > tbody > tr > th.active:hover,\n.table-hover > tbody > tr.active:hover > td,\n.table-hover > tbody > tr:hover > .active,\n.table-hover > tbody > tr.active:hover > th {\n background-color: #e8e8e8;\n}\n.table > thead > tr > td.success,\n.table > tbody > tr > td.success,\n.table > tfoot > tr > td.success,\n.table > thead > tr > th.success,\n.table > tbody > tr > th.success,\n.table > tfoot > tr > th.success,\n.table > thead > tr.success > td,\n.table > tbody > tr.success > td,\n.table > tfoot > tr.success > td,\n.table > thead > tr.success > th,\n.table > tbody > tr.success > th,\n.table > tfoot > tr.success > th {\n background-color: #dff0d8;\n}\n.table-hover > tbody > tr > td.success:hover,\n.table-hover > tbody > tr > th.success:hover,\n.table-hover > tbody > tr.success:hover > td,\n.table-hover > tbody > tr:hover > .success,\n.table-hover > tbody > tr.success:hover > th {\n background-color: #d0e9c6;\n}\n.table > thead > tr > td.info,\n.table > tbody > tr > td.info,\n.table > tfoot > tr > td.info,\n.table > thead > tr > th.info,\n.table > tbody > tr > th.info,\n.table > tfoot > tr > th.info,\n.table > thead > tr.info > td,\n.table > tbody > tr.info > td,\n.table > tfoot > tr.info > td,\n.table > thead > tr.info > th,\n.table > tbody > tr.info > th,\n.table > tfoot > tr.info > th {\n background-color: #d9edf7;\n}\n.table-hover > tbody > tr > td.info:hover,\n.table-hover > tbody > tr > th.info:hover,\n.table-hover > tbody > tr.info:hover > td,\n.table-hover > tbody > tr:hover > .info,\n.table-hover > tbody > tr.info:hover > th {\n background-color: #c4e3f3;\n}\n.table > thead > tr > td.warning,\n.table > tbody > tr > td.warning,\n.table > tfoot > tr > td.warning,\n.table > thead > tr > th.warning,\n.table > tbody > tr > th.warning,\n.table > tfoot > tr > th.warning,\n.table > thead > tr.warning > td,\n.table > tbody > tr.warning > td,\n.table > tfoot > tr.warning > td,\n.table > thead > tr.warning > th,\n.table > tbody > tr.warning > th,\n.table > tfoot > tr.warning > th {\n background-color: #fcf8e3;\n}\n.table-hover > tbody > tr > td.warning:hover,\n.table-hover > tbody > tr > th.warning:hover,\n.table-hover > tbody > tr.warning:hover > td,\n.table-hover > tbody > tr:hover > .warning,\n.table-hover > tbody > tr.warning:hover > th {\n background-color: #faf2cc;\n}\n.table > thead > tr > td.danger,\n.table > tbody > tr > td.danger,\n.table > tfoot > tr > td.danger,\n.table > thead > tr > th.danger,\n.table > tbody > tr > th.danger,\n.table > tfoot > tr > th.danger,\n.table > thead > tr.danger > td,\n.table > tbody > tr.danger > td,\n.table > tfoot > tr.danger > td,\n.table > thead > tr.danger > th,\n.table > tbody > tr.danger > th,\n.table > tfoot > tr.danger > th {\n background-color: #f2dede;\n}\n.table-hover > tbody > tr > td.danger:hover,\n.table-hover > tbody > tr > th.danger:hover,\n.table-hover > tbody > tr.danger:hover > td,\n.table-hover > tbody > tr:hover > .danger,\n.table-hover > tbody > tr.danger:hover > th {\n background-color: #ebcccc;\n}\n.table-responsive {\n overflow-x: auto;\n min-height: 0.01%;\n}\n@media screen and (max-width: 767px) {\n .table-responsive {\n width: 100%;\n margin-bottom: 15px;\n overflow-y: hidden;\n -ms-overflow-style: -ms-autohiding-scrollbar;\n border: 1px solid #ddd;\n }\n .table-responsive > .table {\n margin-bottom: 0;\n }\n .table-responsive > .table > thead > tr > th,\n .table-responsive > .table > tbody > tr > th,\n .table-responsive > .table > tfoot > tr > th,\n .table-responsive > .table > thead > tr > td,\n .table-responsive > .table > tbody > tr > td,\n .table-responsive > .table > tfoot > tr > td {\n white-space: nowrap;\n }\n .table-responsive > .table-bordered {\n border: 0;\n }\n .table-responsive > .table-bordered > thead > tr > th:first-child,\n .table-responsive > .table-bordered > tbody > tr > th:first-child,\n .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n .table-responsive > .table-bordered > thead > tr > td:first-child,\n .table-responsive > .table-bordered > tbody > tr > td:first-child,\n .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n border-left: 0;\n }\n .table-responsive > .table-bordered > thead > tr > th:last-child,\n .table-responsive > .table-bordered > tbody > tr > th:last-child,\n .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n .table-responsive > .table-bordered > thead > tr > td:last-child,\n .table-responsive > .table-bordered > tbody > tr > td:last-child,\n .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n border-right: 0;\n }\n .table-responsive > .table-bordered > tbody > tr:last-child > th,\n .table-responsive > .table-bordered > tfoot > tr:last-child > th,\n .table-responsive > .table-bordered > tbody > tr:last-child > td,\n .table-responsive > .table-bordered > tfoot > tr:last-child > td {\n border-bottom: 0;\n }\n}\nfieldset {\n padding: 0;\n margin: 0;\n border: 0;\n min-width: 0;\n}\nlegend {\n display: block;\n width: 100%;\n padding: 0;\n margin-bottom: 20px;\n font-size: 21px;\n line-height: inherit;\n color: #333333;\n border: 0;\n border-bottom: 1px solid #e5e5e5;\n}\nlabel {\n display: inline-block;\n max-width: 100%;\n margin-bottom: 5px;\n font-weight: bold;\n}\ninput[type=\"search\"] {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n margin: 4px 0 0;\n margin-top: 1px \\9;\n line-height: normal;\n}\ninput[type=\"file\"] {\n display: block;\n}\ninput[type=\"range\"] {\n display: block;\n width: 100%;\n}\nselect[multiple],\nselect[size] {\n height: auto;\n}\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\noutput {\n display: block;\n padding-top: 7px;\n font-size: 14px;\n line-height: 1.42857143;\n color: #555555;\n}\n.form-control {\n display: block;\n width: 100%;\n height: 34px;\n padding: 6px 12px;\n font-size: 14px;\n line-height: 1.42857143;\n color: #555555;\n background-color: #fff;\n background-image: none;\n border: 1px solid #ccc;\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n}\n.form-control:focus {\n border-color: #66afe9;\n outline: 0;\n -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);\n box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);\n}\n.form-control::-moz-placeholder {\n color: #999;\n opacity: 1;\n}\n.form-control:-ms-input-placeholder {\n color: #999;\n}\n.form-control::-webkit-input-placeholder {\n color: #999;\n}\n.form-control::-ms-expand {\n border: 0;\n background-color: transparent;\n}\n.form-control[disabled],\n.form-control[readonly],\nfieldset[disabled] .form-control {\n background-color: #eeeeee;\n opacity: 1;\n}\n.form-control[disabled],\nfieldset[disabled] .form-control {\n cursor: not-allowed;\n}\ntextarea.form-control {\n height: auto;\n}\ninput[type=\"search\"] {\n -webkit-appearance: none;\n}\n@media screen and (-webkit-min-device-pixel-ratio: 0) {\n input[type=\"date\"].form-control,\n input[type=\"time\"].form-control,\n input[type=\"datetime-local\"].form-control,\n input[type=\"month\"].form-control {\n line-height: 34px;\n }\n input[type=\"date\"].input-sm,\n input[type=\"time\"].input-sm,\n input[type=\"datetime-local\"].input-sm,\n input[type=\"month\"].input-sm,\n .input-group-sm input[type=\"date\"],\n .input-group-sm input[type=\"time\"],\n .input-group-sm input[type=\"datetime-local\"],\n .input-group-sm input[type=\"month\"] {\n line-height: 30px;\n }\n input[type=\"date\"].input-lg,\n input[type=\"time\"].input-lg,\n input[type=\"datetime-local\"].input-lg,\n input[type=\"month\"].input-lg,\n .input-group-lg input[type=\"date\"],\n .input-group-lg input[type=\"time\"],\n .input-group-lg input[type=\"datetime-local\"],\n .input-group-lg input[type=\"month\"] {\n line-height: 46px;\n }\n}\n.form-group {\n margin-bottom: 15px;\n}\n.radio,\n.checkbox {\n position: relative;\n display: block;\n margin-top: 10px;\n margin-bottom: 10px;\n}\n.radio label,\n.checkbox label {\n min-height: 20px;\n padding-left: 20px;\n margin-bottom: 0;\n font-weight: normal;\n cursor: pointer;\n}\n.radio input[type=\"radio\"],\n.radio-inline input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"],\n.checkbox-inline input[type=\"checkbox\"] {\n position: absolute;\n margin-left: -20px;\n margin-top: 4px \\9;\n}\n.radio + .radio,\n.checkbox + .checkbox {\n margin-top: -5px;\n}\n.radio-inline,\n.checkbox-inline {\n position: relative;\n display: inline-block;\n padding-left: 20px;\n margin-bottom: 0;\n vertical-align: middle;\n font-weight: normal;\n cursor: pointer;\n}\n.radio-inline + .radio-inline,\n.checkbox-inline + .checkbox-inline {\n margin-top: 0;\n margin-left: 10px;\n}\ninput[type=\"radio\"][disabled],\ninput[type=\"checkbox\"][disabled],\ninput[type=\"radio\"].disabled,\ninput[type=\"checkbox\"].disabled,\nfieldset[disabled] input[type=\"radio\"],\nfieldset[disabled] input[type=\"checkbox\"] {\n cursor: not-allowed;\n}\n.radio-inline.disabled,\n.checkbox-inline.disabled,\nfieldset[disabled] .radio-inline,\nfieldset[disabled] .checkbox-inline {\n cursor: not-allowed;\n}\n.radio.disabled label,\n.checkbox.disabled label,\nfieldset[disabled] .radio label,\nfieldset[disabled] .checkbox label {\n cursor: not-allowed;\n}\n.form-control-static {\n padding-top: 7px;\n padding-bottom: 7px;\n margin-bottom: 0;\n min-height: 34px;\n}\n.form-control-static.input-lg,\n.form-control-static.input-sm {\n padding-left: 0;\n padding-right: 0;\n}\n.input-sm {\n height: 30px;\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\nselect.input-sm {\n height: 30px;\n line-height: 30px;\n}\ntextarea.input-sm,\nselect[multiple].input-sm {\n height: auto;\n}\n.form-group-sm .form-control {\n height: 30px;\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\n.form-group-sm select.form-control {\n height: 30px;\n line-height: 30px;\n}\n.form-group-sm textarea.form-control,\n.form-group-sm select[multiple].form-control {\n height: auto;\n}\n.form-group-sm .form-control-static {\n height: 30px;\n min-height: 32px;\n padding: 6px 10px;\n font-size: 12px;\n line-height: 1.5;\n}\n.input-lg {\n height: 46px;\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\nselect.input-lg {\n height: 46px;\n line-height: 46px;\n}\ntextarea.input-lg,\nselect[multiple].input-lg {\n height: auto;\n}\n.form-group-lg .form-control {\n height: 46px;\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\n.form-group-lg select.form-control {\n height: 46px;\n line-height: 46px;\n}\n.form-group-lg textarea.form-control,\n.form-group-lg select[multiple].form-control {\n height: auto;\n}\n.form-group-lg .form-control-static {\n height: 46px;\n min-height: 38px;\n padding: 11px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n}\n.has-feedback {\n position: relative;\n}\n.has-feedback .form-control {\n padding-right: 42.5px;\n}\n.form-control-feedback {\n position: absolute;\n top: 0;\n right: 0;\n z-index: 2;\n display: block;\n width: 34px;\n height: 34px;\n line-height: 34px;\n text-align: center;\n pointer-events: none;\n}\n.input-lg + .form-control-feedback,\n.input-group-lg + .form-control-feedback,\n.form-group-lg .form-control + .form-control-feedback {\n width: 46px;\n height: 46px;\n line-height: 46px;\n}\n.input-sm + .form-control-feedback,\n.input-group-sm + .form-control-feedback,\n.form-group-sm .form-control + .form-control-feedback {\n width: 30px;\n height: 30px;\n line-height: 30px;\n}\n.has-success .help-block,\n.has-success .control-label,\n.has-success .radio,\n.has-success .checkbox,\n.has-success .radio-inline,\n.has-success .checkbox-inline,\n.has-success.radio label,\n.has-success.checkbox label,\n.has-success.radio-inline label,\n.has-success.checkbox-inline label {\n color: #3c763d;\n}\n.has-success .form-control {\n border-color: #3c763d;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-success .form-control:focus {\n border-color: #2b542c;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n}\n.has-success .input-group-addon {\n color: #3c763d;\n border-color: #3c763d;\n background-color: #dff0d8;\n}\n.has-success .form-control-feedback {\n color: #3c763d;\n}\n.has-warning .help-block,\n.has-warning .control-label,\n.has-warning .radio,\n.has-warning .checkbox,\n.has-warning .radio-inline,\n.has-warning .checkbox-inline,\n.has-warning.radio label,\n.has-warning.checkbox label,\n.has-warning.radio-inline label,\n.has-warning.checkbox-inline label {\n color: #8a6d3b;\n}\n.has-warning .form-control {\n border-color: #8a6d3b;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-warning .form-control:focus {\n border-color: #66512c;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n}\n.has-warning .input-group-addon {\n color: #8a6d3b;\n border-color: #8a6d3b;\n background-color: #fcf8e3;\n}\n.has-warning .form-control-feedback {\n color: #8a6d3b;\n}\n.has-error .help-block,\n.has-error .control-label,\n.has-error .radio,\n.has-error .checkbox,\n.has-error .radio-inline,\n.has-error .checkbox-inline,\n.has-error.radio label,\n.has-error.checkbox label,\n.has-error.radio-inline label,\n.has-error.checkbox-inline label {\n color: #a94442;\n}\n.has-error .form-control {\n border-color: #a94442;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-error .form-control:focus {\n border-color: #843534;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n}\n.has-error .input-group-addon {\n color: #a94442;\n border-color: #a94442;\n background-color: #f2dede;\n}\n.has-error .form-control-feedback {\n color: #a94442;\n}\n.has-feedback label ~ .form-control-feedback {\n top: 25px;\n}\n.has-feedback label.sr-only ~ .form-control-feedback {\n top: 0;\n}\n.help-block {\n display: block;\n margin-top: 5px;\n margin-bottom: 10px;\n color: #737373;\n}\n@media (min-width: 768px) {\n .form-inline .form-group {\n display: inline-block;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .form-inline .form-control {\n display: inline-block;\n width: auto;\n vertical-align: middle;\n }\n .form-inline .form-control-static {\n display: inline-block;\n }\n .form-inline .input-group {\n display: inline-table;\n vertical-align: middle;\n }\n .form-inline .input-group .input-group-addon,\n .form-inline .input-group .input-group-btn,\n .form-inline .input-group .form-control {\n width: auto;\n }\n .form-inline .input-group > .form-control {\n width: 100%;\n }\n .form-inline .control-label {\n margin-bottom: 0;\n vertical-align: middle;\n }\n .form-inline .radio,\n .form-inline .checkbox {\n display: inline-block;\n margin-top: 0;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .form-inline .radio label,\n .form-inline .checkbox label {\n padding-left: 0;\n }\n .form-inline .radio input[type=\"radio\"],\n .form-inline .checkbox input[type=\"checkbox\"] {\n position: relative;\n margin-left: 0;\n }\n .form-inline .has-feedback .form-control-feedback {\n top: 0;\n }\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox,\n.form-horizontal .radio-inline,\n.form-horizontal .checkbox-inline {\n margin-top: 0;\n margin-bottom: 0;\n padding-top: 7px;\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox {\n min-height: 27px;\n}\n.form-horizontal .form-group {\n margin-left: -15px;\n margin-right: -15px;\n}\n@media (min-width: 768px) {\n .form-horizontal .control-label {\n text-align: right;\n margin-bottom: 0;\n padding-top: 7px;\n }\n}\n.form-horizontal .has-feedback .form-control-feedback {\n right: 15px;\n}\n@media (min-width: 768px) {\n .form-horizontal .form-group-lg .control-label {\n padding-top: 11px;\n font-size: 18px;\n }\n}\n@media (min-width: 768px) {\n .form-horizontal .form-group-sm .control-label {\n padding-top: 6px;\n font-size: 12px;\n }\n}\n.btn {\n display: inline-block;\n margin-bottom: 0;\n font-weight: normal;\n text-align: center;\n vertical-align: middle;\n touch-action: manipulation;\n cursor: pointer;\n background-image: none;\n border: 1px solid transparent;\n white-space: nowrap;\n padding: 6px 12px;\n font-size: 14px;\n line-height: 1.42857143;\n border-radius: 4px;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n}\n.btn:focus,\n.btn:active:focus,\n.btn.active:focus,\n.btn.focus,\n.btn:active.focus,\n.btn.active.focus {\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\n.btn:hover,\n.btn:focus,\n.btn.focus {\n color: #333;\n text-decoration: none;\n}\n.btn:active,\n.btn.active {\n outline: 0;\n background-image: none;\n -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn.disabled,\n.btn[disabled],\nfieldset[disabled] .btn {\n cursor: not-allowed;\n opacity: 0.65;\n filter: alpha(opacity=65);\n -webkit-box-shadow: none;\n box-shadow: none;\n}\na.btn.disabled,\nfieldset[disabled] a.btn {\n pointer-events: none;\n}\n.btn-default {\n color: #333;\n background-color: #fff;\n border-color: #ccc;\n}\n.btn-default:focus,\n.btn-default.focus {\n color: #333;\n background-color: #e6e6e6;\n border-color: #8c8c8c;\n}\n.btn-default:hover {\n color: #333;\n background-color: #e6e6e6;\n border-color: #adadad;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n color: #333;\n background-color: #e6e6e6;\n border-color: #adadad;\n}\n.btn-default:active:hover,\n.btn-default.active:hover,\n.open > .dropdown-toggle.btn-default:hover,\n.btn-default:active:focus,\n.btn-default.active:focus,\n.open > .dropdown-toggle.btn-default:focus,\n.btn-default:active.focus,\n.btn-default.active.focus,\n.open > .dropdown-toggle.btn-default.focus {\n color: #333;\n background-color: #d4d4d4;\n border-color: #8c8c8c;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n background-image: none;\n}\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus {\n background-color: #fff;\n border-color: #ccc;\n}\n.btn-default .badge {\n color: #fff;\n background-color: #333;\n}\n.btn-primary {\n color: #fff;\n background-color: #337ab7;\n border-color: #2e6da4;\n}\n.btn-primary:focus,\n.btn-primary.focus {\n color: #fff;\n background-color: #286090;\n border-color: #122b40;\n}\n.btn-primary:hover {\n color: #fff;\n background-color: #286090;\n border-color: #204d74;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n color: #fff;\n background-color: #286090;\n border-color: #204d74;\n}\n.btn-primary:active:hover,\n.btn-primary.active:hover,\n.open > .dropdown-toggle.btn-primary:hover,\n.btn-primary:active:focus,\n.btn-primary.active:focus,\n.open > .dropdown-toggle.btn-primary:focus,\n.btn-primary:active.focus,\n.btn-primary.active.focus,\n.open > .dropdown-toggle.btn-primary.focus {\n color: #fff;\n background-color: #204d74;\n border-color: #122b40;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n background-image: none;\n}\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus {\n background-color: #337ab7;\n border-color: #2e6da4;\n}\n.btn-primary .badge {\n color: #337ab7;\n background-color: #fff;\n}\n.btn-success {\n color: #fff;\n background-color: #5cb85c;\n border-color: #4cae4c;\n}\n.btn-success:focus,\n.btn-success.focus {\n color: #fff;\n background-color: #449d44;\n border-color: #255625;\n}\n.btn-success:hover {\n color: #fff;\n background-color: #449d44;\n border-color: #398439;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n color: #fff;\n background-color: #449d44;\n border-color: #398439;\n}\n.btn-success:active:hover,\n.btn-success.active:hover,\n.open > .dropdown-toggle.btn-success:hover,\n.btn-success:active:focus,\n.btn-success.active:focus,\n.open > .dropdown-toggle.btn-success:focus,\n.btn-success:active.focus,\n.btn-success.active.focus,\n.open > .dropdown-toggle.btn-success.focus {\n color: #fff;\n background-color: #398439;\n border-color: #255625;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n background-image: none;\n}\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus {\n background-color: #5cb85c;\n border-color: #4cae4c;\n}\n.btn-success .badge {\n color: #5cb85c;\n background-color: #fff;\n}\n.btn-info {\n color: #fff;\n background-color: #5bc0de;\n border-color: #46b8da;\n}\n.btn-info:focus,\n.btn-info.focus {\n color: #fff;\n background-color: #31b0d5;\n border-color: #1b6d85;\n}\n.btn-info:hover {\n color: #fff;\n background-color: #31b0d5;\n border-color: #269abc;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n color: #fff;\n background-color: #31b0d5;\n border-color: #269abc;\n}\n.btn-info:active:hover,\n.btn-info.active:hover,\n.open > .dropdown-toggle.btn-info:hover,\n.btn-info:active:focus,\n.btn-info.active:focus,\n.open > .dropdown-toggle.btn-info:focus,\n.btn-info:active.focus,\n.btn-info.active.focus,\n.open > .dropdown-toggle.btn-info.focus {\n color: #fff;\n background-color: #269abc;\n border-color: #1b6d85;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n background-image: none;\n}\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus {\n background-color: #5bc0de;\n border-color: #46b8da;\n}\n.btn-info .badge {\n color: #5bc0de;\n background-color: #fff;\n}\n.btn-warning {\n color: #fff;\n background-color: #f0ad4e;\n border-color: #eea236;\n}\n.btn-warning:focus,\n.btn-warning.focus {\n color: #fff;\n background-color: #ec971f;\n border-color: #985f0d;\n}\n.btn-warning:hover {\n color: #fff;\n background-color: #ec971f;\n border-color: #d58512;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n color: #fff;\n background-color: #ec971f;\n border-color: #d58512;\n}\n.btn-warning:active:hover,\n.btn-warning.active:hover,\n.open > .dropdown-toggle.btn-warning:hover,\n.btn-warning:active:focus,\n.btn-warning.active:focus,\n.open > .dropdown-toggle.btn-warning:focus,\n.btn-warning:active.focus,\n.btn-warning.active.focus,\n.open > .dropdown-toggle.btn-warning.focus {\n color: #fff;\n background-color: #d58512;\n border-color: #985f0d;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n background-image: none;\n}\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus {\n background-color: #f0ad4e;\n border-color: #eea236;\n}\n.btn-warning .badge {\n color: #f0ad4e;\n background-color: #fff;\n}\n.btn-danger {\n color: #fff;\n background-color: #d9534f;\n border-color: #d43f3a;\n}\n.btn-danger:focus,\n.btn-danger.focus {\n color: #fff;\n background-color: #c9302c;\n border-color: #761c19;\n}\n.btn-danger:hover {\n color: #fff;\n background-color: #c9302c;\n border-color: #ac2925;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n color: #fff;\n background-color: #c9302c;\n border-color: #ac2925;\n}\n.btn-danger:active:hover,\n.btn-danger.active:hover,\n.open > .dropdown-toggle.btn-danger:hover,\n.btn-danger:active:focus,\n.btn-danger.active:focus,\n.open > .dropdown-toggle.btn-danger:focus,\n.btn-danger:active.focus,\n.btn-danger.active.focus,\n.open > .dropdown-toggle.btn-danger.focus {\n color: #fff;\n background-color: #ac2925;\n border-color: #761c19;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n background-image: none;\n}\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus {\n background-color: #d9534f;\n border-color: #d43f3a;\n}\n.btn-danger .badge {\n color: #d9534f;\n background-color: #fff;\n}\n.btn-link {\n color: #337ab7;\n font-weight: normal;\n border-radius: 0;\n}\n.btn-link,\n.btn-link:active,\n.btn-link.active,\n.btn-link[disabled],\nfieldset[disabled] .btn-link {\n background-color: transparent;\n -webkit-box-shadow: none;\n box-shadow: none;\n}\n.btn-link,\n.btn-link:hover,\n.btn-link:focus,\n.btn-link:active {\n border-color: transparent;\n}\n.btn-link:hover,\n.btn-link:focus {\n color: #23527c;\n text-decoration: underline;\n background-color: transparent;\n}\n.btn-link[disabled]:hover,\nfieldset[disabled] .btn-link:hover,\n.btn-link[disabled]:focus,\nfieldset[disabled] .btn-link:focus {\n color: #777777;\n text-decoration: none;\n}\n.btn-lg,\n.btn-group-lg > .btn {\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\n.btn-sm,\n.btn-group-sm > .btn {\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\n.btn-xs,\n.btn-group-xs > .btn {\n padding: 1px 5px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\n.btn-block {\n display: block;\n width: 100%;\n}\n.btn-block + .btn-block {\n margin-top: 5px;\n}\ninput[type=\"submit\"].btn-block,\ninput[type=\"reset\"].btn-block,\ninput[type=\"button\"].btn-block {\n width: 100%;\n}\n.fade {\n opacity: 0;\n -webkit-transition: opacity 0.15s linear;\n -o-transition: opacity 0.15s linear;\n transition: opacity 0.15s linear;\n}\n.fade.in {\n opacity: 1;\n}\n.collapse {\n display: none;\n}\n.collapse.in {\n display: block;\n}\ntr.collapse.in {\n display: table-row;\n}\ntbody.collapse.in {\n display: table-row-group;\n}\n.collapsing {\n position: relative;\n height: 0;\n overflow: hidden;\n -webkit-transition-property: height, visibility;\n transition-property: height, visibility;\n -webkit-transition-duration: 0.35s;\n transition-duration: 0.35s;\n -webkit-transition-timing-function: ease;\n transition-timing-function: ease;\n}\n.caret {\n display: inline-block;\n width: 0;\n height: 0;\n margin-left: 2px;\n vertical-align: middle;\n border-top: 4px dashed;\n border-top: 4px solid \\9;\n border-right: 4px solid transparent;\n border-left: 4px solid transparent;\n}\n.dropup,\n.dropdown {\n position: relative;\n}\n.dropdown-toggle:focus {\n outline: 0;\n}\n.dropdown-menu {\n position: absolute;\n top: 100%;\n left: 0;\n z-index: 1000;\n display: none;\n float: left;\n min-width: 160px;\n padding: 5px 0;\n margin: 2px 0 0;\n list-style: none;\n font-size: 14px;\n text-align: left;\n background-color: #fff;\n border: 1px solid #ccc;\n border: 1px solid rgba(0, 0, 0, 0.15);\n border-radius: 4px;\n -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n background-clip: padding-box;\n}\n.dropdown-menu.pull-right {\n right: 0;\n left: auto;\n}\n.dropdown-menu .divider {\n height: 1px;\n margin: 9px 0;\n overflow: hidden;\n background-color: #e5e5e5;\n}\n.dropdown-menu > li > a {\n display: block;\n padding: 3px 20px;\n clear: both;\n font-weight: normal;\n line-height: 1.42857143;\n color: #333333;\n white-space: nowrap;\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n text-decoration: none;\n color: #262626;\n background-color: #f5f5f5;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n color: #fff;\n text-decoration: none;\n outline: 0;\n background-color: #337ab7;\n}\n.dropdown-menu > .disabled > a,\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n color: #777777;\n}\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n text-decoration: none;\n background-color: transparent;\n background-image: none;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n cursor: not-allowed;\n}\n.open > .dropdown-menu {\n display: block;\n}\n.open > a {\n outline: 0;\n}\n.dropdown-menu-right {\n left: auto;\n right: 0;\n}\n.dropdown-menu-left {\n left: 0;\n right: auto;\n}\n.dropdown-header {\n display: block;\n padding: 3px 20px;\n font-size: 12px;\n line-height: 1.42857143;\n color: #777777;\n white-space: nowrap;\n}\n.dropdown-backdrop {\n position: fixed;\n left: 0;\n right: 0;\n bottom: 0;\n top: 0;\n z-index: 990;\n}\n.pull-right > .dropdown-menu {\n right: 0;\n left: auto;\n}\n.dropup .caret,\n.navbar-fixed-bottom .dropdown .caret {\n border-top: 0;\n border-bottom: 4px dashed;\n border-bottom: 4px solid \\9;\n content: \"\";\n}\n.dropup .dropdown-menu,\n.navbar-fixed-bottom .dropdown .dropdown-menu {\n top: auto;\n bottom: 100%;\n margin-bottom: 2px;\n}\n@media (min-width: 768px) {\n .navbar-right .dropdown-menu {\n left: auto;\n right: 0;\n }\n .navbar-right .dropdown-menu-left {\n left: 0;\n right: auto;\n }\n}\n.btn-group,\n.btn-group-vertical {\n position: relative;\n display: inline-block;\n vertical-align: middle;\n}\n.btn-group > .btn,\n.btn-group-vertical > .btn {\n position: relative;\n float: left;\n}\n.btn-group > .btn:hover,\n.btn-group-vertical > .btn:hover,\n.btn-group > .btn:focus,\n.btn-group-vertical > .btn:focus,\n.btn-group > .btn:active,\n.btn-group-vertical > .btn:active,\n.btn-group > .btn.active,\n.btn-group-vertical > .btn.active {\n z-index: 2;\n}\n.btn-group .btn + .btn,\n.btn-group .btn + .btn-group,\n.btn-group .btn-group + .btn,\n.btn-group .btn-group + .btn-group {\n margin-left: -1px;\n}\n.btn-toolbar {\n margin-left: -5px;\n}\n.btn-toolbar .btn,\n.btn-toolbar .btn-group,\n.btn-toolbar .input-group {\n float: left;\n}\n.btn-toolbar > .btn,\n.btn-toolbar > .btn-group,\n.btn-toolbar > .input-group {\n margin-left: 5px;\n}\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n border-radius: 0;\n}\n.btn-group > .btn:first-child {\n margin-left: 0;\n}\n.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {\n border-bottom-right-radius: 0;\n border-top-right-radius: 0;\n}\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n border-bottom-left-radius: 0;\n border-top-left-radius: 0;\n}\n.btn-group > .btn-group {\n float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n border-bottom-right-radius: 0;\n border-top-right-radius: 0;\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n border-bottom-left-radius: 0;\n border-top-left-radius: 0;\n}\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n outline: 0;\n}\n.btn-group > .btn + .dropdown-toggle {\n padding-left: 8px;\n padding-right: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n padding-left: 12px;\n padding-right: 12px;\n}\n.btn-group.open .dropdown-toggle {\n -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-group.open .dropdown-toggle.btn-link {\n -webkit-box-shadow: none;\n box-shadow: none;\n}\n.btn .caret {\n margin-left: 0;\n}\n.btn-lg .caret {\n border-width: 5px 5px 0;\n border-bottom-width: 0;\n}\n.dropup .btn-lg .caret {\n border-width: 0 5px 5px;\n}\n.btn-group-vertical > .btn,\n.btn-group-vertical > .btn-group,\n.btn-group-vertical > .btn-group > .btn {\n display: block;\n float: none;\n width: 100%;\n max-width: 100%;\n}\n.btn-group-vertical > .btn-group > .btn {\n float: none;\n}\n.btn-group-vertical > .btn + .btn,\n.btn-group-vertical > .btn + .btn-group,\n.btn-group-vertical > .btn-group + .btn,\n.btn-group-vertical > .btn-group + .btn-group {\n margin-top: -1px;\n margin-left: 0;\n}\n.btn-group-vertical > .btn:not(:first-child):not(:last-child) {\n border-radius: 0;\n}\n.btn-group-vertical > .btn:first-child:not(:last-child) {\n border-top-right-radius: 4px;\n border-top-left-radius: 4px;\n border-bottom-right-radius: 0;\n border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn:last-child:not(:first-child) {\n border-top-right-radius: 0;\n border-top-left-radius: 0;\n border-bottom-right-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n border-bottom-right-radius: 0;\n border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n border-top-right-radius: 0;\n border-top-left-radius: 0;\n}\n.btn-group-justified {\n display: table;\n width: 100%;\n table-layout: fixed;\n border-collapse: separate;\n}\n.btn-group-justified > .btn,\n.btn-group-justified > .btn-group {\n float: none;\n display: table-cell;\n width: 1%;\n}\n.btn-group-justified > .btn-group .btn {\n width: 100%;\n}\n.btn-group-justified > .btn-group .dropdown-menu {\n left: auto;\n}\n[data-toggle=\"buttons\"] > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn input[type=\"checkbox\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"checkbox\"] {\n position: absolute;\n clip: rect(0, 0, 0, 0);\n pointer-events: none;\n}\n.input-group {\n position: relative;\n display: table;\n border-collapse: separate;\n}\n.input-group[class*=\"col-\"] {\n float: none;\n padding-left: 0;\n padding-right: 0;\n}\n.input-group .form-control {\n position: relative;\n z-index: 2;\n float: left;\n width: 100%;\n margin-bottom: 0;\n}\n.input-group .form-control:focus {\n z-index: 3;\n}\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n height: 46px;\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\nselect.input-group-lg > .form-control,\nselect.input-group-lg > .input-group-addon,\nselect.input-group-lg > .input-group-btn > .btn {\n height: 46px;\n line-height: 46px;\n}\ntextarea.input-group-lg > .form-control,\ntextarea.input-group-lg > .input-group-addon,\ntextarea.input-group-lg > .input-group-btn > .btn,\nselect[multiple].input-group-lg > .form-control,\nselect[multiple].input-group-lg > .input-group-addon,\nselect[multiple].input-group-lg > .input-group-btn > .btn {\n height: auto;\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n height: 30px;\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\nselect.input-group-sm > .form-control,\nselect.input-group-sm > .input-group-addon,\nselect.input-group-sm > .input-group-btn > .btn {\n height: 30px;\n line-height: 30px;\n}\ntextarea.input-group-sm > .form-control,\ntextarea.input-group-sm > .input-group-addon,\ntextarea.input-group-sm > .input-group-btn > .btn,\nselect[multiple].input-group-sm > .form-control,\nselect[multiple].input-group-sm > .input-group-addon,\nselect[multiple].input-group-sm > .input-group-btn > .btn {\n height: auto;\n}\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n display: table-cell;\n}\n.input-group-addon:not(:first-child):not(:last-child),\n.input-group-btn:not(:first-child):not(:last-child),\n.input-group .form-control:not(:first-child):not(:last-child) {\n border-radius: 0;\n}\n.input-group-addon,\n.input-group-btn {\n width: 1%;\n white-space: nowrap;\n vertical-align: middle;\n}\n.input-group-addon {\n padding: 6px 12px;\n font-size: 14px;\n font-weight: normal;\n line-height: 1;\n color: #555555;\n text-align: center;\n background-color: #eeeeee;\n border: 1px solid #ccc;\n border-radius: 4px;\n}\n.input-group-addon.input-sm {\n padding: 5px 10px;\n font-size: 12px;\n border-radius: 3px;\n}\n.input-group-addon.input-lg {\n padding: 10px 16px;\n font-size: 18px;\n border-radius: 6px;\n}\n.input-group-addon input[type=\"radio\"],\n.input-group-addon input[type=\"checkbox\"] {\n margin-top: 0;\n}\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n border-bottom-right-radius: 0;\n border-top-right-radius: 0;\n}\n.input-group-addon:first-child {\n border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n border-bottom-left-radius: 0;\n border-top-left-radius: 0;\n}\n.input-group-addon:last-child {\n border-left: 0;\n}\n.input-group-btn {\n position: relative;\n font-size: 0;\n white-space: nowrap;\n}\n.input-group-btn > .btn {\n position: relative;\n}\n.input-group-btn > .btn + .btn {\n margin-left: -1px;\n}\n.input-group-btn > .btn:hover,\n.input-group-btn > .btn:focus,\n.input-group-btn > .btn:active {\n z-index: 2;\n}\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group {\n margin-right: -1px;\n}\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group {\n z-index: 2;\n margin-left: -1px;\n}\n.nav {\n margin-bottom: 0;\n padding-left: 0;\n list-style: none;\n}\n.nav > li {\n position: relative;\n display: block;\n}\n.nav > li > a {\n position: relative;\n display: block;\n padding: 10px 15px;\n}\n.nav > li > a:hover,\n.nav > li > a:focus {\n text-decoration: none;\n background-color: #eeeeee;\n}\n.nav > li.disabled > a {\n color: #777777;\n}\n.nav > li.disabled > a:hover,\n.nav > li.disabled > a:focus {\n color: #777777;\n text-decoration: none;\n background-color: transparent;\n cursor: not-allowed;\n}\n.nav .open > a,\n.nav .open > a:hover,\n.nav .open > a:focus {\n background-color: #eeeeee;\n border-color: #337ab7;\n}\n.nav .nav-divider {\n height: 1px;\n margin: 9px 0;\n overflow: hidden;\n background-color: #e5e5e5;\n}\n.nav > li > a > img {\n max-width: none;\n}\n.nav-tabs {\n border-bottom: 1px solid #ddd;\n}\n.nav-tabs > li {\n float: left;\n margin-bottom: -1px;\n}\n.nav-tabs > li > a {\n margin-right: 2px;\n line-height: 1.42857143;\n border: 1px solid transparent;\n border-radius: 4px 4px 0 0;\n}\n.nav-tabs > li > a:hover {\n border-color: #eeeeee #eeeeee #ddd;\n}\n.nav-tabs > li.active > a,\n.nav-tabs > li.active > a:hover,\n.nav-tabs > li.active > a:focus {\n color: #555555;\n background-color: #fff;\n border: 1px solid #ddd;\n border-bottom-color: transparent;\n cursor: default;\n}\n.nav-tabs.nav-justified {\n width: 100%;\n border-bottom: 0;\n}\n.nav-tabs.nav-justified > li {\n float: none;\n}\n.nav-tabs.nav-justified > li > a {\n text-align: center;\n margin-bottom: 5px;\n}\n.nav-tabs.nav-justified > .dropdown .dropdown-menu {\n top: auto;\n left: auto;\n}\n@media (min-width: 768px) {\n .nav-tabs.nav-justified > li {\n display: table-cell;\n width: 1%;\n }\n .nav-tabs.nav-justified > li > a {\n margin-bottom: 0;\n }\n}\n.nav-tabs.nav-justified > li > a {\n margin-right: 0;\n border-radius: 4px;\n}\n.nav-tabs.nav-justified > .active > a,\n.nav-tabs.nav-justified > .active > a:hover,\n.nav-tabs.nav-justified > .active > a:focus {\n border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n .nav-tabs.nav-justified > li > a {\n border-bottom: 1px solid #ddd;\n border-radius: 4px 4px 0 0;\n }\n .nav-tabs.nav-justified > .active > a,\n .nav-tabs.nav-justified > .active > a:hover,\n .nav-tabs.nav-justified > .active > a:focus {\n border-bottom-color: #fff;\n }\n}\n.nav-pills > li {\n float: left;\n}\n.nav-pills > li > a {\n border-radius: 4px;\n}\n.nav-pills > li + li {\n margin-left: 2px;\n}\n.nav-pills > li.active > a,\n.nav-pills > li.active > a:hover,\n.nav-pills > li.active > a:focus {\n color: #fff;\n background-color: #337ab7;\n}\n.nav-stacked > li {\n float: none;\n}\n.nav-stacked > li + li {\n margin-top: 2px;\n margin-left: 0;\n}\n.nav-justified {\n width: 100%;\n}\n.nav-justified > li {\n float: none;\n}\n.nav-justified > li > a {\n text-align: center;\n margin-bottom: 5px;\n}\n.nav-justified > .dropdown .dropdown-menu {\n top: auto;\n left: auto;\n}\n@media (min-width: 768px) {\n .nav-justified > li {\n display: table-cell;\n width: 1%;\n }\n .nav-justified > li > a {\n margin-bottom: 0;\n }\n}\n.nav-tabs-justified {\n border-bottom: 0;\n}\n.nav-tabs-justified > li > a {\n margin-right: 0;\n border-radius: 4px;\n}\n.nav-tabs-justified > .active > a,\n.nav-tabs-justified > .active > a:hover,\n.nav-tabs-justified > .active > a:focus {\n border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n .nav-tabs-justified > li > a {\n border-bottom: 1px solid #ddd;\n border-radius: 4px 4px 0 0;\n }\n .nav-tabs-justified > .active > a,\n .nav-tabs-justified > .active > a:hover,\n .nav-tabs-justified > .active > a:focus {\n border-bottom-color: #fff;\n }\n}\n.tab-content > .tab-pane {\n display: none;\n}\n.tab-content > .active {\n display: block;\n}\n.nav-tabs .dropdown-menu {\n margin-top: -1px;\n border-top-right-radius: 0;\n border-top-left-radius: 0;\n}\n.navbar {\n position: relative;\n min-height: 50px;\n margin-bottom: 20px;\n border: 1px solid transparent;\n}\n@media (min-width: 768px) {\n .navbar {\n border-radius: 4px;\n }\n}\n@media (min-width: 768px) {\n .navbar-header {\n float: left;\n }\n}\n.navbar-collapse {\n overflow-x: visible;\n padding-right: 15px;\n padding-left: 15px;\n border-top: 1px solid transparent;\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);\n -webkit-overflow-scrolling: touch;\n}\n.navbar-collapse.in {\n overflow-y: auto;\n}\n@media (min-width: 768px) {\n .navbar-collapse {\n width: auto;\n border-top: 0;\n box-shadow: none;\n }\n .navbar-collapse.collapse {\n display: block !important;\n height: auto !important;\n padding-bottom: 0;\n overflow: visible !important;\n }\n .navbar-collapse.in {\n overflow-y: visible;\n }\n .navbar-fixed-top .navbar-collapse,\n .navbar-static-top .navbar-collapse,\n .navbar-fixed-bottom .navbar-collapse {\n padding-left: 0;\n padding-right: 0;\n }\n}\n.navbar-fixed-top .navbar-collapse,\n.navbar-fixed-bottom .navbar-collapse {\n max-height: 340px;\n}\n@media (max-device-width: 480px) and (orientation: landscape) {\n .navbar-fixed-top .navbar-collapse,\n .navbar-fixed-bottom .navbar-collapse {\n max-height: 200px;\n }\n}\n.container > .navbar-header,\n.container-fluid > .navbar-header,\n.container > .navbar-collapse,\n.container-fluid > .navbar-collapse {\n margin-right: -15px;\n margin-left: -15px;\n}\n@media (min-width: 768px) {\n .container > .navbar-header,\n .container-fluid > .navbar-header,\n .container > .navbar-collapse,\n .container-fluid > .navbar-collapse {\n margin-right: 0;\n margin-left: 0;\n }\n}\n.navbar-static-top {\n z-index: 1000;\n border-width: 0 0 1px;\n}\n@media (min-width: 768px) {\n .navbar-static-top {\n border-radius: 0;\n }\n}\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n position: fixed;\n right: 0;\n left: 0;\n z-index: 1030;\n}\n@media (min-width: 768px) {\n .navbar-fixed-top,\n .navbar-fixed-bottom {\n border-radius: 0;\n }\n}\n.navbar-fixed-top {\n top: 0;\n border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n bottom: 0;\n margin-bottom: 0;\n border-width: 1px 0 0;\n}\n.navbar-brand {\n float: left;\n padding: 15px 15px;\n font-size: 18px;\n line-height: 20px;\n height: 50px;\n}\n.navbar-brand:hover,\n.navbar-brand:focus {\n text-decoration: none;\n}\n.navbar-brand > img {\n display: block;\n}\n@media (min-width: 768px) {\n .navbar > .container .navbar-brand,\n .navbar > .container-fluid .navbar-brand {\n margin-left: -15px;\n }\n}\n.navbar-toggle {\n position: relative;\n float: right;\n margin-right: 15px;\n padding: 9px 10px;\n margin-top: 8px;\n margin-bottom: 8px;\n background-color: transparent;\n background-image: none;\n border: 1px solid transparent;\n border-radius: 4px;\n}\n.navbar-toggle:focus {\n outline: 0;\n}\n.navbar-toggle .icon-bar {\n display: block;\n width: 22px;\n height: 2px;\n border-radius: 1px;\n}\n.navbar-toggle .icon-bar + .icon-bar {\n margin-top: 4px;\n}\n@media (min-width: 768px) {\n .navbar-toggle {\n display: none;\n }\n}\n.navbar-nav {\n margin: 7.5px -15px;\n}\n.navbar-nav > li > a {\n padding-top: 10px;\n padding-bottom: 10px;\n line-height: 20px;\n}\n@media (max-width: 767px) {\n .navbar-nav .open .dropdown-menu {\n position: static;\n float: none;\n width: auto;\n margin-top: 0;\n background-color: transparent;\n border: 0;\n box-shadow: none;\n }\n .navbar-nav .open .dropdown-menu > li > a,\n .navbar-nav .open .dropdown-menu .dropdown-header {\n padding: 5px 15px 5px 25px;\n }\n .navbar-nav .open .dropdown-menu > li > a {\n line-height: 20px;\n }\n .navbar-nav .open .dropdown-menu > li > a:hover,\n .navbar-nav .open .dropdown-menu > li > a:focus {\n background-image: none;\n }\n}\n@media (min-width: 768px) {\n .navbar-nav {\n float: left;\n margin: 0;\n }\n .navbar-nav > li {\n float: left;\n }\n .navbar-nav > li > a {\n padding-top: 15px;\n padding-bottom: 15px;\n }\n}\n.navbar-form {\n margin-left: -15px;\n margin-right: -15px;\n padding: 10px 15px;\n border-top: 1px solid transparent;\n border-bottom: 1px solid transparent;\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n margin-top: 8px;\n margin-bottom: 8px;\n}\n@media (min-width: 768px) {\n .navbar-form .form-group {\n display: inline-block;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .navbar-form .form-control {\n display: inline-block;\n width: auto;\n vertical-align: middle;\n }\n .navbar-form .form-control-static {\n display: inline-block;\n }\n .navbar-form .input-group {\n display: inline-table;\n vertical-align: middle;\n }\n .navbar-form .input-group .input-group-addon,\n .navbar-form .input-group .input-group-btn,\n .navbar-form .input-group .form-control {\n width: auto;\n }\n .navbar-form .input-group > .form-control {\n width: 100%;\n }\n .navbar-form .control-label {\n margin-bottom: 0;\n vertical-align: middle;\n }\n .navbar-form .radio,\n .navbar-form .checkbox {\n display: inline-block;\n margin-top: 0;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .navbar-form .radio label,\n .navbar-form .checkbox label {\n padding-left: 0;\n }\n .navbar-form .radio input[type=\"radio\"],\n .navbar-form .checkbox input[type=\"checkbox\"] {\n position: relative;\n margin-left: 0;\n }\n .navbar-form .has-feedback .form-control-feedback {\n top: 0;\n }\n}\n@media (max-width: 767px) {\n .navbar-form .form-group {\n margin-bottom: 5px;\n }\n .navbar-form .form-group:last-child {\n margin-bottom: 0;\n }\n}\n@media (min-width: 768px) {\n .navbar-form {\n width: auto;\n border: 0;\n margin-left: 0;\n margin-right: 0;\n padding-top: 0;\n padding-bottom: 0;\n -webkit-box-shadow: none;\n box-shadow: none;\n }\n}\n.navbar-nav > li > .dropdown-menu {\n margin-top: 0;\n border-top-right-radius: 0;\n border-top-left-radius: 0;\n}\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n margin-bottom: 0;\n border-top-right-radius: 4px;\n border-top-left-radius: 4px;\n border-bottom-right-radius: 0;\n border-bottom-left-radius: 0;\n}\n.navbar-btn {\n margin-top: 8px;\n margin-bottom: 8px;\n}\n.navbar-btn.btn-sm {\n margin-top: 10px;\n margin-bottom: 10px;\n}\n.navbar-btn.btn-xs {\n margin-top: 14px;\n margin-bottom: 14px;\n}\n.navbar-text {\n margin-top: 15px;\n margin-bottom: 15px;\n}\n@media (min-width: 768px) {\n .navbar-text {\n float: left;\n margin-left: 15px;\n margin-right: 15px;\n }\n}\n@media (min-width: 768px) {\n .navbar-left {\n float: left !important;\n }\n .navbar-right {\n float: right !important;\n margin-right: -15px;\n }\n .navbar-right ~ .navbar-right {\n margin-right: 0;\n }\n}\n.navbar-default {\n background-color: #f8f8f8;\n border-color: #e7e7e7;\n}\n.navbar-default .navbar-brand {\n color: #777;\n}\n.navbar-default .navbar-brand:hover,\n.navbar-default .navbar-brand:focus {\n color: #5e5e5e;\n background-color: transparent;\n}\n.navbar-default .navbar-text {\n color: #777;\n}\n.navbar-default .navbar-nav > li > a {\n color: #777;\n}\n.navbar-default .navbar-nav > li > a:hover,\n.navbar-default .navbar-nav > li > a:focus {\n color: #333;\n background-color: transparent;\n}\n.navbar-default .navbar-nav > .active > a,\n.navbar-default .navbar-nav > .active > a:hover,\n.navbar-default .navbar-nav > .active > a:focus {\n color: #555;\n background-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .disabled > a,\n.navbar-default .navbar-nav > .disabled > a:hover,\n.navbar-default .navbar-nav > .disabled > a:focus {\n color: #ccc;\n background-color: transparent;\n}\n.navbar-default .navbar-toggle {\n border-color: #ddd;\n}\n.navbar-default .navbar-toggle:hover,\n.navbar-default .navbar-toggle:focus {\n background-color: #ddd;\n}\n.navbar-default .navbar-toggle .icon-bar {\n background-color: #888;\n}\n.navbar-default .navbar-collapse,\n.navbar-default .navbar-form {\n border-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .open > a:hover,\n.navbar-default .navbar-nav > .open > a:focus {\n background-color: #e7e7e7;\n color: #555;\n}\n@media (max-width: 767px) {\n .navbar-default .navbar-nav .open .dropdown-menu > li > a {\n color: #777;\n }\n .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,\n .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {\n color: #333;\n background-color: transparent;\n }\n .navbar-default .navbar-nav .open .dropdown-menu > .active > a,\n .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #555;\n background-color: #e7e7e7;\n }\n .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,\n .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n color: #ccc;\n background-color: transparent;\n }\n}\n.navbar-default .navbar-link {\n color: #777;\n}\n.navbar-default .navbar-link:hover {\n color: #333;\n}\n.navbar-default .btn-link {\n color: #777;\n}\n.navbar-default .btn-link:hover,\n.navbar-default .btn-link:focus {\n color: #333;\n}\n.navbar-default .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-default .btn-link:hover,\n.navbar-default .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-default .btn-link:focus {\n color: #ccc;\n}\n.navbar-inverse {\n background-color: #222;\n border-color: #080808;\n}\n.navbar-inverse .navbar-brand {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-brand:hover,\n.navbar-inverse .navbar-brand:focus {\n color: #fff;\n background-color: transparent;\n}\n.navbar-inverse .navbar-text {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a:hover,\n.navbar-inverse .navbar-nav > li > a:focus {\n color: #fff;\n background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .active > a,\n.navbar-inverse .navbar-nav > .active > a:hover,\n.navbar-inverse .navbar-nav > .active > a:focus {\n color: #fff;\n background-color: #080808;\n}\n.navbar-inverse .navbar-nav > .disabled > a,\n.navbar-inverse .navbar-nav > .disabled > a:hover,\n.navbar-inverse .navbar-nav > .disabled > a:focus {\n color: #444;\n background-color: transparent;\n}\n.navbar-inverse .navbar-toggle {\n border-color: #333;\n}\n.navbar-inverse .navbar-toggle:hover,\n.navbar-inverse .navbar-toggle:focus {\n background-color: #333;\n}\n.navbar-inverse .navbar-toggle .icon-bar {\n background-color: #fff;\n}\n.navbar-inverse .navbar-collapse,\n.navbar-inverse .navbar-form {\n border-color: #101010;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .open > a:hover,\n.navbar-inverse .navbar-nav > .open > a:focus {\n background-color: #080808;\n color: #fff;\n}\n@media (max-width: 767px) {\n .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {\n border-color: #080808;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu .divider {\n background-color: #080808;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {\n color: #9d9d9d;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,\n .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {\n color: #fff;\n background-color: transparent;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #fff;\n background-color: #080808;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n color: #444;\n background-color: transparent;\n }\n}\n.navbar-inverse .navbar-link {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-link:hover {\n color: #fff;\n}\n.navbar-inverse .btn-link {\n color: #9d9d9d;\n}\n.navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link:focus {\n color: #fff;\n}\n.navbar-inverse .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-inverse .btn-link:focus {\n color: #444;\n}\n.breadcrumb {\n padding: 8px 15px;\n margin-bottom: 20px;\n list-style: none;\n background-color: #f5f5f5;\n border-radius: 4px;\n}\n.breadcrumb > li {\n display: inline-block;\n}\n.breadcrumb > li + li:before {\n content: \"/\\00a0\";\n padding: 0 5px;\n color: #ccc;\n}\n.breadcrumb > .active {\n color: #777777;\n}\n.pagination {\n display: inline-block;\n padding-left: 0;\n margin: 20px 0;\n border-radius: 4px;\n}\n.pagination > li {\n display: inline;\n}\n.pagination > li > a,\n.pagination > li > span {\n position: relative;\n float: left;\n padding: 6px 12px;\n line-height: 1.42857143;\n text-decoration: none;\n color: #337ab7;\n background-color: #fff;\n border: 1px solid #ddd;\n margin-left: -1px;\n}\n.pagination > li:first-child > a,\n.pagination > li:first-child > span {\n margin-left: 0;\n border-bottom-left-radius: 4px;\n border-top-left-radius: 4px;\n}\n.pagination > li:last-child > a,\n.pagination > li:last-child > span {\n border-bottom-right-radius: 4px;\n border-top-right-radius: 4px;\n}\n.pagination > li > a:hover,\n.pagination > li > span:hover,\n.pagination > li > a:focus,\n.pagination > li > span:focus {\n z-index: 2;\n color: #23527c;\n background-color: #eeeeee;\n border-color: #ddd;\n}\n.pagination > .active > a,\n.pagination > .active > span,\n.pagination > .active > a:hover,\n.pagination > .active > span:hover,\n.pagination > .active > a:focus,\n.pagination > .active > span:focus {\n z-index: 3;\n color: #fff;\n background-color: #337ab7;\n border-color: #337ab7;\n cursor: default;\n}\n.pagination > .disabled > span,\n.pagination > .disabled > span:hover,\n.pagination > .disabled > span:focus,\n.pagination > .disabled > a,\n.pagination > .disabled > a:hover,\n.pagination > .disabled > a:focus {\n color: #777777;\n background-color: #fff;\n border-color: #ddd;\n cursor: not-allowed;\n}\n.pagination-lg > li > a,\n.pagination-lg > li > span {\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n}\n.pagination-lg > li:first-child > a,\n.pagination-lg > li:first-child > span {\n border-bottom-left-radius: 6px;\n border-top-left-radius: 6px;\n}\n.pagination-lg > li:last-child > a,\n.pagination-lg > li:last-child > span {\n border-bottom-right-radius: 6px;\n border-top-right-radius: 6px;\n}\n.pagination-sm > li > a,\n.pagination-sm > li > span {\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n}\n.pagination-sm > li:first-child > a,\n.pagination-sm > li:first-child > span {\n border-bottom-left-radius: 3px;\n border-top-left-radius: 3px;\n}\n.pagination-sm > li:last-child > a,\n.pagination-sm > li:last-child > span {\n border-bottom-right-radius: 3px;\n border-top-right-radius: 3px;\n}\n.pager {\n padding-left: 0;\n margin: 20px 0;\n list-style: none;\n text-align: center;\n}\n.pager li {\n display: inline;\n}\n.pager li > a,\n.pager li > span {\n display: inline-block;\n padding: 5px 14px;\n background-color: #fff;\n border: 1px solid #ddd;\n border-radius: 15px;\n}\n.pager li > a:hover,\n.pager li > a:focus {\n text-decoration: none;\n background-color: #eeeeee;\n}\n.pager .next > a,\n.pager .next > span {\n float: right;\n}\n.pager .previous > a,\n.pager .previous > span {\n float: left;\n}\n.pager .disabled > a,\n.pager .disabled > a:hover,\n.pager .disabled > a:focus,\n.pager .disabled > span {\n color: #777777;\n background-color: #fff;\n cursor: not-allowed;\n}\n.label {\n display: inline;\n padding: .2em .6em .3em;\n font-size: 75%;\n font-weight: bold;\n line-height: 1;\n color: #fff;\n text-align: center;\n white-space: nowrap;\n vertical-align: baseline;\n border-radius: .25em;\n}\na.label:hover,\na.label:focus {\n color: #fff;\n text-decoration: none;\n cursor: pointer;\n}\n.label:empty {\n display: none;\n}\n.btn .label {\n position: relative;\n top: -1px;\n}\n.label-default {\n background-color: #777777;\n}\n.label-default[href]:hover,\n.label-default[href]:focus {\n background-color: #5e5e5e;\n}\n.label-primary {\n background-color: #337ab7;\n}\n.label-primary[href]:hover,\n.label-primary[href]:focus {\n background-color: #286090;\n}\n.label-success {\n background-color: #5cb85c;\n}\n.label-success[href]:hover,\n.label-success[href]:focus {\n background-color: #449d44;\n}\n.label-info {\n background-color: #5bc0de;\n}\n.label-info[href]:hover,\n.label-info[href]:focus {\n background-color: #31b0d5;\n}\n.label-warning {\n background-color: #f0ad4e;\n}\n.label-warning[href]:hover,\n.label-warning[href]:focus {\n background-color: #ec971f;\n}\n.label-danger {\n background-color: #d9534f;\n}\n.label-danger[href]:hover,\n.label-danger[href]:focus {\n background-color: #c9302c;\n}\n.badge {\n display: inline-block;\n min-width: 10px;\n padding: 3px 7px;\n font-size: 12px;\n font-weight: bold;\n color: #fff;\n line-height: 1;\n vertical-align: middle;\n white-space: nowrap;\n text-align: center;\n background-color: #777777;\n border-radius: 10px;\n}\n.badge:empty {\n display: none;\n}\n.btn .badge {\n position: relative;\n top: -1px;\n}\n.btn-xs .badge,\n.btn-group-xs > .btn .badge {\n top: 0;\n padding: 1px 5px;\n}\na.badge:hover,\na.badge:focus {\n color: #fff;\n text-decoration: none;\n cursor: pointer;\n}\n.list-group-item.active > .badge,\n.nav-pills > .active > a > .badge {\n color: #337ab7;\n background-color: #fff;\n}\n.list-group-item > .badge {\n float: right;\n}\n.list-group-item > .badge + .badge {\n margin-right: 5px;\n}\n.nav-pills > li > a > .badge {\n margin-left: 3px;\n}\n.jumbotron {\n padding-top: 30px;\n padding-bottom: 30px;\n margin-bottom: 30px;\n color: inherit;\n background-color: #eeeeee;\n}\n.jumbotron h1,\n.jumbotron .h1 {\n color: inherit;\n}\n.jumbotron p {\n margin-bottom: 15px;\n font-size: 21px;\n font-weight: 200;\n}\n.jumbotron > hr {\n border-top-color: #d5d5d5;\n}\n.container .jumbotron,\n.container-fluid .jumbotron {\n border-radius: 6px;\n padding-left: 15px;\n padding-right: 15px;\n}\n.jumbotron .container {\n max-width: 100%;\n}\n@media screen and (min-width: 768px) {\n .jumbotron {\n padding-top: 48px;\n padding-bottom: 48px;\n }\n .container .jumbotron,\n .container-fluid .jumbotron {\n padding-left: 60px;\n padding-right: 60px;\n }\n .jumbotron h1,\n .jumbotron .h1 {\n font-size: 63px;\n }\n}\n.thumbnail {\n display: block;\n padding: 4px;\n margin-bottom: 20px;\n line-height: 1.42857143;\n background-color: #fff;\n border: 1px solid #ddd;\n border-radius: 4px;\n -webkit-transition: border 0.2s ease-in-out;\n -o-transition: border 0.2s ease-in-out;\n transition: border 0.2s ease-in-out;\n}\n.thumbnail > img,\n.thumbnail a > img {\n margin-left: auto;\n margin-right: auto;\n}\na.thumbnail:hover,\na.thumbnail:focus,\na.thumbnail.active {\n border-color: #337ab7;\n}\n.thumbnail .caption {\n padding: 9px;\n color: #333333;\n}\n.alert {\n padding: 15px;\n margin-bottom: 20px;\n border: 1px solid transparent;\n border-radius: 4px;\n}\n.alert h4 {\n margin-top: 0;\n color: inherit;\n}\n.alert .alert-link {\n font-weight: bold;\n}\n.alert > p,\n.alert > ul {\n margin-bottom: 0;\n}\n.alert > p + p {\n margin-top: 5px;\n}\n.alert-dismissable,\n.alert-dismissible {\n padding-right: 35px;\n}\n.alert-dismissable .close,\n.alert-dismissible .close {\n position: relative;\n top: -2px;\n right: -21px;\n color: inherit;\n}\n.alert-success {\n background-color: #dff0d8;\n border-color: #d6e9c6;\n color: #3c763d;\n}\n.alert-success hr {\n border-top-color: #c9e2b3;\n}\n.alert-success .alert-link {\n color: #2b542c;\n}\n.alert-info {\n background-color: #d9edf7;\n border-color: #bce8f1;\n color: #31708f;\n}\n.alert-info hr {\n border-top-color: #a6e1ec;\n}\n.alert-info .alert-link {\n color: #245269;\n}\n.alert-warning {\n background-color: #fcf8e3;\n border-color: #faebcc;\n color: #8a6d3b;\n}\n.alert-warning hr {\n border-top-color: #f7e1b5;\n}\n.alert-warning .alert-link {\n color: #66512c;\n}\n.alert-danger {\n background-color: #f2dede;\n border-color: #ebccd1;\n color: #a94442;\n}\n.alert-danger hr {\n border-top-color: #e4b9c0;\n}\n.alert-danger .alert-link {\n color: #843534;\n}\n@-webkit-keyframes progress-bar-stripes {\n from {\n background-position: 40px 0;\n }\n to {\n background-position: 0 0;\n }\n}\n@keyframes progress-bar-stripes {\n from {\n background-position: 40px 0;\n }\n to {\n background-position: 0 0;\n }\n}\n.progress {\n overflow: hidden;\n height: 20px;\n margin-bottom: 20px;\n background-color: #f5f5f5;\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n}\n.progress-bar {\n float: left;\n width: 0%;\n height: 100%;\n font-size: 12px;\n line-height: 20px;\n color: #fff;\n text-align: center;\n background-color: #337ab7;\n -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n -webkit-transition: width 0.6s ease;\n -o-transition: width 0.6s ease;\n transition: width 0.6s ease;\n}\n.progress-striped .progress-bar,\n.progress-bar-striped {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-size: 40px 40px;\n}\n.progress.active .progress-bar,\n.progress-bar.active {\n -webkit-animation: progress-bar-stripes 2s linear infinite;\n -o-animation: progress-bar-stripes 2s linear infinite;\n animation: progress-bar-stripes 2s linear infinite;\n}\n.progress-bar-success {\n background-color: #5cb85c;\n}\n.progress-striped .progress-bar-success {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-info {\n background-color: #5bc0de;\n}\n.progress-striped .progress-bar-info {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-warning {\n background-color: #f0ad4e;\n}\n.progress-striped .progress-bar-warning {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-danger {\n background-color: #d9534f;\n}\n.progress-striped .progress-bar-danger {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.media {\n margin-top: 15px;\n}\n.media:first-child {\n margin-top: 0;\n}\n.media,\n.media-body {\n zoom: 1;\n overflow: hidden;\n}\n.media-body {\n width: 10000px;\n}\n.media-object {\n display: block;\n}\n.media-object.img-thumbnail {\n max-width: none;\n}\n.media-right,\n.media > .pull-right {\n padding-left: 10px;\n}\n.media-left,\n.media > .pull-left {\n padding-right: 10px;\n}\n.media-left,\n.media-right,\n.media-body {\n display: table-cell;\n vertical-align: top;\n}\n.media-middle {\n vertical-align: middle;\n}\n.media-bottom {\n vertical-align: bottom;\n}\n.media-heading {\n margin-top: 0;\n margin-bottom: 5px;\n}\n.media-list {\n padding-left: 0;\n list-style: none;\n}\n.list-group {\n margin-bottom: 20px;\n padding-left: 0;\n}\n.list-group-item {\n position: relative;\n display: block;\n padding: 10px 15px;\n margin-bottom: -1px;\n background-color: #fff;\n border: 1px solid #ddd;\n}\n.list-group-item:first-child {\n border-top-right-radius: 4px;\n border-top-left-radius: 4px;\n}\n.list-group-item:last-child {\n margin-bottom: 0;\n border-bottom-right-radius: 4px;\n border-bottom-left-radius: 4px;\n}\na.list-group-item,\nbutton.list-group-item {\n color: #555;\n}\na.list-group-item .list-group-item-heading,\nbutton.list-group-item .list-group-item-heading {\n color: #333;\n}\na.list-group-item:hover,\nbutton.list-group-item:hover,\na.list-group-item:focus,\nbutton.list-group-item:focus {\n text-decoration: none;\n color: #555;\n background-color: #f5f5f5;\n}\nbutton.list-group-item {\n width: 100%;\n text-align: left;\n}\n.list-group-item.disabled,\n.list-group-item.disabled:hover,\n.list-group-item.disabled:focus {\n background-color: #eeeeee;\n color: #777777;\n cursor: not-allowed;\n}\n.list-group-item.disabled .list-group-item-heading,\n.list-group-item.disabled:hover .list-group-item-heading,\n.list-group-item.disabled:focus .list-group-item-heading {\n color: inherit;\n}\n.list-group-item.disabled .list-group-item-text,\n.list-group-item.disabled:hover .list-group-item-text,\n.list-group-item.disabled:focus .list-group-item-text {\n color: #777777;\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n z-index: 2;\n color: #fff;\n background-color: #337ab7;\n border-color: #337ab7;\n}\n.list-group-item.active .list-group-item-heading,\n.list-group-item.active:hover .list-group-item-heading,\n.list-group-item.active:focus .list-group-item-heading,\n.list-group-item.active .list-group-item-heading > small,\n.list-group-item.active:hover .list-group-item-heading > small,\n.list-group-item.active:focus .list-group-item-heading > small,\n.list-group-item.active .list-group-item-heading > .small,\n.list-group-item.active:hover .list-group-item-heading > .small,\n.list-group-item.active:focus .list-group-item-heading > .small {\n color: inherit;\n}\n.list-group-item.active .list-group-item-text,\n.list-group-item.active:hover .list-group-item-text,\n.list-group-item.active:focus .list-group-item-text {\n color: #c7ddef;\n}\n.list-group-item-success {\n color: #3c763d;\n background-color: #dff0d8;\n}\na.list-group-item-success,\nbutton.list-group-item-success {\n color: #3c763d;\n}\na.list-group-item-success .list-group-item-heading,\nbutton.list-group-item-success .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-success:hover,\nbutton.list-group-item-success:hover,\na.list-group-item-success:focus,\nbutton.list-group-item-success:focus {\n color: #3c763d;\n background-color: #d0e9c6;\n}\na.list-group-item-success.active,\nbutton.list-group-item-success.active,\na.list-group-item-success.active:hover,\nbutton.list-group-item-success.active:hover,\na.list-group-item-success.active:focus,\nbutton.list-group-item-success.active:focus {\n color: #fff;\n background-color: #3c763d;\n border-color: #3c763d;\n}\n.list-group-item-info {\n color: #31708f;\n background-color: #d9edf7;\n}\na.list-group-item-info,\nbutton.list-group-item-info {\n color: #31708f;\n}\na.list-group-item-info .list-group-item-heading,\nbutton.list-group-item-info .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-info:hover,\nbutton.list-group-item-info:hover,\na.list-group-item-info:focus,\nbutton.list-group-item-info:focus {\n color: #31708f;\n background-color: #c4e3f3;\n}\na.list-group-item-info.active,\nbutton.list-group-item-info.active,\na.list-group-item-info.active:hover,\nbutton.list-group-item-info.active:hover,\na.list-group-item-info.active:focus,\nbutton.list-group-item-info.active:focus {\n color: #fff;\n background-color: #31708f;\n border-color: #31708f;\n}\n.list-group-item-warning {\n color: #8a6d3b;\n background-color: #fcf8e3;\n}\na.list-group-item-warning,\nbutton.list-group-item-warning {\n color: #8a6d3b;\n}\na.list-group-item-warning .list-group-item-heading,\nbutton.list-group-item-warning .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-warning:hover,\nbutton.list-group-item-warning:hover,\na.list-group-item-warning:focus,\nbutton.list-group-item-warning:focus {\n color: #8a6d3b;\n background-color: #faf2cc;\n}\na.list-group-item-warning.active,\nbutton.list-group-item-warning.active,\na.list-group-item-warning.active:hover,\nbutton.list-group-item-warning.active:hover,\na.list-group-item-warning.active:focus,\nbutton.list-group-item-warning.active:focus {\n color: #fff;\n background-color: #8a6d3b;\n border-color: #8a6d3b;\n}\n.list-group-item-danger {\n color: #a94442;\n background-color: #f2dede;\n}\na.list-group-item-danger,\nbutton.list-group-item-danger {\n color: #a94442;\n}\na.list-group-item-danger .list-group-item-heading,\nbutton.list-group-item-danger .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-danger:hover,\nbutton.list-group-item-danger:hover,\na.list-group-item-danger:focus,\nbutton.list-group-item-danger:focus {\n color: #a94442;\n background-color: #ebcccc;\n}\na.list-group-item-danger.active,\nbutton.list-group-item-danger.active,\na.list-group-item-danger.active:hover,\nbutton.list-group-item-danger.active:hover,\na.list-group-item-danger.active:focus,\nbutton.list-group-item-danger.active:focus {\n color: #fff;\n background-color: #a94442;\n border-color: #a94442;\n}\n.list-group-item-heading {\n margin-top: 0;\n margin-bottom: 5px;\n}\n.list-group-item-text {\n margin-bottom: 0;\n line-height: 1.3;\n}\n.panel {\n margin-bottom: 20px;\n background-color: #fff;\n border: 1px solid transparent;\n border-radius: 4px;\n -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.panel-body {\n padding: 15px;\n}\n.panel-heading {\n padding: 10px 15px;\n border-bottom: 1px solid transparent;\n border-top-right-radius: 3px;\n border-top-left-radius: 3px;\n}\n.panel-heading > .dropdown .dropdown-toggle {\n color: inherit;\n}\n.panel-title {\n margin-top: 0;\n margin-bottom: 0;\n font-size: 16px;\n color: inherit;\n}\n.panel-title > a,\n.panel-title > small,\n.panel-title > .small,\n.panel-title > small > a,\n.panel-title > .small > a {\n color: inherit;\n}\n.panel-footer {\n padding: 10px 15px;\n background-color: #f5f5f5;\n border-top: 1px solid #ddd;\n border-bottom-right-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.panel > .list-group,\n.panel > .panel-collapse > .list-group {\n margin-bottom: 0;\n}\n.panel > .list-group .list-group-item,\n.panel > .panel-collapse > .list-group .list-group-item {\n border-width: 1px 0;\n border-radius: 0;\n}\n.panel > .list-group:first-child .list-group-item:first-child,\n.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {\n border-top: 0;\n border-top-right-radius: 3px;\n border-top-left-radius: 3px;\n}\n.panel > .list-group:last-child .list-group-item:last-child,\n.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {\n border-bottom: 0;\n border-bottom-right-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {\n border-top-right-radius: 0;\n border-top-left-radius: 0;\n}\n.panel-heading + .list-group .list-group-item:first-child {\n border-top-width: 0;\n}\n.list-group + .panel-footer {\n border-top-width: 0;\n}\n.panel > .table,\n.panel > .table-responsive > .table,\n.panel > .panel-collapse > .table {\n margin-bottom: 0;\n}\n.panel > .table caption,\n.panel > .table-responsive > .table caption,\n.panel > .panel-collapse > .table caption {\n padding-left: 15px;\n padding-right: 15px;\n}\n.panel > .table:first-child,\n.panel > .table-responsive:first-child > .table:first-child {\n border-top-right-radius: 3px;\n border-top-left-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {\n border-top-left-radius: 3px;\n border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {\n border-top-left-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {\n border-top-right-radius: 3px;\n}\n.panel > .table:last-child,\n.panel > .table-responsive:last-child > .table:last-child {\n border-bottom-right-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {\n border-bottom-left-radius: 3px;\n border-bottom-right-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {\n border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {\n border-bottom-right-radius: 3px;\n}\n.panel > .panel-body + .table,\n.panel > .panel-body + .table-responsive,\n.panel > .table + .panel-body,\n.panel > .table-responsive + .panel-body {\n border-top: 1px solid #ddd;\n}\n.panel > .table > tbody:first-child > tr:first-child th,\n.panel > .table > tbody:first-child > tr:first-child td {\n border-top: 0;\n}\n.panel > .table-bordered,\n.panel > .table-responsive > .table-bordered {\n border: 0;\n}\n.panel > .table-bordered > thead > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,\n.panel > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-bordered > thead > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,\n.panel > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-bordered > tfoot > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n border-left: 0;\n}\n.panel > .table-bordered > thead > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,\n.panel > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-bordered > thead > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,\n.panel > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-bordered > tfoot > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n border-right: 0;\n}\n.panel > .table-bordered > thead > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,\n.panel > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-bordered > thead > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,\n.panel > .table-bordered > tbody > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {\n border-bottom: 0;\n}\n.panel > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-bordered > tfoot > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {\n border-bottom: 0;\n}\n.panel > .table-responsive {\n border: 0;\n margin-bottom: 0;\n}\n.panel-group {\n margin-bottom: 20px;\n}\n.panel-group .panel {\n margin-bottom: 0;\n border-radius: 4px;\n}\n.panel-group .panel + .panel {\n margin-top: 5px;\n}\n.panel-group .panel-heading {\n border-bottom: 0;\n}\n.panel-group .panel-heading + .panel-collapse > .panel-body,\n.panel-group .panel-heading + .panel-collapse > .list-group {\n border-top: 1px solid #ddd;\n}\n.panel-group .panel-footer {\n border-top: 0;\n}\n.panel-group .panel-footer + .panel-collapse .panel-body {\n border-bottom: 1px solid #ddd;\n}\n.panel-default {\n border-color: #ddd;\n}\n.panel-default > .panel-heading {\n color: #333333;\n background-color: #f5f5f5;\n border-color: #ddd;\n}\n.panel-default > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #ddd;\n}\n.panel-default > .panel-heading .badge {\n color: #f5f5f5;\n background-color: #333333;\n}\n.panel-default > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #ddd;\n}\n.panel-primary {\n border-color: #337ab7;\n}\n.panel-primary > .panel-heading {\n color: #fff;\n background-color: #337ab7;\n border-color: #337ab7;\n}\n.panel-primary > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #337ab7;\n}\n.panel-primary > .panel-heading .badge {\n color: #337ab7;\n background-color: #fff;\n}\n.panel-primary > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #337ab7;\n}\n.panel-success {\n border-color: #d6e9c6;\n}\n.panel-success > .panel-heading {\n color: #3c763d;\n background-color: #dff0d8;\n border-color: #d6e9c6;\n}\n.panel-success > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #d6e9c6;\n}\n.panel-success > .panel-heading .badge {\n color: #dff0d8;\n background-color: #3c763d;\n}\n.panel-success > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #d6e9c6;\n}\n.panel-info {\n border-color: #bce8f1;\n}\n.panel-info > .panel-heading {\n color: #31708f;\n background-color: #d9edf7;\n border-color: #bce8f1;\n}\n.panel-info > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #bce8f1;\n}\n.panel-info > .panel-heading .badge {\n color: #d9edf7;\n background-color: #31708f;\n}\n.panel-info > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #bce8f1;\n}\n.panel-warning {\n border-color: #faebcc;\n}\n.panel-warning > .panel-heading {\n color: #8a6d3b;\n background-color: #fcf8e3;\n border-color: #faebcc;\n}\n.panel-warning > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #faebcc;\n}\n.panel-warning > .panel-heading .badge {\n color: #fcf8e3;\n background-color: #8a6d3b;\n}\n.panel-warning > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #faebcc;\n}\n.panel-danger {\n border-color: #ebccd1;\n}\n.panel-danger > .panel-heading {\n color: #a94442;\n background-color: #f2dede;\n border-color: #ebccd1;\n}\n.panel-danger > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #ebccd1;\n}\n.panel-danger > .panel-heading .badge {\n color: #f2dede;\n background-color: #a94442;\n}\n.panel-danger > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #ebccd1;\n}\n.embed-responsive {\n position: relative;\n display: block;\n height: 0;\n padding: 0;\n overflow: hidden;\n}\n.embed-responsive .embed-responsive-item,\n.embed-responsive iframe,\n.embed-responsive embed,\n.embed-responsive object,\n.embed-responsive video {\n position: absolute;\n top: 0;\n left: 0;\n bottom: 0;\n height: 100%;\n width: 100%;\n border: 0;\n}\n.embed-responsive-16by9 {\n padding-bottom: 56.25%;\n}\n.embed-responsive-4by3 {\n padding-bottom: 75%;\n}\n.well {\n min-height: 20px;\n padding: 19px;\n margin-bottom: 20px;\n background-color: #f5f5f5;\n border: 1px solid #e3e3e3;\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.well blockquote {\n border-color: #ddd;\n border-color: rgba(0, 0, 0, 0.15);\n}\n.well-lg {\n padding: 24px;\n border-radius: 6px;\n}\n.well-sm {\n padding: 9px;\n border-radius: 3px;\n}\n.close {\n float: right;\n font-size: 21px;\n font-weight: bold;\n line-height: 1;\n color: #000;\n text-shadow: 0 1px 0 #fff;\n opacity: 0.2;\n filter: alpha(opacity=20);\n}\n.close:hover,\n.close:focus {\n color: #000;\n text-decoration: none;\n cursor: pointer;\n opacity: 0.5;\n filter: alpha(opacity=50);\n}\nbutton.close {\n padding: 0;\n cursor: pointer;\n background: transparent;\n border: 0;\n -webkit-appearance: none;\n}\n.modal-open {\n overflow: hidden;\n}\n.modal {\n display: none;\n overflow: hidden;\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1050;\n -webkit-overflow-scrolling: touch;\n outline: 0;\n}\n.modal.fade .modal-dialog {\n -webkit-transform: translate(0, -25%);\n -ms-transform: translate(0, -25%);\n -o-transform: translate(0, -25%);\n transform: translate(0, -25%);\n -webkit-transition: -webkit-transform 0.3s ease-out;\n -moz-transition: -moz-transform 0.3s ease-out;\n -o-transition: -o-transform 0.3s ease-out;\n transition: transform 0.3s ease-out;\n}\n.modal.in .modal-dialog {\n -webkit-transform: translate(0, 0);\n -ms-transform: translate(0, 0);\n -o-transform: translate(0, 0);\n transform: translate(0, 0);\n}\n.modal-open .modal {\n overflow-x: hidden;\n overflow-y: auto;\n}\n.modal-dialog {\n position: relative;\n width: auto;\n margin: 10px;\n}\n.modal-content {\n position: relative;\n background-color: #fff;\n border: 1px solid #999;\n border: 1px solid rgba(0, 0, 0, 0.2);\n border-radius: 6px;\n -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n background-clip: padding-box;\n outline: 0;\n}\n.modal-backdrop {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1040;\n background-color: #000;\n}\n.modal-backdrop.fade {\n opacity: 0;\n filter: alpha(opacity=0);\n}\n.modal-backdrop.in {\n opacity: 0.5;\n filter: alpha(opacity=50);\n}\n.modal-header {\n padding: 15px;\n border-bottom: 1px solid #e5e5e5;\n}\n.modal-header .close {\n margin-top: -2px;\n}\n.modal-title {\n margin: 0;\n line-height: 1.42857143;\n}\n.modal-body {\n position: relative;\n padding: 15px;\n}\n.modal-footer {\n padding: 15px;\n text-align: right;\n border-top: 1px solid #e5e5e5;\n}\n.modal-footer .btn + .btn {\n margin-left: 5px;\n margin-bottom: 0;\n}\n.modal-footer .btn-group .btn + .btn {\n margin-left: -1px;\n}\n.modal-footer .btn-block + .btn-block {\n margin-left: 0;\n}\n.modal-scrollbar-measure {\n position: absolute;\n top: -9999px;\n width: 50px;\n height: 50px;\n overflow: scroll;\n}\n@media (min-width: 768px) {\n .modal-dialog {\n width: 600px;\n margin: 30px auto;\n }\n .modal-content {\n -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n }\n .modal-sm {\n width: 300px;\n }\n}\n@media (min-width: 992px) {\n .modal-lg {\n width: 900px;\n }\n}\n.tooltip {\n position: absolute;\n z-index: 1070;\n display: block;\n font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n font-style: normal;\n font-weight: normal;\n letter-spacing: normal;\n line-break: auto;\n line-height: 1.42857143;\n text-align: left;\n text-align: start;\n text-decoration: none;\n text-shadow: none;\n text-transform: none;\n white-space: normal;\n word-break: normal;\n word-spacing: normal;\n word-wrap: normal;\n font-size: 12px;\n opacity: 0;\n filter: alpha(opacity=0);\n}\n.tooltip.in {\n opacity: 0.9;\n filter: alpha(opacity=90);\n}\n.tooltip.top {\n margin-top: -3px;\n padding: 5px 0;\n}\n.tooltip.right {\n margin-left: 3px;\n padding: 0 5px;\n}\n.tooltip.bottom {\n margin-top: 3px;\n padding: 5px 0;\n}\n.tooltip.left {\n margin-left: -3px;\n padding: 0 5px;\n}\n.tooltip-inner {\n max-width: 200px;\n padding: 3px 8px;\n color: #fff;\n text-align: center;\n background-color: #000;\n border-radius: 4px;\n}\n.tooltip-arrow {\n position: absolute;\n width: 0;\n height: 0;\n border-color: transparent;\n border-style: solid;\n}\n.tooltip.top .tooltip-arrow {\n bottom: 0;\n left: 50%;\n margin-left: -5px;\n border-width: 5px 5px 0;\n border-top-color: #000;\n}\n.tooltip.top-left .tooltip-arrow {\n bottom: 0;\n right: 5px;\n margin-bottom: -5px;\n border-width: 5px 5px 0;\n border-top-color: #000;\n}\n.tooltip.top-right .tooltip-arrow {\n bottom: 0;\n left: 5px;\n margin-bottom: -5px;\n border-width: 5px 5px 0;\n border-top-color: #000;\n}\n.tooltip.right .tooltip-arrow {\n top: 50%;\n left: 0;\n margin-top: -5px;\n border-width: 5px 5px 5px 0;\n border-right-color: #000;\n}\n.tooltip.left .tooltip-arrow {\n top: 50%;\n right: 0;\n margin-top: -5px;\n border-width: 5px 0 5px 5px;\n border-left-color: #000;\n}\n.tooltip.bottom .tooltip-arrow {\n top: 0;\n left: 50%;\n margin-left: -5px;\n border-width: 0 5px 5px;\n border-bottom-color: #000;\n}\n.tooltip.bottom-left .tooltip-arrow {\n top: 0;\n right: 5px;\n margin-top: -5px;\n border-width: 0 5px 5px;\n border-bottom-color: #000;\n}\n.tooltip.bottom-right .tooltip-arrow {\n top: 0;\n left: 5px;\n margin-top: -5px;\n border-width: 0 5px 5px;\n border-bottom-color: #000;\n}\n.popover {\n position: absolute;\n top: 0;\n left: 0;\n z-index: 1060;\n display: none;\n max-width: 276px;\n padding: 1px;\n font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n font-style: normal;\n font-weight: normal;\n letter-spacing: normal;\n line-break: auto;\n line-height: 1.42857143;\n text-align: left;\n text-align: start;\n text-decoration: none;\n text-shadow: none;\n text-transform: none;\n white-space: normal;\n word-break: normal;\n word-spacing: normal;\n word-wrap: normal;\n font-size: 14px;\n background-color: #fff;\n background-clip: padding-box;\n border: 1px solid #ccc;\n border: 1px solid rgba(0, 0, 0, 0.2);\n border-radius: 6px;\n -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n}\n.popover.top {\n margin-top: -10px;\n}\n.popover.right {\n margin-left: 10px;\n}\n.popover.bottom {\n margin-top: 10px;\n}\n.popover.left {\n margin-left: -10px;\n}\n.popover-title {\n margin: 0;\n padding: 8px 14px;\n font-size: 14px;\n background-color: #f7f7f7;\n border-bottom: 1px solid #ebebeb;\n border-radius: 5px 5px 0 0;\n}\n.popover-content {\n padding: 9px 14px;\n}\n.popover > .arrow,\n.popover > .arrow:after {\n position: absolute;\n display: block;\n width: 0;\n height: 0;\n border-color: transparent;\n border-style: solid;\n}\n.popover > .arrow {\n border-width: 11px;\n}\n.popover > .arrow:after {\n border-width: 10px;\n content: \"\";\n}\n.popover.top > .arrow {\n left: 50%;\n margin-left: -11px;\n border-bottom-width: 0;\n border-top-color: #999999;\n border-top-color: rgba(0, 0, 0, 0.25);\n bottom: -11px;\n}\n.popover.top > .arrow:after {\n content: \" \";\n bottom: 1px;\n margin-left: -10px;\n border-bottom-width: 0;\n border-top-color: #fff;\n}\n.popover.right > .arrow {\n top: 50%;\n left: -11px;\n margin-top: -11px;\n border-left-width: 0;\n border-right-color: #999999;\n border-right-color: rgba(0, 0, 0, 0.25);\n}\n.popover.right > .arrow:after {\n content: \" \";\n left: 1px;\n bottom: -10px;\n border-left-width: 0;\n border-right-color: #fff;\n}\n.popover.bottom > .arrow {\n left: 50%;\n margin-left: -11px;\n border-top-width: 0;\n border-bottom-color: #999999;\n border-bottom-color: rgba(0, 0, 0, 0.25);\n top: -11px;\n}\n.popover.bottom > .arrow:after {\n content: \" \";\n top: 1px;\n margin-left: -10px;\n border-top-width: 0;\n border-bottom-color: #fff;\n}\n.popover.left > .arrow {\n top: 50%;\n right: -11px;\n margin-top: -11px;\n border-right-width: 0;\n border-left-color: #999999;\n border-left-color: rgba(0, 0, 0, 0.25);\n}\n.popover.left > .arrow:after {\n content: \" \";\n right: 1px;\n border-right-width: 0;\n border-left-color: #fff;\n bottom: -10px;\n}\n.carousel {\n position: relative;\n}\n.carousel-inner {\n position: relative;\n overflow: hidden;\n width: 100%;\n}\n.carousel-inner > .item {\n display: none;\n position: relative;\n -webkit-transition: 0.6s ease-in-out left;\n -o-transition: 0.6s ease-in-out left;\n transition: 0.6s ease-in-out left;\n}\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n line-height: 1;\n}\n@media all and (transform-3d), (-webkit-transform-3d) {\n .carousel-inner > .item {\n -webkit-transition: -webkit-transform 0.6s ease-in-out;\n -moz-transition: -moz-transform 0.6s ease-in-out;\n -o-transition: -o-transform 0.6s ease-in-out;\n transition: transform 0.6s ease-in-out;\n -webkit-backface-visibility: hidden;\n -moz-backface-visibility: hidden;\n backface-visibility: hidden;\n -webkit-perspective: 1000px;\n -moz-perspective: 1000px;\n perspective: 1000px;\n }\n .carousel-inner > .item.next,\n .carousel-inner > .item.active.right {\n -webkit-transform: translate3d(100%, 0, 0);\n transform: translate3d(100%, 0, 0);\n left: 0;\n }\n .carousel-inner > .item.prev,\n .carousel-inner > .item.active.left {\n -webkit-transform: translate3d(-100%, 0, 0);\n transform: translate3d(-100%, 0, 0);\n left: 0;\n }\n .carousel-inner > .item.next.left,\n .carousel-inner > .item.prev.right,\n .carousel-inner > .item.active {\n -webkit-transform: translate3d(0, 0, 0);\n transform: translate3d(0, 0, 0);\n left: 0;\n }\n}\n.carousel-inner > .active,\n.carousel-inner > .next,\n.carousel-inner > .prev {\n display: block;\n}\n.carousel-inner > .active {\n left: 0;\n}\n.carousel-inner > .next,\n.carousel-inner > .prev {\n position: absolute;\n top: 0;\n width: 100%;\n}\n.carousel-inner > .next {\n left: 100%;\n}\n.carousel-inner > .prev {\n left: -100%;\n}\n.carousel-inner > .next.left,\n.carousel-inner > .prev.right {\n left: 0;\n}\n.carousel-inner > .active.left {\n left: -100%;\n}\n.carousel-inner > .active.right {\n left: 100%;\n}\n.carousel-control {\n position: absolute;\n top: 0;\n left: 0;\n bottom: 0;\n width: 15%;\n opacity: 0.5;\n filter: alpha(opacity=50);\n font-size: 20px;\n color: #fff;\n text-align: center;\n text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n background-color: rgba(0, 0, 0, 0);\n}\n.carousel-control.left {\n background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);\n}\n.carousel-control.right {\n left: auto;\n right: 0;\n background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);\n}\n.carousel-control:hover,\n.carousel-control:focus {\n outline: 0;\n color: #fff;\n text-decoration: none;\n opacity: 0.9;\n filter: alpha(opacity=90);\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-left,\n.carousel-control .glyphicon-chevron-right {\n position: absolute;\n top: 50%;\n margin-top: -10px;\n z-index: 5;\n display: inline-block;\n}\n.carousel-control .icon-prev,\n.carousel-control .glyphicon-chevron-left {\n left: 50%;\n margin-left: -10px;\n}\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-right {\n right: 50%;\n margin-right: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next {\n width: 20px;\n height: 20px;\n line-height: 1;\n font-family: serif;\n}\n.carousel-control .icon-prev:before {\n content: '\\2039';\n}\n.carousel-control .icon-next:before {\n content: '\\203a';\n}\n.carousel-indicators {\n position: absolute;\n bottom: 10px;\n left: 50%;\n z-index: 15;\n width: 60%;\n margin-left: -30%;\n padding-left: 0;\n list-style: none;\n text-align: center;\n}\n.carousel-indicators li {\n display: inline-block;\n width: 10px;\n height: 10px;\n margin: 1px;\n text-indent: -999px;\n border: 1px solid #fff;\n border-radius: 10px;\n cursor: pointer;\n background-color: #000 \\9;\n background-color: rgba(0, 0, 0, 0);\n}\n.carousel-indicators .active {\n margin: 0;\n width: 12px;\n height: 12px;\n background-color: #fff;\n}\n.carousel-caption {\n position: absolute;\n left: 15%;\n right: 15%;\n bottom: 20px;\n z-index: 10;\n padding-top: 20px;\n padding-bottom: 20px;\n color: #fff;\n text-align: center;\n text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n}\n.carousel-caption .btn {\n text-shadow: none;\n}\n@media screen and (min-width: 768px) {\n .carousel-control .glyphicon-chevron-left,\n .carousel-control .glyphicon-chevron-right,\n .carousel-control .icon-prev,\n .carousel-control .icon-next {\n width: 30px;\n height: 30px;\n margin-top: -10px;\n font-size: 30px;\n }\n .carousel-control .glyphicon-chevron-left,\n .carousel-control .icon-prev {\n margin-left: -10px;\n }\n .carousel-control .glyphicon-chevron-right,\n .carousel-control .icon-next {\n margin-right: -10px;\n }\n .carousel-caption {\n left: 20%;\n right: 20%;\n padding-bottom: 30px;\n }\n .carousel-indicators {\n bottom: 20px;\n }\n}\n.clearfix:before,\n.clearfix:after,\n.dl-horizontal dd:before,\n.dl-horizontal dd:after,\n.container:before,\n.container:after,\n.container-fluid:before,\n.container-fluid:after,\n.row:before,\n.row:after,\n.form-horizontal .form-group:before,\n.form-horizontal .form-group:after,\n.btn-toolbar:before,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:before,\n.btn-group-vertical > .btn-group:after,\n.nav:before,\n.nav:after,\n.navbar:before,\n.navbar:after,\n.navbar-header:before,\n.navbar-header:after,\n.navbar-collapse:before,\n.navbar-collapse:after,\n.pager:before,\n.pager:after,\n.panel-body:before,\n.panel-body:after,\n.modal-header:before,\n.modal-header:after,\n.modal-footer:before,\n.modal-footer:after {\n content: \" \";\n display: table;\n}\n.clearfix:after,\n.dl-horizontal dd:after,\n.container:after,\n.container-fluid:after,\n.row:after,\n.form-horizontal .form-group:after,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:after,\n.nav:after,\n.navbar:after,\n.navbar-header:after,\n.navbar-collapse:after,\n.pager:after,\n.panel-body:after,\n.modal-header:after,\n.modal-footer:after {\n clear: both;\n}\n.center-block {\n display: block;\n margin-left: auto;\n margin-right: auto;\n}\n.pull-right {\n float: right !important;\n}\n.pull-left {\n float: left !important;\n}\n.hide {\n display: none !important;\n}\n.show {\n display: block !important;\n}\n.invisible {\n visibility: hidden;\n}\n.text-hide {\n font: 0/0 a;\n color: transparent;\n text-shadow: none;\n background-color: transparent;\n border: 0;\n}\n.hidden {\n display: none !important;\n}\n.affix {\n position: fixed;\n}\n@-ms-viewport {\n width: device-width;\n}\n.visible-xs,\n.visible-sm,\n.visible-md,\n.visible-lg {\n display: none !important;\n}\n.visible-xs-block,\n.visible-xs-inline,\n.visible-xs-inline-block,\n.visible-sm-block,\n.visible-sm-inline,\n.visible-sm-inline-block,\n.visible-md-block,\n.visible-md-inline,\n.visible-md-inline-block,\n.visible-lg-block,\n.visible-lg-inline,\n.visible-lg-inline-block {\n display: none !important;\n}\n@media (max-width: 767px) {\n .visible-xs {\n display: block !important;\n }\n table.visible-xs {\n display: table !important;\n }\n tr.visible-xs {\n display: table-row !important;\n }\n th.visible-xs,\n td.visible-xs {\n display: table-cell !important;\n }\n}\n@media (max-width: 767px) {\n .visible-xs-block {\n display: block !important;\n }\n}\n@media (max-width: 767px) {\n .visible-xs-inline {\n display: inline !important;\n }\n}\n@media (max-width: 767px) {\n .visible-xs-inline-block {\n display: inline-block !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm {\n display: block !important;\n }\n table.visible-sm {\n display: table !important;\n }\n tr.visible-sm {\n display: table-row !important;\n }\n th.visible-sm,\n td.visible-sm {\n display: table-cell !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm-block {\n display: block !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm-inline {\n display: inline !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm-inline-block {\n display: inline-block !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md {\n display: block !important;\n }\n table.visible-md {\n display: table !important;\n }\n tr.visible-md {\n display: table-row !important;\n }\n th.visible-md,\n td.visible-md {\n display: table-cell !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md-block {\n display: block !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md-inline {\n display: inline !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md-inline-block {\n display: inline-block !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg {\n display: block !important;\n }\n table.visible-lg {\n display: table !important;\n }\n tr.visible-lg {\n display: table-row !important;\n }\n th.visible-lg,\n td.visible-lg {\n display: table-cell !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg-block {\n display: block !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg-inline {\n display: inline !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg-inline-block {\n display: inline-block !important;\n }\n}\n@media (max-width: 767px) {\n .hidden-xs {\n display: none !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .hidden-sm {\n display: none !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .hidden-md {\n display: none !important;\n }\n}\n@media (min-width: 1200px) {\n .hidden-lg {\n display: none !important;\n }\n}\n.visible-print {\n display: none !important;\n}\n@media print {\n .visible-print {\n display: block !important;\n }\n table.visible-print {\n display: table !important;\n }\n tr.visible-print {\n display: table-row !important;\n }\n th.visible-print,\n td.visible-print {\n display: table-cell !important;\n }\n}\n.visible-print-block {\n display: none !important;\n}\n@media print {\n .visible-print-block {\n display: block !important;\n }\n}\n.visible-print-inline {\n display: none !important;\n}\n@media print {\n .visible-print-inline {\n display: inline !important;\n }\n}\n.visible-print-inline-block {\n display: none !important;\n}\n@media print {\n .visible-print-inline-block {\n display: inline-block !important;\n }\n}\n@media print {\n .hidden-print {\n display: none !important;\n }\n}\n/*# sourceMappingURL=bootstrap.css.map */","/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */\n\n//\n// 1. Set default font family to sans-serif.\n// 2. Prevent iOS and IE text size adjust after device orientation change,\n// without disabling user zoom.\n//\n\nhtml {\n font-family: sans-serif; // 1\n -ms-text-size-adjust: 100%; // 2\n -webkit-text-size-adjust: 100%; // 2\n}\n\n//\n// Remove default margin.\n//\n\nbody {\n margin: 0;\n}\n\n// HTML5 display definitions\n// ==========================================================================\n\n//\n// Correct `block` display not defined for any HTML5 element in IE 8/9.\n// Correct `block` display not defined for `details` or `summary` in IE 10/11\n// and Firefox.\n// Correct `block` display not defined for `main` in IE 11.\n//\n\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nmenu,\nnav,\nsection,\nsummary {\n display: block;\n}\n\n//\n// 1. Correct `inline-block` display not defined in IE 8/9.\n// 2. Normalize vertical alignment of `progress` in Chrome, Firefox, and Opera.\n//\n\naudio,\ncanvas,\nprogress,\nvideo {\n display: inline-block; // 1\n vertical-align: baseline; // 2\n}\n\n//\n// Prevent modern browsers from displaying `audio` without controls.\n// Remove excess height in iOS 5 devices.\n//\n\naudio:not([controls]) {\n display: none;\n height: 0;\n}\n\n//\n// Address `[hidden]` styling not present in IE 8/9/10.\n// Hide the `template` element in IE 8/9/10/11, Safari, and Firefox < 22.\n//\n\n[hidden],\ntemplate {\n display: none;\n}\n\n// Links\n// ==========================================================================\n\n//\n// Remove the gray background color from active links in IE 10.\n//\n\na {\n background-color: transparent;\n}\n\n//\n// Improve readability of focused elements when they are also in an\n// active/hover state.\n//\n\na:active,\na:hover {\n outline: 0;\n}\n\n// Text-level semantics\n// ==========================================================================\n\n//\n// Address styling not present in IE 8/9/10/11, Safari, and Chrome.\n//\n\nabbr[title] {\n border-bottom: 1px dotted;\n}\n\n//\n// Address style set to `bolder` in Firefox 4+, Safari, and Chrome.\n//\n\nb,\nstrong {\n font-weight: bold;\n}\n\n//\n// Address styling not present in Safari and Chrome.\n//\n\ndfn {\n font-style: italic;\n}\n\n//\n// Address variable `h1` font-size and margin within `section` and `article`\n// contexts in Firefox 4+, Safari, and Chrome.\n//\n\nh1 {\n font-size: 2em;\n margin: 0.67em 0;\n}\n\n//\n// Address styling not present in IE 8/9.\n//\n\nmark {\n background: #ff0;\n color: #000;\n}\n\n//\n// Address inconsistent and variable font size in all browsers.\n//\n\nsmall {\n font-size: 80%;\n}\n\n//\n// Prevent `sub` and `sup` affecting `line-height` in all browsers.\n//\n\nsub,\nsup {\n font-size: 75%;\n line-height: 0;\n position: relative;\n vertical-align: baseline;\n}\n\nsup {\n top: -0.5em;\n}\n\nsub {\n bottom: -0.25em;\n}\n\n// Embedded content\n// ==========================================================================\n\n//\n// Remove border when inside `a` element in IE 8/9/10.\n//\n\nimg {\n border: 0;\n}\n\n//\n// Correct overflow not hidden in IE 9/10/11.\n//\n\nsvg:not(:root) {\n overflow: hidden;\n}\n\n// Grouping content\n// ==========================================================================\n\n//\n// Address margin not present in IE 8/9 and Safari.\n//\n\nfigure {\n margin: 1em 40px;\n}\n\n//\n// Address differences between Firefox and other browsers.\n//\n\nhr {\n box-sizing: content-box;\n height: 0;\n}\n\n//\n// Contain overflow in all browsers.\n//\n\npre {\n overflow: auto;\n}\n\n//\n// Address odd `em`-unit font size rendering in all browsers.\n//\n\ncode,\nkbd,\npre,\nsamp {\n font-family: monospace, monospace;\n font-size: 1em;\n}\n\n// Forms\n// ==========================================================================\n\n//\n// Known limitation: by default, Chrome and Safari on OS X allow very limited\n// styling of `select`, unless a `border` property is set.\n//\n\n//\n// 1. Correct color not being inherited.\n// Known issue: affects color of disabled elements.\n// 2. Correct font properties not being inherited.\n// 3. Address margins set differently in Firefox 4+, Safari, and Chrome.\n//\n\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n color: inherit; // 1\n font: inherit; // 2\n margin: 0; // 3\n}\n\n//\n// Address `overflow` set to `hidden` in IE 8/9/10/11.\n//\n\nbutton {\n overflow: visible;\n}\n\n//\n// Address inconsistent `text-transform` inheritance for `button` and `select`.\n// All other form control elements do not inherit `text-transform` values.\n// Correct `button` style inheritance in Firefox, IE 8/9/10/11, and Opera.\n// Correct `select` style inheritance in Firefox.\n//\n\nbutton,\nselect {\n text-transform: none;\n}\n\n//\n// 1. Avoid the WebKit bug in Android 4.0.* where (2) destroys native `audio`\n// and `video` controls.\n// 2. Correct inability to style clickable `input` types in iOS.\n// 3. Improve usability and consistency of cursor style between image-type\n// `input` and others.\n//\n\nbutton,\nhtml input[type=\"button\"], // 1\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n -webkit-appearance: button; // 2\n cursor: pointer; // 3\n}\n\n//\n// Re-set default cursor for disabled elements.\n//\n\nbutton[disabled],\nhtml input[disabled] {\n cursor: default;\n}\n\n//\n// Remove inner padding and border in Firefox 4+.\n//\n\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n border: 0;\n padding: 0;\n}\n\n//\n// Address Firefox 4+ setting `line-height` on `input` using `!important` in\n// the UA stylesheet.\n//\n\ninput {\n line-height: normal;\n}\n\n//\n// It's recommended that you don't attempt to style these elements.\n// Firefox's implementation doesn't respect box-sizing, padding, or width.\n//\n// 1. Address box sizing set to `content-box` in IE 8/9/10.\n// 2. Remove excess padding in IE 8/9/10.\n//\n\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n box-sizing: border-box; // 1\n padding: 0; // 2\n}\n\n//\n// Fix the cursor style for Chrome's increment/decrement buttons. For certain\n// `font-size` values of the `input`, it causes the cursor style of the\n// decrement button to change from `default` to `text`.\n//\n\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n//\n// 1. Address `appearance` set to `searchfield` in Safari and Chrome.\n// 2. Address `box-sizing` set to `border-box` in Safari and Chrome.\n//\n\ninput[type=\"search\"] {\n -webkit-appearance: textfield; // 1\n box-sizing: content-box; //2\n}\n\n//\n// Remove inner padding and search cancel button in Safari and Chrome on OS X.\n// Safari (but not Chrome) clips the cancel button when the search input has\n// padding (and `textfield` appearance).\n//\n\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n//\n// Define consistent border, margin, and padding.\n//\n\nfieldset {\n border: 1px solid #c0c0c0;\n margin: 0 2px;\n padding: 0.35em 0.625em 0.75em;\n}\n\n//\n// 1. Correct `color` not being inherited in IE 8/9/10/11.\n// 2. Remove padding so people aren't caught out if they zero out fieldsets.\n//\n\nlegend {\n border: 0; // 1\n padding: 0; // 2\n}\n\n//\n// Remove default vertical scrollbar in IE 8/9/10/11.\n//\n\ntextarea {\n overflow: auto;\n}\n\n//\n// Don't inherit the `font-weight` (applied by a rule above).\n// NOTE: the default cannot safely be changed in Chrome and Safari on OS X.\n//\n\noptgroup {\n font-weight: bold;\n}\n\n// Tables\n// ==========================================================================\n\n//\n// Remove most spacing between table cells.\n//\n\ntable {\n border-collapse: collapse;\n border-spacing: 0;\n}\n\ntd,\nth {\n padding: 0;\n}\n","/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n\n// ==========================================================================\n// Print styles.\n// Inlined to avoid the additional HTTP request: h5bp.com/r\n// ==========================================================================\n\n@media print {\n *,\n *:before,\n *:after {\n background: transparent !important;\n color: #000 !important; // Black prints faster: h5bp.com/s\n box-shadow: none !important;\n text-shadow: none !important;\n }\n\n a,\n a:visited {\n text-decoration: underline;\n }\n\n a[href]:after {\n content: \" (\" attr(href) \")\";\n }\n\n abbr[title]:after {\n content: \" (\" attr(title) \")\";\n }\n\n // Don't show links that are fragment identifiers,\n // or use the `javascript:` pseudo protocol\n a[href^=\"#\"]:after,\n a[href^=\"javascript:\"]:after {\n content: \"\";\n }\n\n pre,\n blockquote {\n border: 1px solid #999;\n page-break-inside: avoid;\n }\n\n thead {\n display: table-header-group; // h5bp.com/t\n }\n\n tr,\n img {\n page-break-inside: avoid;\n }\n\n img {\n max-width: 100% !important;\n }\n\n p,\n h2,\n h3 {\n orphans: 3;\n widows: 3;\n }\n\n h2,\n h3 {\n page-break-after: avoid;\n }\n\n // Bootstrap specific changes start\n\n // Bootstrap components\n .navbar {\n display: none;\n }\n .btn,\n .dropup > .btn {\n > .caret {\n border-top-color: #000 !important;\n }\n }\n .label {\n border: 1px solid #000;\n }\n\n .table {\n border-collapse: collapse !important;\n\n td,\n th {\n background-color: #fff !important;\n }\n }\n .table-bordered {\n th,\n td {\n border: 1px solid #ddd !important;\n }\n }\n\n // Bootstrap specific changes end\n}\n","//\n// Glyphicons for Bootstrap\n//\n// Since icons are fonts, they can be placed anywhere text is placed and are\n// thus automatically sized to match the surrounding child. To use, create an\n// inline element with the appropriate classes, like so:\n//\n// Star\n\n// Import the fonts\n@font-face {\n font-family: 'Glyphicons Halflings';\n src: url('@{icon-font-path}@{icon-font-name}.eot');\n src: url('@{icon-font-path}@{icon-font-name}.eot?#iefix') format('embedded-opentype'),\n url('@{icon-font-path}@{icon-font-name}.woff2') format('woff2'),\n url('@{icon-font-path}@{icon-font-name}.woff') format('woff'),\n url('@{icon-font-path}@{icon-font-name}.ttf') format('truetype'),\n url('@{icon-font-path}@{icon-font-name}.svg#@{icon-font-svg-id}') format('svg');\n}\n\n// Catchall baseclass\n.glyphicon {\n position: relative;\n top: 1px;\n display: inline-block;\n font-family: 'Glyphicons Halflings';\n font-style: normal;\n font-weight: normal;\n line-height: 1;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n}\n\n// Individual icons\n.glyphicon-asterisk { &:before { content: \"\\002a\"; } }\n.glyphicon-plus { &:before { content: \"\\002b\"; } }\n.glyphicon-euro,\n.glyphicon-eur { &:before { content: \"\\20ac\"; } }\n.glyphicon-minus { &:before { content: \"\\2212\"; } }\n.glyphicon-cloud { &:before { content: \"\\2601\"; } }\n.glyphicon-envelope { &:before { content: \"\\2709\"; } }\n.glyphicon-pencil { &:before { content: \"\\270f\"; } }\n.glyphicon-glass { &:before { content: \"\\e001\"; } }\n.glyphicon-music { &:before { content: \"\\e002\"; } }\n.glyphicon-search { &:before { content: \"\\e003\"; } }\n.glyphicon-heart { &:before { content: \"\\e005\"; } }\n.glyphicon-star { &:before { content: \"\\e006\"; } }\n.glyphicon-star-empty { &:before { content: \"\\e007\"; } }\n.glyphicon-user { &:before { content: \"\\e008\"; } }\n.glyphicon-film { &:before { content: \"\\e009\"; } }\n.glyphicon-th-large { &:before { content: \"\\e010\"; } }\n.glyphicon-th { &:before { content: \"\\e011\"; } }\n.glyphicon-th-list { &:before { content: \"\\e012\"; } }\n.glyphicon-ok { &:before { content: \"\\e013\"; } }\n.glyphicon-remove { &:before { content: \"\\e014\"; } }\n.glyphicon-zoom-in { &:before { content: \"\\e015\"; } }\n.glyphicon-zoom-out { &:before { content: \"\\e016\"; } }\n.glyphicon-off { &:before { content: \"\\e017\"; } }\n.glyphicon-signal { &:before { content: \"\\e018\"; } }\n.glyphicon-cog { &:before { content: \"\\e019\"; } }\n.glyphicon-trash { &:before { content: \"\\e020\"; } }\n.glyphicon-home { &:before { content: \"\\e021\"; } }\n.glyphicon-file { &:before { content: \"\\e022\"; } }\n.glyphicon-time { &:before { content: \"\\e023\"; } }\n.glyphicon-road { &:before { content: \"\\e024\"; } }\n.glyphicon-download-alt { &:before { content: \"\\e025\"; } }\n.glyphicon-download { &:before { content: \"\\e026\"; } }\n.glyphicon-upload { &:before { content: \"\\e027\"; } }\n.glyphicon-inbox { &:before { content: \"\\e028\"; } }\n.glyphicon-play-circle { &:before { content: \"\\e029\"; } }\n.glyphicon-repeat { &:before { content: \"\\e030\"; } }\n.glyphicon-refresh { &:before { content: \"\\e031\"; } }\n.glyphicon-list-alt { &:before { content: \"\\e032\"; } }\n.glyphicon-lock { &:before { content: \"\\e033\"; } }\n.glyphicon-flag { &:before { content: \"\\e034\"; } }\n.glyphicon-headphones { &:before { content: \"\\e035\"; } }\n.glyphicon-volume-off { &:before { content: \"\\e036\"; } }\n.glyphicon-volume-down { &:before { content: \"\\e037\"; } }\n.glyphicon-volume-up { &:before { content: \"\\e038\"; } }\n.glyphicon-qrcode { &:before { content: \"\\e039\"; } }\n.glyphicon-barcode { &:before { content: \"\\e040\"; } }\n.glyphicon-tag { &:before { content: \"\\e041\"; } }\n.glyphicon-tags { &:before { content: \"\\e042\"; } }\n.glyphicon-book { &:before { content: \"\\e043\"; } }\n.glyphicon-bookmark { &:before { content: \"\\e044\"; } }\n.glyphicon-print { &:before { content: \"\\e045\"; } }\n.glyphicon-camera { &:before { content: \"\\e046\"; } }\n.glyphicon-font { &:before { content: \"\\e047\"; } }\n.glyphicon-bold { &:before { content: \"\\e048\"; } }\n.glyphicon-italic { &:before { content: \"\\e049\"; } }\n.glyphicon-text-height { &:before { content: \"\\e050\"; } }\n.glyphicon-text-width { &:before { content: \"\\e051\"; } }\n.glyphicon-align-left { &:before { content: \"\\e052\"; } }\n.glyphicon-align-center { &:before { content: \"\\e053\"; } }\n.glyphicon-align-right { &:before { content: \"\\e054\"; } }\n.glyphicon-align-justify { &:before { content: \"\\e055\"; } }\n.glyphicon-list { &:before { content: \"\\e056\"; } }\n.glyphicon-indent-left { &:before { content: \"\\e057\"; } }\n.glyphicon-indent-right { &:before { content: \"\\e058\"; } }\n.glyphicon-facetime-video { &:before { content: \"\\e059\"; } }\n.glyphicon-picture { &:before { content: \"\\e060\"; } }\n.glyphicon-map-marker { &:before { content: \"\\e062\"; } }\n.glyphicon-adjust { &:before { content: \"\\e063\"; } }\n.glyphicon-tint { &:before { content: \"\\e064\"; } }\n.glyphicon-edit { &:before { content: \"\\e065\"; } }\n.glyphicon-share { &:before { content: \"\\e066\"; } }\n.glyphicon-check { &:before { content: \"\\e067\"; } }\n.glyphicon-move { &:before { content: \"\\e068\"; } }\n.glyphicon-step-backward { &:before { content: \"\\e069\"; } }\n.glyphicon-fast-backward { &:before { content: \"\\e070\"; } }\n.glyphicon-backward { &:before { content: \"\\e071\"; } }\n.glyphicon-play { &:before { content: \"\\e072\"; } }\n.glyphicon-pause { &:before { content: \"\\e073\"; } }\n.glyphicon-stop { &:before { content: \"\\e074\"; } }\n.glyphicon-forward { &:before { content: \"\\e075\"; } }\n.glyphicon-fast-forward { &:before { content: \"\\e076\"; } }\n.glyphicon-step-forward { &:before { content: \"\\e077\"; } }\n.glyphicon-eject { &:before { content: \"\\e078\"; } }\n.glyphicon-chevron-left { &:before { content: \"\\e079\"; } }\n.glyphicon-chevron-right { &:before { content: \"\\e080\"; } }\n.glyphicon-plus-sign { &:before { content: \"\\e081\"; } }\n.glyphicon-minus-sign { &:before { content: \"\\e082\"; } }\n.glyphicon-remove-sign { &:before { content: \"\\e083\"; } }\n.glyphicon-ok-sign { &:before { content: \"\\e084\"; } }\n.glyphicon-question-sign { &:before { content: \"\\e085\"; } }\n.glyphicon-info-sign { &:before { content: \"\\e086\"; } }\n.glyphicon-screenshot { &:before { content: \"\\e087\"; } }\n.glyphicon-remove-circle { &:before { content: \"\\e088\"; } }\n.glyphicon-ok-circle { &:before { content: \"\\e089\"; } }\n.glyphicon-ban-circle { &:before { content: \"\\e090\"; } }\n.glyphicon-arrow-left { &:before { content: \"\\e091\"; } }\n.glyphicon-arrow-right { &:before { content: \"\\e092\"; } }\n.glyphicon-arrow-up { &:before { content: \"\\e093\"; } }\n.glyphicon-arrow-down { &:before { content: \"\\e094\"; } }\n.glyphicon-share-alt { &:before { content: \"\\e095\"; } }\n.glyphicon-resize-full { &:before { content: \"\\e096\"; } }\n.glyphicon-resize-small { &:before { content: \"\\e097\"; } }\n.glyphicon-exclamation-sign { &:before { content: \"\\e101\"; } }\n.glyphicon-gift { &:before { content: \"\\e102\"; } }\n.glyphicon-leaf { &:before { content: \"\\e103\"; } }\n.glyphicon-fire { &:before { content: \"\\e104\"; } }\n.glyphicon-eye-open { &:before { content: \"\\e105\"; } }\n.glyphicon-eye-close { &:before { content: \"\\e106\"; } }\n.glyphicon-warning-sign { &:before { content: \"\\e107\"; } }\n.glyphicon-plane { &:before { content: \"\\e108\"; } }\n.glyphicon-calendar { &:before { content: \"\\e109\"; } }\n.glyphicon-random { &:before { content: \"\\e110\"; } }\n.glyphicon-comment { &:before { content: \"\\e111\"; } }\n.glyphicon-magnet { &:before { content: \"\\e112\"; } }\n.glyphicon-chevron-up { &:before { content: \"\\e113\"; } }\n.glyphicon-chevron-down { &:before { content: \"\\e114\"; } }\n.glyphicon-retweet { &:before { content: \"\\e115\"; } }\n.glyphicon-shopping-cart { &:before { content: \"\\e116\"; } }\n.glyphicon-folder-close { &:before { content: \"\\e117\"; } }\n.glyphicon-folder-open { &:before { content: \"\\e118\"; } }\n.glyphicon-resize-vertical { &:before { content: \"\\e119\"; } }\n.glyphicon-resize-horizontal { &:before { content: \"\\e120\"; } }\n.glyphicon-hdd { &:before { content: \"\\e121\"; } }\n.glyphicon-bullhorn { &:before { content: \"\\e122\"; } }\n.glyphicon-bell { &:before { content: \"\\e123\"; } }\n.glyphicon-certificate { &:before { content: \"\\e124\"; } }\n.glyphicon-thumbs-up { &:before { content: \"\\e125\"; } }\n.glyphicon-thumbs-down { &:before { content: \"\\e126\"; } }\n.glyphicon-hand-right { &:before { content: \"\\e127\"; } }\n.glyphicon-hand-left { &:before { content: \"\\e128\"; } }\n.glyphicon-hand-up { &:before { content: \"\\e129\"; } }\n.glyphicon-hand-down { &:before { content: \"\\e130\"; } }\n.glyphicon-circle-arrow-right { &:before { content: \"\\e131\"; } }\n.glyphicon-circle-arrow-left { &:before { content: \"\\e132\"; } }\n.glyphicon-circle-arrow-up { &:before { content: \"\\e133\"; } }\n.glyphicon-circle-arrow-down { &:before { content: \"\\e134\"; } }\n.glyphicon-globe { &:before { content: \"\\e135\"; } }\n.glyphicon-wrench { &:before { content: \"\\e136\"; } }\n.glyphicon-tasks { &:before { content: \"\\e137\"; } }\n.glyphicon-filter { &:before { content: \"\\e138\"; } }\n.glyphicon-briefcase { &:before { content: \"\\e139\"; } }\n.glyphicon-fullscreen { &:before { content: \"\\e140\"; } }\n.glyphicon-dashboard { &:before { content: \"\\e141\"; } }\n.glyphicon-paperclip { &:before { content: \"\\e142\"; } }\n.glyphicon-heart-empty { &:before { content: \"\\e143\"; } }\n.glyphicon-link { &:before { content: \"\\e144\"; } }\n.glyphicon-phone { &:before { content: \"\\e145\"; } }\n.glyphicon-pushpin { &:before { content: \"\\e146\"; } }\n.glyphicon-usd { &:before { content: \"\\e148\"; } }\n.glyphicon-gbp { &:before { content: \"\\e149\"; } }\n.glyphicon-sort { &:before { content: \"\\e150\"; } }\n.glyphicon-sort-by-alphabet { &:before { content: \"\\e151\"; } }\n.glyphicon-sort-by-alphabet-alt { &:before { content: \"\\e152\"; } }\n.glyphicon-sort-by-order { &:before { content: \"\\e153\"; } }\n.glyphicon-sort-by-order-alt { &:before { content: \"\\e154\"; } }\n.glyphicon-sort-by-attributes { &:before { content: \"\\e155\"; } }\n.glyphicon-sort-by-attributes-alt { &:before { content: \"\\e156\"; } }\n.glyphicon-unchecked { &:before { content: \"\\e157\"; } }\n.glyphicon-expand { &:before { content: \"\\e158\"; } }\n.glyphicon-collapse-down { &:before { content: \"\\e159\"; } }\n.glyphicon-collapse-up { &:before { content: \"\\e160\"; } }\n.glyphicon-log-in { &:before { content: \"\\e161\"; } }\n.glyphicon-flash { &:before { content: \"\\e162\"; } }\n.glyphicon-log-out { &:before { content: \"\\e163\"; } }\n.glyphicon-new-window { &:before { content: \"\\e164\"; } }\n.glyphicon-record { &:before { content: \"\\e165\"; } }\n.glyphicon-save { &:before { content: \"\\e166\"; } }\n.glyphicon-open { &:before { content: \"\\e167\"; } }\n.glyphicon-saved { &:before { content: \"\\e168\"; } }\n.glyphicon-import { &:before { content: \"\\e169\"; } }\n.glyphicon-export { &:before { content: \"\\e170\"; } }\n.glyphicon-send { &:before { content: \"\\e171\"; } }\n.glyphicon-floppy-disk { &:before { content: \"\\e172\"; } }\n.glyphicon-floppy-saved { &:before { content: \"\\e173\"; } }\n.glyphicon-floppy-remove { &:before { content: \"\\e174\"; } }\n.glyphicon-floppy-save { &:before { content: \"\\e175\"; } }\n.glyphicon-floppy-open { &:before { content: \"\\e176\"; } }\n.glyphicon-credit-card { &:before { content: \"\\e177\"; } }\n.glyphicon-transfer { &:before { content: \"\\e178\"; } }\n.glyphicon-cutlery { &:before { content: \"\\e179\"; } }\n.glyphicon-header { &:before { content: \"\\e180\"; } }\n.glyphicon-compressed { &:before { content: \"\\e181\"; } }\n.glyphicon-earphone { &:before { content: \"\\e182\"; } }\n.glyphicon-phone-alt { &:before { content: \"\\e183\"; } }\n.glyphicon-tower { &:before { content: \"\\e184\"; } }\n.glyphicon-stats { &:before { content: \"\\e185\"; } }\n.glyphicon-sd-video { &:before { content: \"\\e186\"; } }\n.glyphicon-hd-video { &:before { content: \"\\e187\"; } }\n.glyphicon-subtitles { &:before { content: \"\\e188\"; } }\n.glyphicon-sound-stereo { &:before { content: \"\\e189\"; } }\n.glyphicon-sound-dolby { &:before { content: \"\\e190\"; } }\n.glyphicon-sound-5-1 { &:before { content: \"\\e191\"; } }\n.glyphicon-sound-6-1 { &:before { content: \"\\e192\"; } }\n.glyphicon-sound-7-1 { &:before { content: \"\\e193\"; } }\n.glyphicon-copyright-mark { &:before { content: \"\\e194\"; } }\n.glyphicon-registration-mark { &:before { content: \"\\e195\"; } }\n.glyphicon-cloud-download { &:before { content: \"\\e197\"; } }\n.glyphicon-cloud-upload { &:before { content: \"\\e198\"; } }\n.glyphicon-tree-conifer { &:before { content: \"\\e199\"; } }\n.glyphicon-tree-deciduous { &:before { content: \"\\e200\"; } }\n.glyphicon-cd { &:before { content: \"\\e201\"; } }\n.glyphicon-save-file { &:before { content: \"\\e202\"; } }\n.glyphicon-open-file { &:before { content: \"\\e203\"; } }\n.glyphicon-level-up { &:before { content: \"\\e204\"; } }\n.glyphicon-copy { &:before { content: \"\\e205\"; } }\n.glyphicon-paste { &:before { content: \"\\e206\"; } }\n// The following 2 Glyphicons are omitted for the time being because\n// they currently use Unicode codepoints that are outside the\n// Basic Multilingual Plane (BMP). Older buggy versions of WebKit can't handle\n// non-BMP codepoints in CSS string escapes, and thus can't display these two icons.\n// Notably, the bug affects some older versions of the Android Browser.\n// More info: https://github.com/twbs/bootstrap/issues/10106\n// .glyphicon-door { &:before { content: \"\\1f6aa\"; } }\n// .glyphicon-key { &:before { content: \"\\1f511\"; } }\n.glyphicon-alert { &:before { content: \"\\e209\"; } }\n.glyphicon-equalizer { &:before { content: \"\\e210\"; } }\n.glyphicon-king { &:before { content: \"\\e211\"; } }\n.glyphicon-queen { &:before { content: \"\\e212\"; } }\n.glyphicon-pawn { &:before { content: \"\\e213\"; } }\n.glyphicon-bishop { &:before { content: \"\\e214\"; } }\n.glyphicon-knight { &:before { content: \"\\e215\"; } }\n.glyphicon-baby-formula { &:before { content: \"\\e216\"; } }\n.glyphicon-tent { &:before { content: \"\\26fa\"; } }\n.glyphicon-blackboard { &:before { content: \"\\e218\"; } }\n.glyphicon-bed { &:before { content: \"\\e219\"; } }\n.glyphicon-apple { &:before { content: \"\\f8ff\"; } }\n.glyphicon-erase { &:before { content: \"\\e221\"; } }\n.glyphicon-hourglass { &:before { content: \"\\231b\"; } }\n.glyphicon-lamp { &:before { content: \"\\e223\"; } }\n.glyphicon-duplicate { &:before { content: \"\\e224\"; } }\n.glyphicon-piggy-bank { &:before { content: \"\\e225\"; } }\n.glyphicon-scissors { &:before { content: \"\\e226\"; } }\n.glyphicon-bitcoin { &:before { content: \"\\e227\"; } }\n.glyphicon-btc { &:before { content: \"\\e227\"; } }\n.glyphicon-xbt { &:before { content: \"\\e227\"; } }\n.glyphicon-yen { &:before { content: \"\\00a5\"; } }\n.glyphicon-jpy { &:before { content: \"\\00a5\"; } }\n.glyphicon-ruble { &:before { content: \"\\20bd\"; } }\n.glyphicon-rub { &:before { content: \"\\20bd\"; } }\n.glyphicon-scale { &:before { content: \"\\e230\"; } }\n.glyphicon-ice-lolly { &:before { content: \"\\e231\"; } }\n.glyphicon-ice-lolly-tasted { &:before { content: \"\\e232\"; } }\n.glyphicon-education { &:before { content: \"\\e233\"; } }\n.glyphicon-option-horizontal { &:before { content: \"\\e234\"; } }\n.glyphicon-option-vertical { &:before { content: \"\\e235\"; } }\n.glyphicon-menu-hamburger { &:before { content: \"\\e236\"; } }\n.glyphicon-modal-window { &:before { content: \"\\e237\"; } }\n.glyphicon-oil { &:before { content: \"\\e238\"; } }\n.glyphicon-grain { &:before { content: \"\\e239\"; } }\n.glyphicon-sunglasses { &:before { content: \"\\e240\"; } }\n.glyphicon-text-size { &:before { content: \"\\e241\"; } }\n.glyphicon-text-color { &:before { content: \"\\e242\"; } }\n.glyphicon-text-background { &:before { content: \"\\e243\"; } }\n.glyphicon-object-align-top { &:before { content: \"\\e244\"; } }\n.glyphicon-object-align-bottom { &:before { content: \"\\e245\"; } }\n.glyphicon-object-align-horizontal{ &:before { content: \"\\e246\"; } }\n.glyphicon-object-align-left { &:before { content: \"\\e247\"; } }\n.glyphicon-object-align-vertical { &:before { content: \"\\e248\"; } }\n.glyphicon-object-align-right { &:before { content: \"\\e249\"; } }\n.glyphicon-triangle-right { &:before { content: \"\\e250\"; } }\n.glyphicon-triangle-left { &:before { content: \"\\e251\"; } }\n.glyphicon-triangle-bottom { &:before { content: \"\\e252\"; } }\n.glyphicon-triangle-top { &:before { content: \"\\e253\"; } }\n.glyphicon-console { &:before { content: \"\\e254\"; } }\n.glyphicon-superscript { &:before { content: \"\\e255\"; } }\n.glyphicon-subscript { &:before { content: \"\\e256\"; } }\n.glyphicon-menu-left { &:before { content: \"\\e257\"; } }\n.glyphicon-menu-right { &:before { content: \"\\e258\"; } }\n.glyphicon-menu-down { &:before { content: \"\\e259\"; } }\n.glyphicon-menu-up { &:before { content: \"\\e260\"; } }\n","//\n// Scaffolding\n// --------------------------------------------------\n\n\n// Reset the box-sizing\n//\n// Heads up! This reset may cause conflicts with some third-party widgets.\n// For recommendations on resolving such conflicts, see\n// http://getbootstrap.com/getting-started/#third-box-sizing\n* {\n .box-sizing(border-box);\n}\n*:before,\n*:after {\n .box-sizing(border-box);\n}\n\n\n// Body reset\n\nhtml {\n font-size: 10px;\n -webkit-tap-highlight-color: rgba(0,0,0,0);\n}\n\nbody {\n font-family: @font-family-base;\n font-size: @font-size-base;\n line-height: @line-height-base;\n color: @text-color;\n background-color: @body-bg;\n}\n\n// Reset fonts for relevant elements\ninput,\nbutton,\nselect,\ntextarea {\n font-family: inherit;\n font-size: inherit;\n line-height: inherit;\n}\n\n\n// Links\n\na {\n color: @link-color;\n text-decoration: none;\n\n &:hover,\n &:focus {\n color: @link-hover-color;\n text-decoration: @link-hover-decoration;\n }\n\n &:focus {\n .tab-focus();\n }\n}\n\n\n// Figures\n//\n// We reset this here because previously Normalize had no `figure` margins. This\n// ensures we don't break anyone's use of the element.\n\nfigure {\n margin: 0;\n}\n\n\n// Images\n\nimg {\n vertical-align: middle;\n}\n\n// Responsive images (ensure images don't scale beyond their parents)\n.img-responsive {\n .img-responsive();\n}\n\n// Rounded corners\n.img-rounded {\n border-radius: @border-radius-large;\n}\n\n// Image thumbnails\n//\n// Heads up! This is mixin-ed into thumbnails.less for `.thumbnail`.\n.img-thumbnail {\n padding: @thumbnail-padding;\n line-height: @line-height-base;\n background-color: @thumbnail-bg;\n border: 1px solid @thumbnail-border;\n border-radius: @thumbnail-border-radius;\n .transition(all .2s ease-in-out);\n\n // Keep them at most 100% wide\n .img-responsive(inline-block);\n}\n\n// Perfect circle\n.img-circle {\n border-radius: 50%; // set radius in percents\n}\n\n\n// Horizontal rules\n\nhr {\n margin-top: @line-height-computed;\n margin-bottom: @line-height-computed;\n border: 0;\n border-top: 1px solid @hr-border;\n}\n\n\n// Only display content to screen readers\n//\n// See: http://a11yproject.com/posts/how-to-hide-content\n\n.sr-only {\n position: absolute;\n width: 1px;\n height: 1px;\n margin: -1px;\n padding: 0;\n overflow: hidden;\n clip: rect(0,0,0,0);\n border: 0;\n}\n\n// Use in conjunction with .sr-only to only display content when it's focused.\n// Useful for \"Skip to main content\" links; see http://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n// Credit: HTML5 Boilerplate\n\n.sr-only-focusable {\n &:active,\n &:focus {\n position: static;\n width: auto;\n height: auto;\n margin: 0;\n overflow: visible;\n clip: auto;\n }\n}\n\n\n// iOS \"clickable elements\" fix for role=\"button\"\n//\n// Fixes \"clickability\" issue (and more generally, the firing of events such as focus as well)\n// for traditionally non-focusable elements with role=\"button\"\n// see https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n\n[role=\"button\"] {\n cursor: pointer;\n}\n","// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n -webkit-animation: @animation;\n -o-animation: @animation;\n animation: @animation;\n}\n.animation-name(@name) {\n -webkit-animation-name: @name;\n animation-name: @name;\n}\n.animation-duration(@duration) {\n -webkit-animation-duration: @duration;\n animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n -webkit-animation-timing-function: @timing-function;\n animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n -webkit-animation-delay: @delay;\n animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n -webkit-animation-iteration-count: @iteration-count;\n animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n -webkit-animation-direction: @direction;\n animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n -webkit-animation-fill-mode: @fill-mode;\n animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n -webkit-backface-visibility: @visibility;\n -moz-backface-visibility: @visibility;\n backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n -webkit-box-sizing: @boxmodel;\n -moz-box-sizing: @boxmodel;\n box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n -webkit-column-count: @column-count;\n -moz-column-count: @column-count;\n column-count: @column-count;\n -webkit-column-gap: @column-gap;\n -moz-column-gap: @column-gap;\n column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n word-wrap: break-word;\n -webkit-hyphens: @mode;\n -moz-hyphens: @mode;\n -ms-hyphens: @mode; // IE10+\n -o-hyphens: @mode;\n hyphens: @mode;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n // Firefox\n &::-moz-placeholder {\n color: @color;\n opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n }\n &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n &::-webkit-input-placeholder { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n -webkit-transform: scale(@ratio);\n -ms-transform: scale(@ratio); // IE9 only\n -o-transform: scale(@ratio);\n transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n -webkit-transform: scale(@ratioX, @ratioY);\n -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n -o-transform: scale(@ratioX, @ratioY);\n transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n -webkit-transform: scaleX(@ratio);\n -ms-transform: scaleX(@ratio); // IE9 only\n -o-transform: scaleX(@ratio);\n transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n -webkit-transform: scaleY(@ratio);\n -ms-transform: scaleY(@ratio); // IE9 only\n -o-transform: scaleY(@ratio);\n transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n -webkit-transform: skewX(@x) skewY(@y);\n -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n -o-transform: skewX(@x) skewY(@y);\n transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n -webkit-transform: translate(@x, @y);\n -ms-transform: translate(@x, @y); // IE9 only\n -o-transform: translate(@x, @y);\n transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n -webkit-transform: translate3d(@x, @y, @z);\n transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n -webkit-transform: rotate(@degrees);\n -ms-transform: rotate(@degrees); // IE9 only\n -o-transform: rotate(@degrees);\n transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n -webkit-transform: rotateX(@degrees);\n -ms-transform: rotateX(@degrees); // IE9 only\n -o-transform: rotateX(@degrees);\n transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n -webkit-transform: rotateY(@degrees);\n -ms-transform: rotateY(@degrees); // IE9 only\n -o-transform: rotateY(@degrees);\n transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n -webkit-perspective: @perspective;\n -moz-perspective: @perspective;\n perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n -webkit-perspective-origin: @perspective;\n -moz-perspective-origin: @perspective;\n perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n -webkit-transform-origin: @origin;\n -moz-transform-origin: @origin;\n -ms-transform-origin: @origin; // IE9 only\n transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n -webkit-transition: @transition;\n -o-transition: @transition;\n transition: @transition;\n}\n.transition-property(@transition-property) {\n -webkit-transition-property: @transition-property;\n transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n -webkit-transition-delay: @transition-delay;\n transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n -webkit-transition-duration: @transition-duration;\n transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n -webkit-transition-timing-function: @timing-function;\n transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n -webkit-transition: -webkit-transform @transition;\n -moz-transition: -moz-transform @transition;\n -o-transition: -o-transform @transition;\n transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n -webkit-user-select: @select;\n -moz-user-select: @select;\n -ms-user-select: @select; // IE10+\n user-select: @select;\n}\n","// WebKit-style focus\n\n.tab-focus() {\n // WebKit-specific. Other browsers will keep their default outline style.\n // (Initially tried to also force default via `outline: initial`,\n // but that seems to erroneously remove the outline in Firefox altogether.)\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\n","// Image Mixins\n// - Responsive image\n// - Retina image\n\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n.img-responsive(@display: block) {\n display: @display;\n max-width: 100%; // Part 1: Set a maximum relative to the parent\n height: auto; // Part 2: Scale the height according to the width, otherwise you get stretching\n}\n\n\n// Retina image\n//\n// Short retina mixin for setting background-image and -size. Note that the\n// spelling of `min--moz-device-pixel-ratio` is intentional.\n.img-retina(@file-1x; @file-2x; @width-1x; @height-1x) {\n background-image: url(\"@{file-1x}\");\n\n @media\n only screen and (-webkit-min-device-pixel-ratio: 2),\n only screen and ( min--moz-device-pixel-ratio: 2),\n only screen and ( -o-min-device-pixel-ratio: 2/1),\n only screen and ( min-device-pixel-ratio: 2),\n only screen and ( min-resolution: 192dpi),\n only screen and ( min-resolution: 2dppx) {\n background-image: url(\"@{file-2x}\");\n background-size: @width-1x @height-1x;\n }\n}\n","//\n// Typography\n// --------------------------------------------------\n\n\n// Headings\n// -------------------------\n\nh1, h2, h3, h4, h5, h6,\n.h1, .h2, .h3, .h4, .h5, .h6 {\n font-family: @headings-font-family;\n font-weight: @headings-font-weight;\n line-height: @headings-line-height;\n color: @headings-color;\n\n small,\n .small {\n font-weight: normal;\n line-height: 1;\n color: @headings-small-color;\n }\n}\n\nh1, .h1,\nh2, .h2,\nh3, .h3 {\n margin-top: @line-height-computed;\n margin-bottom: (@line-height-computed / 2);\n\n small,\n .small {\n font-size: 65%;\n }\n}\nh4, .h4,\nh5, .h5,\nh6, .h6 {\n margin-top: (@line-height-computed / 2);\n margin-bottom: (@line-height-computed / 2);\n\n small,\n .small {\n font-size: 75%;\n }\n}\n\nh1, .h1 { font-size: @font-size-h1; }\nh2, .h2 { font-size: @font-size-h2; }\nh3, .h3 { font-size: @font-size-h3; }\nh4, .h4 { font-size: @font-size-h4; }\nh5, .h5 { font-size: @font-size-h5; }\nh6, .h6 { font-size: @font-size-h6; }\n\n\n// Body text\n// -------------------------\n\np {\n margin: 0 0 (@line-height-computed / 2);\n}\n\n.lead {\n margin-bottom: @line-height-computed;\n font-size: floor((@font-size-base * 1.15));\n font-weight: 300;\n line-height: 1.4;\n\n @media (min-width: @screen-sm-min) {\n font-size: (@font-size-base * 1.5);\n }\n}\n\n\n// Emphasis & misc\n// -------------------------\n\n// Ex: (12px small font / 14px base font) * 100% = about 85%\nsmall,\n.small {\n font-size: floor((100% * @font-size-small / @font-size-base));\n}\n\nmark,\n.mark {\n background-color: @state-warning-bg;\n padding: .2em;\n}\n\n// Alignment\n.text-left { text-align: left; }\n.text-right { text-align: right; }\n.text-center { text-align: center; }\n.text-justify { text-align: justify; }\n.text-nowrap { white-space: nowrap; }\n\n// Transformation\n.text-lowercase { text-transform: lowercase; }\n.text-uppercase { text-transform: uppercase; }\n.text-capitalize { text-transform: capitalize; }\n\n// Contextual colors\n.text-muted {\n color: @text-muted;\n}\n.text-primary {\n .text-emphasis-variant(@brand-primary);\n}\n.text-success {\n .text-emphasis-variant(@state-success-text);\n}\n.text-info {\n .text-emphasis-variant(@state-info-text);\n}\n.text-warning {\n .text-emphasis-variant(@state-warning-text);\n}\n.text-danger {\n .text-emphasis-variant(@state-danger-text);\n}\n\n// Contextual backgrounds\n// For now we'll leave these alongside the text classes until v4 when we can\n// safely shift things around (per SemVer rules).\n.bg-primary {\n // Given the contrast here, this is the only class to have its color inverted\n // automatically.\n color: #fff;\n .bg-variant(@brand-primary);\n}\n.bg-success {\n .bg-variant(@state-success-bg);\n}\n.bg-info {\n .bg-variant(@state-info-bg);\n}\n.bg-warning {\n .bg-variant(@state-warning-bg);\n}\n.bg-danger {\n .bg-variant(@state-danger-bg);\n}\n\n\n// Page header\n// -------------------------\n\n.page-header {\n padding-bottom: ((@line-height-computed / 2) - 1);\n margin: (@line-height-computed * 2) 0 @line-height-computed;\n border-bottom: 1px solid @page-header-border-color;\n}\n\n\n// Lists\n// -------------------------\n\n// Unordered and Ordered lists\nul,\nol {\n margin-top: 0;\n margin-bottom: (@line-height-computed / 2);\n ul,\n ol {\n margin-bottom: 0;\n }\n}\n\n// List options\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n.list-unstyled {\n padding-left: 0;\n list-style: none;\n}\n\n// Inline turns list items into inline-block\n.list-inline {\n .list-unstyled();\n margin-left: -5px;\n\n > li {\n display: inline-block;\n padding-left: 5px;\n padding-right: 5px;\n }\n}\n\n// Description Lists\ndl {\n margin-top: 0; // Remove browser default\n margin-bottom: @line-height-computed;\n}\ndt,\ndd {\n line-height: @line-height-base;\n}\ndt {\n font-weight: bold;\n}\ndd {\n margin-left: 0; // Undo browser default\n}\n\n// Horizontal description lists\n//\n// Defaults to being stacked without any of the below styles applied, until the\n// grid breakpoint is reached (default of ~768px).\n\n.dl-horizontal {\n dd {\n &:extend(.clearfix all); // Clear the floated `dt` if an empty `dd` is present\n }\n\n @media (min-width: @dl-horizontal-breakpoint) {\n dt {\n float: left;\n width: (@dl-horizontal-offset - 20);\n clear: left;\n text-align: right;\n .text-overflow();\n }\n dd {\n margin-left: @dl-horizontal-offset;\n }\n }\n}\n\n\n// Misc\n// -------------------------\n\n// Abbreviations and acronyms\nabbr[title],\n// Add data-* attribute to help out our tooltip plugin, per https://github.com/twbs/bootstrap/issues/5257\nabbr[data-original-title] {\n cursor: help;\n border-bottom: 1px dotted @abbr-border-color;\n}\n.initialism {\n font-size: 90%;\n .text-uppercase();\n}\n\n// Blockquotes\nblockquote {\n padding: (@line-height-computed / 2) @line-height-computed;\n margin: 0 0 @line-height-computed;\n font-size: @blockquote-font-size;\n border-left: 5px solid @blockquote-border-color;\n\n p,\n ul,\n ol {\n &:last-child {\n margin-bottom: 0;\n }\n }\n\n // Note: Deprecated small and .small as of v3.1.0\n // Context: https://github.com/twbs/bootstrap/issues/11660\n footer,\n small,\n .small {\n display: block;\n font-size: 80%; // back to default font-size\n line-height: @line-height-base;\n color: @blockquote-small-color;\n\n &:before {\n content: '\\2014 \\00A0'; // em dash, nbsp\n }\n }\n}\n\n// Opposite alignment of blockquote\n//\n// Heads up: `blockquote.pull-right` has been deprecated as of v3.1.0.\n.blockquote-reverse,\nblockquote.pull-right {\n padding-right: 15px;\n padding-left: 0;\n border-right: 5px solid @blockquote-border-color;\n border-left: 0;\n text-align: right;\n\n // Account for citation\n footer,\n small,\n .small {\n &:before { content: ''; }\n &:after {\n content: '\\00A0 \\2014'; // nbsp, em dash\n }\n }\n}\n\n// Addresses\naddress {\n margin-bottom: @line-height-computed;\n font-style: normal;\n line-height: @line-height-base;\n}\n","// Typography\n\n.text-emphasis-variant(@color) {\n color: @color;\n a&:hover,\n a&:focus {\n color: darken(@color, 10%);\n }\n}\n","// Contextual backgrounds\n\n.bg-variant(@color) {\n background-color: @color;\n a&:hover,\n a&:focus {\n background-color: darken(@color, 10%);\n }\n}\n","// Text overflow\n// Requires inline-block or block for proper styling\n\n.text-overflow() {\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n}\n","//\n// Code (inline and block)\n// --------------------------------------------------\n\n\n// Inline and block code styles\ncode,\nkbd,\npre,\nsamp {\n font-family: @font-family-monospace;\n}\n\n// Inline code\ncode {\n padding: 2px 4px;\n font-size: 90%;\n color: @code-color;\n background-color: @code-bg;\n border-radius: @border-radius-base;\n}\n\n// User input typically entered via keyboard\nkbd {\n padding: 2px 4px;\n font-size: 90%;\n color: @kbd-color;\n background-color: @kbd-bg;\n border-radius: @border-radius-small;\n box-shadow: inset 0 -1px 0 rgba(0,0,0,.25);\n\n kbd {\n padding: 0;\n font-size: 100%;\n font-weight: bold;\n box-shadow: none;\n }\n}\n\n// Blocks of code\npre {\n display: block;\n padding: ((@line-height-computed - 1) / 2);\n margin: 0 0 (@line-height-computed / 2);\n font-size: (@font-size-base - 1); // 14px to 13px\n line-height: @line-height-base;\n word-break: break-all;\n word-wrap: break-word;\n color: @pre-color;\n background-color: @pre-bg;\n border: 1px solid @pre-border-color;\n border-radius: @border-radius-base;\n\n // Account for some code outputs that place code tags in pre tags\n code {\n padding: 0;\n font-size: inherit;\n color: inherit;\n white-space: pre-wrap;\n background-color: transparent;\n border-radius: 0;\n }\n}\n\n// Enable scrollable blocks of code\n.pre-scrollable {\n max-height: @pre-scrollable-max-height;\n overflow-y: scroll;\n}\n","//\n// Grid system\n// --------------------------------------------------\n\n\n// Container widths\n//\n// Set the container width, and override it for fixed navbars in media queries.\n\n.container {\n .container-fixed();\n\n @media (min-width: @screen-sm-min) {\n width: @container-sm;\n }\n @media (min-width: @screen-md-min) {\n width: @container-md;\n }\n @media (min-width: @screen-lg-min) {\n width: @container-lg;\n }\n}\n\n\n// Fluid container\n//\n// Utilizes the mixin meant for fixed width containers, but without any defined\n// width for fluid, full width layouts.\n\n.container-fluid {\n .container-fixed();\n}\n\n\n// Row\n//\n// Rows contain and clear the floats of your columns.\n\n.row {\n .make-row();\n}\n\n\n// Columns\n//\n// Common styles for small and large grid columns\n\n.make-grid-columns();\n\n\n// Extra small grid\n//\n// Columns, offsets, pushes, and pulls for extra small devices like\n// smartphones.\n\n.make-grid(xs);\n\n\n// Small grid\n//\n// Columns, offsets, pushes, and pulls for the small device range, from phones\n// to tablets.\n\n@media (min-width: @screen-sm-min) {\n .make-grid(sm);\n}\n\n\n// Medium grid\n//\n// Columns, offsets, pushes, and pulls for the desktop device range.\n\n@media (min-width: @screen-md-min) {\n .make-grid(md);\n}\n\n\n// Large grid\n//\n// Columns, offsets, pushes, and pulls for the large desktop device range.\n\n@media (min-width: @screen-lg-min) {\n .make-grid(lg);\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n// Centered container element\n.container-fixed(@gutter: @grid-gutter-width) {\n margin-right: auto;\n margin-left: auto;\n padding-left: floor((@gutter / 2));\n padding-right: ceil((@gutter / 2));\n &:extend(.clearfix all);\n}\n\n// Creates a wrapper for a series of columns\n.make-row(@gutter: @grid-gutter-width) {\n margin-left: ceil((@gutter / -2));\n margin-right: floor((@gutter / -2));\n &:extend(.clearfix all);\n}\n\n// Generate the extra small columns\n.make-xs-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n float: left;\n width: percentage((@columns / @grid-columns));\n min-height: 1px;\n padding-left: (@gutter / 2);\n padding-right: (@gutter / 2);\n}\n.make-xs-column-offset(@columns) {\n margin-left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-push(@columns) {\n left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-pull(@columns) {\n right: percentage((@columns / @grid-columns));\n}\n\n// Generate the small columns\n.make-sm-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n min-height: 1px;\n padding-left: (@gutter / 2);\n padding-right: (@gutter / 2);\n\n @media (min-width: @screen-sm-min) {\n float: left;\n width: percentage((@columns / @grid-columns));\n }\n}\n.make-sm-column-offset(@columns) {\n @media (min-width: @screen-sm-min) {\n margin-left: percentage((@columns / @grid-columns));\n }\n}\n.make-sm-column-push(@columns) {\n @media (min-width: @screen-sm-min) {\n left: percentage((@columns / @grid-columns));\n }\n}\n.make-sm-column-pull(@columns) {\n @media (min-width: @screen-sm-min) {\n right: percentage((@columns / @grid-columns));\n }\n}\n\n// Generate the medium columns\n.make-md-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n min-height: 1px;\n padding-left: (@gutter / 2);\n padding-right: (@gutter / 2);\n\n @media (min-width: @screen-md-min) {\n float: left;\n width: percentage((@columns / @grid-columns));\n }\n}\n.make-md-column-offset(@columns) {\n @media (min-width: @screen-md-min) {\n margin-left: percentage((@columns / @grid-columns));\n }\n}\n.make-md-column-push(@columns) {\n @media (min-width: @screen-md-min) {\n left: percentage((@columns / @grid-columns));\n }\n}\n.make-md-column-pull(@columns) {\n @media (min-width: @screen-md-min) {\n right: percentage((@columns / @grid-columns));\n }\n}\n\n// Generate the large columns\n.make-lg-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n min-height: 1px;\n padding-left: (@gutter / 2);\n padding-right: (@gutter / 2);\n\n @media (min-width: @screen-lg-min) {\n float: left;\n width: percentage((@columns / @grid-columns));\n }\n}\n.make-lg-column-offset(@columns) {\n @media (min-width: @screen-lg-min) {\n margin-left: percentage((@columns / @grid-columns));\n }\n}\n.make-lg-column-push(@columns) {\n @media (min-width: @screen-lg-min) {\n left: percentage((@columns / @grid-columns));\n }\n}\n.make-lg-column-pull(@columns) {\n @media (min-width: @screen-lg-min) {\n right: percentage((@columns / @grid-columns));\n }\n}\n","// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `@grid-columns`.\n\n.make-grid-columns() {\n // Common styles for all sizes of grid columns, widths 1-12\n .col(@index) { // initial\n @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n .col((@index + 1), @item);\n }\n .col(@index, @list) when (@index =< @grid-columns) { // general; \"=<\" isn't a typo\n @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n .col((@index + 1), ~\"@{list}, @{item}\");\n }\n .col(@index, @list) when (@index > @grid-columns) { // terminal\n @{list} {\n position: relative;\n // Prevent columns from collapsing when empty\n min-height: 1px;\n // Inner gutter via padding\n padding-left: ceil((@grid-gutter-width / 2));\n padding-right: floor((@grid-gutter-width / 2));\n }\n }\n .col(1); // kickstart it\n}\n\n.float-grid-columns(@class) {\n .col(@index) { // initial\n @item: ~\".col-@{class}-@{index}\";\n .col((@index + 1), @item);\n }\n .col(@index, @list) when (@index =< @grid-columns) { // general\n @item: ~\".col-@{class}-@{index}\";\n .col((@index + 1), ~\"@{list}, @{item}\");\n }\n .col(@index, @list) when (@index > @grid-columns) { // terminal\n @{list} {\n float: left;\n }\n }\n .col(1); // kickstart it\n}\n\n.calc-grid-column(@index, @class, @type) when (@type = width) and (@index > 0) {\n .col-@{class}-@{index} {\n width: percentage((@index / @grid-columns));\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index > 0) {\n .col-@{class}-push-@{index} {\n left: percentage((@index / @grid-columns));\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index = 0) {\n .col-@{class}-push-0 {\n left: auto;\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index > 0) {\n .col-@{class}-pull-@{index} {\n right: percentage((@index / @grid-columns));\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index = 0) {\n .col-@{class}-pull-0 {\n right: auto;\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = offset) {\n .col-@{class}-offset-@{index} {\n margin-left: percentage((@index / @grid-columns));\n }\n}\n\n// Basic looping in LESS\n.loop-grid-columns(@index, @class, @type) when (@index >= 0) {\n .calc-grid-column(@index, @class, @type);\n // next iteration\n .loop-grid-columns((@index - 1), @class, @type);\n}\n\n// Create grid for specific class\n.make-grid(@class) {\n .float-grid-columns(@class);\n .loop-grid-columns(@grid-columns, @class, width);\n .loop-grid-columns(@grid-columns, @class, pull);\n .loop-grid-columns(@grid-columns, @class, push);\n .loop-grid-columns(@grid-columns, @class, offset);\n}\n","//\n// Tables\n// --------------------------------------------------\n\n\ntable {\n background-color: @table-bg;\n}\ncaption {\n padding-top: @table-cell-padding;\n padding-bottom: @table-cell-padding;\n color: @text-muted;\n text-align: left;\n}\nth {\n text-align: left;\n}\n\n\n// Baseline styles\n\n.table {\n width: 100%;\n max-width: 100%;\n margin-bottom: @line-height-computed;\n // Cells\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n padding: @table-cell-padding;\n line-height: @line-height-base;\n vertical-align: top;\n border-top: 1px solid @table-border-color;\n }\n }\n }\n // Bottom align for column headings\n > thead > tr > th {\n vertical-align: bottom;\n border-bottom: 2px solid @table-border-color;\n }\n // Remove top border from thead by default\n > caption + thead,\n > colgroup + thead,\n > thead:first-child {\n > tr:first-child {\n > th,\n > td {\n border-top: 0;\n }\n }\n }\n // Account for multiple tbody instances\n > tbody + tbody {\n border-top: 2px solid @table-border-color;\n }\n\n // Nesting\n .table {\n background-color: @body-bg;\n }\n}\n\n\n// Condensed table w/ half padding\n\n.table-condensed {\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n padding: @table-condensed-cell-padding;\n }\n }\n }\n}\n\n\n// Bordered version\n//\n// Add borders all around the table and between all the columns.\n\n.table-bordered {\n border: 1px solid @table-border-color;\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n border: 1px solid @table-border-color;\n }\n }\n }\n > thead > tr {\n > th,\n > td {\n border-bottom-width: 2px;\n }\n }\n}\n\n\n// Zebra-striping\n//\n// Default zebra-stripe styles (alternating gray and transparent backgrounds)\n\n.table-striped {\n > tbody > tr:nth-of-type(odd) {\n background-color: @table-bg-accent;\n }\n}\n\n\n// Hover effect\n//\n// Placed here since it has to come after the potential zebra striping\n\n.table-hover {\n > tbody > tr:hover {\n background-color: @table-bg-hover;\n }\n}\n\n\n// Table cell sizing\n//\n// Reset default table behavior\n\ntable col[class*=\"col-\"] {\n position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n float: none;\n display: table-column;\n}\ntable {\n td,\n th {\n &[class*=\"col-\"] {\n position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n float: none;\n display: table-cell;\n }\n }\n}\n\n\n// Table backgrounds\n//\n// Exact selectors below required to override `.table-striped` and prevent\n// inheritance to nested tables.\n\n// Generate the contextual variants\n.table-row-variant(active; @table-bg-active);\n.table-row-variant(success; @state-success-bg);\n.table-row-variant(info; @state-info-bg);\n.table-row-variant(warning; @state-warning-bg);\n.table-row-variant(danger; @state-danger-bg);\n\n\n// Responsive tables\n//\n// Wrap your tables in `.table-responsive` and we'll make them mobile friendly\n// by enabling horizontal scrolling. Only applies <768px. Everything above that\n// will display normally.\n\n.table-responsive {\n overflow-x: auto;\n min-height: 0.01%; // Workaround for IE9 bug (see https://github.com/twbs/bootstrap/issues/14837)\n\n @media screen and (max-width: @screen-xs-max) {\n width: 100%;\n margin-bottom: (@line-height-computed * 0.75);\n overflow-y: hidden;\n -ms-overflow-style: -ms-autohiding-scrollbar;\n border: 1px solid @table-border-color;\n\n // Tighten up spacing\n > .table {\n margin-bottom: 0;\n\n // Ensure the content doesn't wrap\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n white-space: nowrap;\n }\n }\n }\n }\n\n // Special overrides for the bordered tables\n > .table-bordered {\n border: 0;\n\n // Nuke the appropriate borders so that the parent can handle them\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th:first-child,\n > td:first-child {\n border-left: 0;\n }\n > th:last-child,\n > td:last-child {\n border-right: 0;\n }\n }\n }\n\n // Only nuke the last row's bottom-border in `tbody` and `tfoot` since\n // chances are there will be only one `tr` in a `thead` and that would\n // remove the border altogether.\n > tbody,\n > tfoot {\n > tr:last-child {\n > th,\n > td {\n border-bottom: 0;\n }\n }\n }\n\n }\n }\n}\n","// Tables\n\n.table-row-variant(@state; @background) {\n // Exact selectors below required to override `.table-striped` and prevent\n // inheritance to nested tables.\n .table > thead > tr,\n .table > tbody > tr,\n .table > tfoot > tr {\n > td.@{state},\n > th.@{state},\n &.@{state} > td,\n &.@{state} > th {\n background-color: @background;\n }\n }\n\n // Hover states for `.table-hover`\n // Note: this is not available for cells or rows within `thead` or `tfoot`.\n .table-hover > tbody > tr {\n > td.@{state}:hover,\n > th.@{state}:hover,\n &.@{state}:hover > td,\n &:hover > .@{state},\n &.@{state}:hover > th {\n background-color: darken(@background, 5%);\n }\n }\n}\n","//\n// Forms\n// --------------------------------------------------\n\n\n// Normalize non-controls\n//\n// Restyle and baseline non-control form elements.\n\nfieldset {\n padding: 0;\n margin: 0;\n border: 0;\n // Chrome and Firefox set a `min-width: min-content;` on fieldsets,\n // so we reset that to ensure it behaves more like a standard block element.\n // See https://github.com/twbs/bootstrap/issues/12359.\n min-width: 0;\n}\n\nlegend {\n display: block;\n width: 100%;\n padding: 0;\n margin-bottom: @line-height-computed;\n font-size: (@font-size-base * 1.5);\n line-height: inherit;\n color: @legend-color;\n border: 0;\n border-bottom: 1px solid @legend-border-color;\n}\n\nlabel {\n display: inline-block;\n max-width: 100%; // Force IE8 to wrap long content (see https://github.com/twbs/bootstrap/issues/13141)\n margin-bottom: 5px;\n font-weight: bold;\n}\n\n\n// Normalize form controls\n//\n// While most of our form styles require extra classes, some basic normalization\n// is required to ensure optimum display with or without those classes to better\n// address browser inconsistencies.\n\n// Override content-box in Normalize (* isn't specific enough)\ninput[type=\"search\"] {\n .box-sizing(border-box);\n}\n\n// Position radios and checkboxes better\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n margin: 4px 0 0;\n margin-top: 1px \\9; // IE8-9\n line-height: normal;\n}\n\ninput[type=\"file\"] {\n display: block;\n}\n\n// Make range inputs behave like textual form controls\ninput[type=\"range\"] {\n display: block;\n width: 100%;\n}\n\n// Make multiple select elements height not fixed\nselect[multiple],\nselect[size] {\n height: auto;\n}\n\n// Focus for file, radio, and checkbox\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n .tab-focus();\n}\n\n// Adjust output element\noutput {\n display: block;\n padding-top: (@padding-base-vertical + 1);\n font-size: @font-size-base;\n line-height: @line-height-base;\n color: @input-color;\n}\n\n\n// Common form controls\n//\n// Shared size and type resets for form controls. Apply `.form-control` to any\n// of the following form controls:\n//\n// select\n// textarea\n// input[type=\"text\"]\n// input[type=\"password\"]\n// input[type=\"datetime\"]\n// input[type=\"datetime-local\"]\n// input[type=\"date\"]\n// input[type=\"month\"]\n// input[type=\"time\"]\n// input[type=\"week\"]\n// input[type=\"number\"]\n// input[type=\"email\"]\n// input[type=\"url\"]\n// input[type=\"search\"]\n// input[type=\"tel\"]\n// input[type=\"color\"]\n\n.form-control {\n display: block;\n width: 100%;\n height: @input-height-base; // Make inputs at least the height of their button counterpart (base line-height + padding + border)\n padding: @padding-base-vertical @padding-base-horizontal;\n font-size: @font-size-base;\n line-height: @line-height-base;\n color: @input-color;\n background-color: @input-bg;\n background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n border: 1px solid @input-border;\n border-radius: @input-border-radius; // Note: This has no effect on s in CSS.\n .box-shadow(inset 0 1px 1px rgba(0,0,0,.075));\n .transition(~\"border-color ease-in-out .15s, box-shadow ease-in-out .15s\");\n\n // Customize the `:focus` state to imitate native WebKit styles.\n .form-control-focus();\n\n // Placeholder\n .placeholder();\n\n // Unstyle the caret on ``\n// element gets special love because it's special, and that's a fact!\n.input-size(@input-height; @padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n height: @input-height;\n padding: @padding-vertical @padding-horizontal;\n font-size: @font-size;\n line-height: @line-height;\n border-radius: @border-radius;\n\n select& {\n height: @input-height;\n line-height: @input-height;\n }\n\n textarea&,\n select[multiple]& {\n height: auto;\n }\n}\n","//\n// Buttons\n// --------------------------------------------------\n\n\n// Base styles\n// --------------------------------------------------\n\n.btn {\n display: inline-block;\n margin-bottom: 0; // For input.btn\n font-weight: @btn-font-weight;\n text-align: center;\n vertical-align: middle;\n touch-action: manipulation;\n cursor: pointer;\n background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n border: 1px solid transparent;\n white-space: nowrap;\n .button-size(@padding-base-vertical; @padding-base-horizontal; @font-size-base; @line-height-base; @btn-border-radius-base);\n .user-select(none);\n\n &,\n &:active,\n &.active {\n &:focus,\n &.focus {\n .tab-focus();\n }\n }\n\n &:hover,\n &:focus,\n &.focus {\n color: @btn-default-color;\n text-decoration: none;\n }\n\n &:active,\n &.active {\n outline: 0;\n background-image: none;\n .box-shadow(inset 0 3px 5px rgba(0,0,0,.125));\n }\n\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n cursor: @cursor-disabled;\n .opacity(.65);\n .box-shadow(none);\n }\n\n a& {\n &.disabled,\n fieldset[disabled] & {\n pointer-events: none; // Future-proof disabling of clicks on `` elements\n }\n }\n}\n\n\n// Alternate buttons\n// --------------------------------------------------\n\n.btn-default {\n .button-variant(@btn-default-color; @btn-default-bg; @btn-default-border);\n}\n.btn-primary {\n .button-variant(@btn-primary-color; @btn-primary-bg; @btn-primary-border);\n}\n// Success appears as green\n.btn-success {\n .button-variant(@btn-success-color; @btn-success-bg; @btn-success-border);\n}\n// Info appears as blue-green\n.btn-info {\n .button-variant(@btn-info-color; @btn-info-bg; @btn-info-border);\n}\n// Warning appears as orange\n.btn-warning {\n .button-variant(@btn-warning-color; @btn-warning-bg; @btn-warning-border);\n}\n// Danger and error appear as red\n.btn-danger {\n .button-variant(@btn-danger-color; @btn-danger-bg; @btn-danger-border);\n}\n\n\n// Link buttons\n// -------------------------\n\n// Make a button look and behave like a link\n.btn-link {\n color: @link-color;\n font-weight: normal;\n border-radius: 0;\n\n &,\n &:active,\n &.active,\n &[disabled],\n fieldset[disabled] & {\n background-color: transparent;\n .box-shadow(none);\n }\n &,\n &:hover,\n &:focus,\n &:active {\n border-color: transparent;\n }\n &:hover,\n &:focus {\n color: @link-hover-color;\n text-decoration: @link-hover-decoration;\n background-color: transparent;\n }\n &[disabled],\n fieldset[disabled] & {\n &:hover,\n &:focus {\n color: @btn-link-disabled-color;\n text-decoration: none;\n }\n }\n}\n\n\n// Button Sizes\n// --------------------------------------------------\n\n.btn-lg {\n // line-height: ensure even-numbered height of button next to large input\n .button-size(@padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @btn-border-radius-large);\n}\n.btn-sm {\n // line-height: ensure proper height of button next to small input\n .button-size(@padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @btn-border-radius-small);\n}\n.btn-xs {\n .button-size(@padding-xs-vertical; @padding-xs-horizontal; @font-size-small; @line-height-small; @btn-border-radius-small);\n}\n\n\n// Block button\n// --------------------------------------------------\n\n.btn-block {\n display: block;\n width: 100%;\n}\n\n// Vertically space out multiple block buttons\n.btn-block + .btn-block {\n margin-top: 5px;\n}\n\n// Specificity overrides\ninput[type=\"submit\"],\ninput[type=\"reset\"],\ninput[type=\"button\"] {\n &.btn-block {\n width: 100%;\n }\n}\n","// Button variants\n//\n// Easily pump out default styles, as well as :hover, :focus, :active,\n// and disabled options for all buttons\n\n.button-variant(@color; @background; @border) {\n color: @color;\n background-color: @background;\n border-color: @border;\n\n &:focus,\n &.focus {\n color: @color;\n background-color: darken(@background, 10%);\n border-color: darken(@border, 25%);\n }\n &:hover {\n color: @color;\n background-color: darken(@background, 10%);\n border-color: darken(@border, 12%);\n }\n &:active,\n &.active,\n .open > .dropdown-toggle& {\n color: @color;\n background-color: darken(@background, 10%);\n border-color: darken(@border, 12%);\n\n &:hover,\n &:focus,\n &.focus {\n color: @color;\n background-color: darken(@background, 17%);\n border-color: darken(@border, 25%);\n }\n }\n &:active,\n &.active,\n .open > .dropdown-toggle& {\n background-image: none;\n }\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n &:hover,\n &:focus,\n &.focus {\n background-color: @background;\n border-color: @border;\n }\n }\n\n .badge {\n color: @background;\n background-color: @color;\n }\n}\n\n// Button sizes\n.button-size(@padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n padding: @padding-vertical @padding-horizontal;\n font-size: @font-size;\n line-height: @line-height;\n border-radius: @border-radius;\n}\n","// Opacity\n\n.opacity(@opacity) {\n opacity: @opacity;\n // IE8 filter\n @opacity-ie: (@opacity * 100);\n filter: ~\"alpha(opacity=@{opacity-ie})\";\n}\n","//\n// Component animations\n// --------------------------------------------------\n\n// Heads up!\n//\n// We don't use the `.opacity()` mixin here since it causes a bug with text\n// fields in IE7-8. Source: https://github.com/twbs/bootstrap/pull/3552.\n\n.fade {\n opacity: 0;\n .transition(opacity .15s linear);\n &.in {\n opacity: 1;\n }\n}\n\n.collapse {\n display: none;\n\n &.in { display: block; }\n tr&.in { display: table-row; }\n tbody&.in { display: table-row-group; }\n}\n\n.collapsing {\n position: relative;\n height: 0;\n overflow: hidden;\n .transition-property(~\"height, visibility\");\n .transition-duration(.35s);\n .transition-timing-function(ease);\n}\n","//\n// Dropdown menus\n// --------------------------------------------------\n\n\n// Dropdown arrow/caret\n.caret {\n display: inline-block;\n width: 0;\n height: 0;\n margin-left: 2px;\n vertical-align: middle;\n border-top: @caret-width-base dashed;\n border-top: @caret-width-base solid ~\"\\9\"; // IE8\n border-right: @caret-width-base solid transparent;\n border-left: @caret-width-base solid transparent;\n}\n\n// The dropdown wrapper (div)\n.dropup,\n.dropdown {\n position: relative;\n}\n\n// Prevent the focus on the dropdown toggle when closing dropdowns\n.dropdown-toggle:focus {\n outline: 0;\n}\n\n// The dropdown menu (ul)\n.dropdown-menu {\n position: absolute;\n top: 100%;\n left: 0;\n z-index: @zindex-dropdown;\n display: none; // none by default, but block on \"open\" of the menu\n float: left;\n min-width: 160px;\n padding: 5px 0;\n margin: 2px 0 0; // override default ul\n list-style: none;\n font-size: @font-size-base;\n text-align: left; // Ensures proper alignment if parent has it changed (e.g., modal footer)\n background-color: @dropdown-bg;\n border: 1px solid @dropdown-fallback-border; // IE8 fallback\n border: 1px solid @dropdown-border;\n border-radius: @border-radius-base;\n .box-shadow(0 6px 12px rgba(0,0,0,.175));\n background-clip: padding-box;\n\n // Aligns the dropdown menu to right\n //\n // Deprecated as of 3.1.0 in favor of `.dropdown-menu-[dir]`\n &.pull-right {\n right: 0;\n left: auto;\n }\n\n // Dividers (basically an hr) within the dropdown\n .divider {\n .nav-divider(@dropdown-divider-bg);\n }\n\n // Links within the dropdown menu\n > li > a {\n display: block;\n padding: 3px 20px;\n clear: both;\n font-weight: normal;\n line-height: @line-height-base;\n color: @dropdown-link-color;\n white-space: nowrap; // prevent links from randomly breaking onto new lines\n }\n}\n\n// Hover/Focus state\n.dropdown-menu > li > a {\n &:hover,\n &:focus {\n text-decoration: none;\n color: @dropdown-link-hover-color;\n background-color: @dropdown-link-hover-bg;\n }\n}\n\n// Active state\n.dropdown-menu > .active > a {\n &,\n &:hover,\n &:focus {\n color: @dropdown-link-active-color;\n text-decoration: none;\n outline: 0;\n background-color: @dropdown-link-active-bg;\n }\n}\n\n// Disabled state\n//\n// Gray out text and ensure the hover/focus state remains gray\n\n.dropdown-menu > .disabled > a {\n &,\n &:hover,\n &:focus {\n color: @dropdown-link-disabled-color;\n }\n\n // Nuke hover/focus effects\n &:hover,\n &:focus {\n text-decoration: none;\n background-color: transparent;\n background-image: none; // Remove CSS gradient\n .reset-filter();\n cursor: @cursor-disabled;\n }\n}\n\n// Open state for the dropdown\n.open {\n // Show the menu\n > .dropdown-menu {\n display: block;\n }\n\n // Remove the outline when :focus is triggered\n > a {\n outline: 0;\n }\n}\n\n// Menu positioning\n//\n// Add extra class to `.dropdown-menu` to flip the alignment of the dropdown\n// menu with the parent.\n.dropdown-menu-right {\n left: auto; // Reset the default from `.dropdown-menu`\n right: 0;\n}\n// With v3, we enabled auto-flipping if you have a dropdown within a right\n// aligned nav component. To enable the undoing of that, we provide an override\n// to restore the default dropdown menu alignment.\n//\n// This is only for left-aligning a dropdown menu within a `.navbar-right` or\n// `.pull-right` nav component.\n.dropdown-menu-left {\n left: 0;\n right: auto;\n}\n\n// Dropdown section headers\n.dropdown-header {\n display: block;\n padding: 3px 20px;\n font-size: @font-size-small;\n line-height: @line-height-base;\n color: @dropdown-header-color;\n white-space: nowrap; // as with > li > a\n}\n\n// Backdrop to catch body clicks on mobile, etc.\n.dropdown-backdrop {\n position: fixed;\n left: 0;\n right: 0;\n bottom: 0;\n top: 0;\n z-index: (@zindex-dropdown - 10);\n}\n\n// Right aligned dropdowns\n.pull-right > .dropdown-menu {\n right: 0;\n left: auto;\n}\n\n// Allow for dropdowns to go bottom up (aka, dropup-menu)\n//\n// Just add .dropup after the standard .dropdown class and you're set, bro.\n// TODO: abstract this so that the navbar fixed styles are not placed here?\n\n.dropup,\n.navbar-fixed-bottom .dropdown {\n // Reverse the caret\n .caret {\n border-top: 0;\n border-bottom: @caret-width-base dashed;\n border-bottom: @caret-width-base solid ~\"\\9\"; // IE8\n content: \"\";\n }\n // Different positioning for bottom up menu\n .dropdown-menu {\n top: auto;\n bottom: 100%;\n margin-bottom: 2px;\n }\n}\n\n\n// Component alignment\n//\n// Reiterate per navbar.less and the modified component alignment there.\n\n@media (min-width: @grid-float-breakpoint) {\n .navbar-right {\n .dropdown-menu {\n .dropdown-menu-right();\n }\n // Necessary for overrides of the default right aligned menu.\n // Will remove come v4 in all likelihood.\n .dropdown-menu-left {\n .dropdown-menu-left();\n }\n }\n}\n","// Horizontal dividers\n//\n// Dividers (basically an hr) within dropdowns and nav lists\n\n.nav-divider(@color: #e5e5e5) {\n height: 1px;\n margin: ((@line-height-computed / 2) - 1) 0;\n overflow: hidden;\n background-color: @color;\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n","//\n// Button groups\n// --------------------------------------------------\n\n// Make the div behave like a button\n.btn-group,\n.btn-group-vertical {\n position: relative;\n display: inline-block;\n vertical-align: middle; // match .btn alignment given font-size hack above\n > .btn {\n position: relative;\n float: left;\n // Bring the \"active\" button to the front\n &:hover,\n &:focus,\n &:active,\n &.active {\n z-index: 2;\n }\n }\n}\n\n// Prevent double borders when buttons are next to each other\n.btn-group {\n .btn + .btn,\n .btn + .btn-group,\n .btn-group + .btn,\n .btn-group + .btn-group {\n margin-left: -1px;\n }\n}\n\n// Optional: Group multiple button groups together for a toolbar\n.btn-toolbar {\n margin-left: -5px; // Offset the first child's margin\n &:extend(.clearfix all);\n\n .btn,\n .btn-group,\n .input-group {\n float: left;\n }\n > .btn,\n > .btn-group,\n > .input-group {\n margin-left: 5px;\n }\n}\n\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n border-radius: 0;\n}\n\n// Set corners individual because sometimes a single button can be in a .btn-group and we need :first-child and :last-child to both match\n.btn-group > .btn:first-child {\n margin-left: 0;\n &:not(:last-child):not(.dropdown-toggle) {\n .border-right-radius(0);\n }\n}\n// Need .dropdown-toggle since :last-child doesn't apply, given that a .dropdown-menu is used immediately after it\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n .border-left-radius(0);\n}\n\n// Custom edits for including btn-groups within btn-groups (useful for including dropdown buttons within a btn-group)\n.btn-group > .btn-group {\n float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) {\n > .btn:last-child,\n > .dropdown-toggle {\n .border-right-radius(0);\n }\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n .border-left-radius(0);\n}\n\n// On active and open, don't show outline\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n outline: 0;\n}\n\n\n// Sizing\n//\n// Remix the default button sizing classes into new ones for easier manipulation.\n\n.btn-group-xs > .btn { &:extend(.btn-xs); }\n.btn-group-sm > .btn { &:extend(.btn-sm); }\n.btn-group-lg > .btn { &:extend(.btn-lg); }\n\n\n// Split button dropdowns\n// ----------------------\n\n// Give the line between buttons some depth\n.btn-group > .btn + .dropdown-toggle {\n padding-left: 8px;\n padding-right: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n padding-left: 12px;\n padding-right: 12px;\n}\n\n// The clickable button for toggling the menu\n// Remove the gradient and set the same inset shadow as the :active state\n.btn-group.open .dropdown-toggle {\n .box-shadow(inset 0 3px 5px rgba(0,0,0,.125));\n\n // Show no shadow for `.btn-link` since it has no other button styles.\n &.btn-link {\n .box-shadow(none);\n }\n}\n\n\n// Reposition the caret\n.btn .caret {\n margin-left: 0;\n}\n// Carets in other button sizes\n.btn-lg .caret {\n border-width: @caret-width-large @caret-width-large 0;\n border-bottom-width: 0;\n}\n// Upside down carets for .dropup\n.dropup .btn-lg .caret {\n border-width: 0 @caret-width-large @caret-width-large;\n}\n\n\n// Vertical button groups\n// ----------------------\n\n.btn-group-vertical {\n > .btn,\n > .btn-group,\n > .btn-group > .btn {\n display: block;\n float: none;\n width: 100%;\n max-width: 100%;\n }\n\n // Clear floats so dropdown menus can be properly placed\n > .btn-group {\n &:extend(.clearfix all);\n > .btn {\n float: none;\n }\n }\n\n > .btn + .btn,\n > .btn + .btn-group,\n > .btn-group + .btn,\n > .btn-group + .btn-group {\n margin-top: -1px;\n margin-left: 0;\n }\n}\n\n.btn-group-vertical > .btn {\n &:not(:first-child):not(:last-child) {\n border-radius: 0;\n }\n &:first-child:not(:last-child) {\n .border-top-radius(@btn-border-radius-base);\n .border-bottom-radius(0);\n }\n &:last-child:not(:first-child) {\n .border-top-radius(0);\n .border-bottom-radius(@btn-border-radius-base);\n }\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) {\n > .btn:last-child,\n > .dropdown-toggle {\n .border-bottom-radius(0);\n }\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n .border-top-radius(0);\n}\n\n\n// Justified button groups\n// ----------------------\n\n.btn-group-justified {\n display: table;\n width: 100%;\n table-layout: fixed;\n border-collapse: separate;\n > .btn,\n > .btn-group {\n float: none;\n display: table-cell;\n width: 1%;\n }\n > .btn-group .btn {\n width: 100%;\n }\n\n > .btn-group .dropdown-menu {\n left: auto;\n }\n}\n\n\n// Checkbox and radio options\n//\n// In order to support the browser's form validation feedback, powered by the\n// `required` attribute, we have to \"hide\" the inputs via `clip`. We cannot use\n// `display: none;` or `visibility: hidden;` as that also hides the popover.\n// Simply visually hiding the inputs via `opacity` would leave them clickable in\n// certain cases which is prevented by using `clip` and `pointer-events`.\n// This way, we ensure a DOM element is visible to position the popover from.\n//\n// See https://github.com/twbs/bootstrap/pull/12794 and\n// https://github.com/twbs/bootstrap/pull/14559 for more information.\n\n[data-toggle=\"buttons\"] {\n > .btn,\n > .btn-group > .btn {\n input[type=\"radio\"],\n input[type=\"checkbox\"] {\n position: absolute;\n clip: rect(0,0,0,0);\n pointer-events: none;\n }\n }\n}\n","// Single side border-radius\n\n.border-top-radius(@radius) {\n border-top-right-radius: @radius;\n border-top-left-radius: @radius;\n}\n.border-right-radius(@radius) {\n border-bottom-right-radius: @radius;\n border-top-right-radius: @radius;\n}\n.border-bottom-radius(@radius) {\n border-bottom-right-radius: @radius;\n border-bottom-left-radius: @radius;\n}\n.border-left-radius(@radius) {\n border-bottom-left-radius: @radius;\n border-top-left-radius: @radius;\n}\n","//\n// Input groups\n// --------------------------------------------------\n\n// Base styles\n// -------------------------\n.input-group {\n position: relative; // For dropdowns\n display: table;\n border-collapse: separate; // prevent input groups from inheriting border styles from table cells when placed within a table\n\n // Undo padding and float of grid classes\n &[class*=\"col-\"] {\n float: none;\n padding-left: 0;\n padding-right: 0;\n }\n\n .form-control {\n // Ensure that the input is always above the *appended* addon button for\n // proper border colors.\n position: relative;\n z-index: 2;\n\n // IE9 fubars the placeholder attribute in text inputs and the arrows on\n // select elements in input groups. To fix it, we float the input. Details:\n // https://github.com/twbs/bootstrap/issues/11561#issuecomment-28936855\n float: left;\n\n width: 100%;\n margin-bottom: 0;\n\n &:focus {\n z-index: 3;\n }\n }\n}\n\n// Sizing options\n//\n// Remix the default form control sizing classes into new ones for easier\n// manipulation.\n\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n .input-lg();\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n .input-sm();\n}\n\n\n// Display as table-cell\n// -------------------------\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n display: table-cell;\n\n &:not(:first-child):not(:last-child) {\n border-radius: 0;\n }\n}\n// Addon and addon wrapper for buttons\n.input-group-addon,\n.input-group-btn {\n width: 1%;\n white-space: nowrap;\n vertical-align: middle; // Match the inputs\n}\n\n// Text input groups\n// -------------------------\n.input-group-addon {\n padding: @padding-base-vertical @padding-base-horizontal;\n font-size: @font-size-base;\n font-weight: normal;\n line-height: 1;\n color: @input-color;\n text-align: center;\n background-color: @input-group-addon-bg;\n border: 1px solid @input-group-addon-border-color;\n border-radius: @input-border-radius;\n\n // Sizing\n &.input-sm {\n padding: @padding-small-vertical @padding-small-horizontal;\n font-size: @font-size-small;\n border-radius: @input-border-radius-small;\n }\n &.input-lg {\n padding: @padding-large-vertical @padding-large-horizontal;\n font-size: @font-size-large;\n border-radius: @input-border-radius-large;\n }\n\n // Nuke default margins from checkboxes and radios to vertically center within.\n input[type=\"radio\"],\n input[type=\"checkbox\"] {\n margin-top: 0;\n }\n}\n\n// Reset rounded corners\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n .border-right-radius(0);\n}\n.input-group-addon:first-child {\n border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n .border-left-radius(0);\n}\n.input-group-addon:last-child {\n border-left: 0;\n}\n\n// Button input groups\n// -------------------------\n.input-group-btn {\n position: relative;\n // Jankily prevent input button groups from wrapping with `white-space` and\n // `font-size` in combination with `inline-block` on buttons.\n font-size: 0;\n white-space: nowrap;\n\n // Negative margin for spacing, position for bringing hovered/focused/actived\n // element above the siblings.\n > .btn {\n position: relative;\n + .btn {\n margin-left: -1px;\n }\n // Bring the \"active\" button to the front\n &:hover,\n &:focus,\n &:active {\n z-index: 2;\n }\n }\n\n // Negative margin to only have a 1px border between the two\n &:first-child {\n > .btn,\n > .btn-group {\n margin-right: -1px;\n }\n }\n &:last-child {\n > .btn,\n > .btn-group {\n z-index: 2;\n margin-left: -1px;\n }\n }\n}\n","//\n// Navs\n// --------------------------------------------------\n\n\n// Base class\n// --------------------------------------------------\n\n.nav {\n margin-bottom: 0;\n padding-left: 0; // Override default ul/ol\n list-style: none;\n &:extend(.clearfix all);\n\n > li {\n position: relative;\n display: block;\n\n > a {\n position: relative;\n display: block;\n padding: @nav-link-padding;\n &:hover,\n &:focus {\n text-decoration: none;\n background-color: @nav-link-hover-bg;\n }\n }\n\n // Disabled state sets text to gray and nukes hover/tab effects\n &.disabled > a {\n color: @nav-disabled-link-color;\n\n &:hover,\n &:focus {\n color: @nav-disabled-link-hover-color;\n text-decoration: none;\n background-color: transparent;\n cursor: @cursor-disabled;\n }\n }\n }\n\n // Open dropdowns\n .open > a {\n &,\n &:hover,\n &:focus {\n background-color: @nav-link-hover-bg;\n border-color: @link-color;\n }\n }\n\n // Nav dividers (deprecated with v3.0.1)\n //\n // This should have been removed in v3 with the dropping of `.nav-list`, but\n // we missed it. We don't currently support this anywhere, but in the interest\n // of maintaining backward compatibility in case you use it, it's deprecated.\n .nav-divider {\n .nav-divider();\n }\n\n // Prevent IE8 from misplacing imgs\n //\n // See https://github.com/h5bp/html5-boilerplate/issues/984#issuecomment-3985989\n > li > a > img {\n max-width: none;\n }\n}\n\n\n// Tabs\n// -------------------------\n\n// Give the tabs something to sit on\n.nav-tabs {\n border-bottom: 1px solid @nav-tabs-border-color;\n > li {\n float: left;\n // Make the list-items overlay the bottom border\n margin-bottom: -1px;\n\n // Actual tabs (as links)\n > a {\n margin-right: 2px;\n line-height: @line-height-base;\n border: 1px solid transparent;\n border-radius: @border-radius-base @border-radius-base 0 0;\n &:hover {\n border-color: @nav-tabs-link-hover-border-color @nav-tabs-link-hover-border-color @nav-tabs-border-color;\n }\n }\n\n // Active state, and its :hover to override normal :hover\n &.active > a {\n &,\n &:hover,\n &:focus {\n color: @nav-tabs-active-link-hover-color;\n background-color: @nav-tabs-active-link-hover-bg;\n border: 1px solid @nav-tabs-active-link-hover-border-color;\n border-bottom-color: transparent;\n cursor: default;\n }\n }\n }\n // pulling this in mainly for less shorthand\n &.nav-justified {\n .nav-justified();\n .nav-tabs-justified();\n }\n}\n\n\n// Pills\n// -------------------------\n.nav-pills {\n > li {\n float: left;\n\n // Links rendered as pills\n > a {\n border-radius: @nav-pills-border-radius;\n }\n + li {\n margin-left: 2px;\n }\n\n // Active state\n &.active > a {\n &,\n &:hover,\n &:focus {\n color: @nav-pills-active-link-hover-color;\n background-color: @nav-pills-active-link-hover-bg;\n }\n }\n }\n}\n\n\n// Stacked pills\n.nav-stacked {\n > li {\n float: none;\n + li {\n margin-top: 2px;\n margin-left: 0; // no need for this gap between nav items\n }\n }\n}\n\n\n// Nav variations\n// --------------------------------------------------\n\n// Justified nav links\n// -------------------------\n\n.nav-justified {\n width: 100%;\n\n > li {\n float: none;\n > a {\n text-align: center;\n margin-bottom: 5px;\n }\n }\n\n > .dropdown .dropdown-menu {\n top: auto;\n left: auto;\n }\n\n @media (min-width: @screen-sm-min) {\n > li {\n display: table-cell;\n width: 1%;\n > a {\n margin-bottom: 0;\n }\n }\n }\n}\n\n// Move borders to anchors instead of bottom of list\n//\n// Mixin for adding on top the shared `.nav-justified` styles for our tabs\n.nav-tabs-justified {\n border-bottom: 0;\n\n > li > a {\n // Override margin from .nav-tabs\n margin-right: 0;\n border-radius: @border-radius-base;\n }\n\n > .active > a,\n > .active > a:hover,\n > .active > a:focus {\n border: 1px solid @nav-tabs-justified-link-border-color;\n }\n\n @media (min-width: @screen-sm-min) {\n > li > a {\n border-bottom: 1px solid @nav-tabs-justified-link-border-color;\n border-radius: @border-radius-base @border-radius-base 0 0;\n }\n > .active > a,\n > .active > a:hover,\n > .active > a:focus {\n border-bottom-color: @nav-tabs-justified-active-link-border-color;\n }\n }\n}\n\n\n// Tabbable tabs\n// -------------------------\n\n// Hide tabbable panes to start, show them when `.active`\n.tab-content {\n > .tab-pane {\n display: none;\n }\n > .active {\n display: block;\n }\n}\n\n\n// Dropdowns\n// -------------------------\n\n// Specific dropdowns\n.nav-tabs .dropdown-menu {\n // make dropdown border overlap tab border\n margin-top: -1px;\n // Remove the top rounded corners here since there is a hard edge above the menu\n .border-top-radius(0);\n}\n","//\n// Navbars\n// --------------------------------------------------\n\n\n// Wrapper and base class\n//\n// Provide a static navbar from which we expand to create full-width, fixed, and\n// other navbar variations.\n\n.navbar {\n position: relative;\n min-height: @navbar-height; // Ensure a navbar always shows (e.g., without a .navbar-brand in collapsed mode)\n margin-bottom: @navbar-margin-bottom;\n border: 1px solid transparent;\n\n // Prevent floats from breaking the navbar\n &:extend(.clearfix all);\n\n @media (min-width: @grid-float-breakpoint) {\n border-radius: @navbar-border-radius;\n }\n}\n\n\n// Navbar heading\n//\n// Groups `.navbar-brand` and `.navbar-toggle` into a single component for easy\n// styling of responsive aspects.\n\n.navbar-header {\n &:extend(.clearfix all);\n\n @media (min-width: @grid-float-breakpoint) {\n float: left;\n }\n}\n\n\n// Navbar collapse (body)\n//\n// Group your navbar content into this for easy collapsing and expanding across\n// various device sizes. By default, this content is collapsed when <768px, but\n// will expand past that for a horizontal display.\n//\n// To start (on mobile devices) the navbar links, forms, and buttons are stacked\n// vertically and include a `max-height` to overflow in case you have too much\n// content for the user's viewport.\n\n.navbar-collapse {\n overflow-x: visible;\n padding-right: @navbar-padding-horizontal;\n padding-left: @navbar-padding-horizontal;\n border-top: 1px solid transparent;\n box-shadow: inset 0 1px 0 rgba(255,255,255,.1);\n &:extend(.clearfix all);\n -webkit-overflow-scrolling: touch;\n\n &.in {\n overflow-y: auto;\n }\n\n @media (min-width: @grid-float-breakpoint) {\n width: auto;\n border-top: 0;\n box-shadow: none;\n\n &.collapse {\n display: block !important;\n height: auto !important;\n padding-bottom: 0; // Override default setting\n overflow: visible !important;\n }\n\n &.in {\n overflow-y: visible;\n }\n\n // Undo the collapse side padding for navbars with containers to ensure\n // alignment of right-aligned contents.\n .navbar-fixed-top &,\n .navbar-static-top &,\n .navbar-fixed-bottom & {\n padding-left: 0;\n padding-right: 0;\n }\n }\n}\n\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n .navbar-collapse {\n max-height: @navbar-collapse-max-height;\n\n @media (max-device-width: @screen-xs-min) and (orientation: landscape) {\n max-height: 200px;\n }\n }\n}\n\n\n// Both navbar header and collapse\n//\n// When a container is present, change the behavior of the header and collapse.\n\n.container,\n.container-fluid {\n > .navbar-header,\n > .navbar-collapse {\n margin-right: -@navbar-padding-horizontal;\n margin-left: -@navbar-padding-horizontal;\n\n @media (min-width: @grid-float-breakpoint) {\n margin-right: 0;\n margin-left: 0;\n }\n }\n}\n\n\n//\n// Navbar alignment options\n//\n// Display the navbar across the entirety of the page or fixed it to the top or\n// bottom of the page.\n\n// Static top (unfixed, but 100% wide) navbar\n.navbar-static-top {\n z-index: @zindex-navbar;\n border-width: 0 0 1px;\n\n @media (min-width: @grid-float-breakpoint) {\n border-radius: 0;\n }\n}\n\n// Fix the top/bottom navbars when screen real estate supports it\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n position: fixed;\n right: 0;\n left: 0;\n z-index: @zindex-navbar-fixed;\n\n // Undo the rounded corners\n @media (min-width: @grid-float-breakpoint) {\n border-radius: 0;\n }\n}\n.navbar-fixed-top {\n top: 0;\n border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n bottom: 0;\n margin-bottom: 0; // override .navbar defaults\n border-width: 1px 0 0;\n}\n\n\n// Brand/project name\n\n.navbar-brand {\n float: left;\n padding: @navbar-padding-vertical @navbar-padding-horizontal;\n font-size: @font-size-large;\n line-height: @line-height-computed;\n height: @navbar-height;\n\n &:hover,\n &:focus {\n text-decoration: none;\n }\n\n > img {\n display: block;\n }\n\n @media (min-width: @grid-float-breakpoint) {\n .navbar > .container &,\n .navbar > .container-fluid & {\n margin-left: -@navbar-padding-horizontal;\n }\n }\n}\n\n\n// Navbar toggle\n//\n// Custom button for toggling the `.navbar-collapse`, powered by the collapse\n// JavaScript plugin.\n\n.navbar-toggle {\n position: relative;\n float: right;\n margin-right: @navbar-padding-horizontal;\n padding: 9px 10px;\n .navbar-vertical-align(34px);\n background-color: transparent;\n background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n border: 1px solid transparent;\n border-radius: @border-radius-base;\n\n // We remove the `outline` here, but later compensate by attaching `:hover`\n // styles to `:focus`.\n &:focus {\n outline: 0;\n }\n\n // Bars\n .icon-bar {\n display: block;\n width: 22px;\n height: 2px;\n border-radius: 1px;\n }\n .icon-bar + .icon-bar {\n margin-top: 4px;\n }\n\n @media (min-width: @grid-float-breakpoint) {\n display: none;\n }\n}\n\n\n// Navbar nav links\n//\n// Builds on top of the `.nav` components with its own modifier class to make\n// the nav the full height of the horizontal nav (above 768px).\n\n.navbar-nav {\n margin: (@navbar-padding-vertical / 2) -@navbar-padding-horizontal;\n\n > li > a {\n padding-top: 10px;\n padding-bottom: 10px;\n line-height: @line-height-computed;\n }\n\n @media (max-width: @grid-float-breakpoint-max) {\n // Dropdowns get custom display when collapsed\n .open .dropdown-menu {\n position: static;\n float: none;\n width: auto;\n margin-top: 0;\n background-color: transparent;\n border: 0;\n box-shadow: none;\n > li > a,\n .dropdown-header {\n padding: 5px 15px 5px 25px;\n }\n > li > a {\n line-height: @line-height-computed;\n &:hover,\n &:focus {\n background-image: none;\n }\n }\n }\n }\n\n // Uncollapse the nav\n @media (min-width: @grid-float-breakpoint) {\n float: left;\n margin: 0;\n\n > li {\n float: left;\n > a {\n padding-top: @navbar-padding-vertical;\n padding-bottom: @navbar-padding-vertical;\n }\n }\n }\n}\n\n\n// Navbar form\n//\n// Extension of the `.form-inline` with some extra flavor for optimum display in\n// our navbars.\n\n.navbar-form {\n margin-left: -@navbar-padding-horizontal;\n margin-right: -@navbar-padding-horizontal;\n padding: 10px @navbar-padding-horizontal;\n border-top: 1px solid transparent;\n border-bottom: 1px solid transparent;\n @shadow: inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);\n .box-shadow(@shadow);\n\n // Mixin behavior for optimum display\n .form-inline();\n\n .form-group {\n @media (max-width: @grid-float-breakpoint-max) {\n margin-bottom: 5px;\n\n &:last-child {\n margin-bottom: 0;\n }\n }\n }\n\n // Vertically center in expanded, horizontal navbar\n .navbar-vertical-align(@input-height-base);\n\n // Undo 100% width for pull classes\n @media (min-width: @grid-float-breakpoint) {\n width: auto;\n border: 0;\n margin-left: 0;\n margin-right: 0;\n padding-top: 0;\n padding-bottom: 0;\n .box-shadow(none);\n }\n}\n\n\n// Dropdown menus\n\n// Menu position and menu carets\n.navbar-nav > li > .dropdown-menu {\n margin-top: 0;\n .border-top-radius(0);\n}\n// Menu position and menu caret support for dropups via extra dropup class\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n margin-bottom: 0;\n .border-top-radius(@navbar-border-radius);\n .border-bottom-radius(0);\n}\n\n\n// Buttons in navbars\n//\n// Vertically center a button within a navbar (when *not* in a form).\n\n.navbar-btn {\n .navbar-vertical-align(@input-height-base);\n\n &.btn-sm {\n .navbar-vertical-align(@input-height-small);\n }\n &.btn-xs {\n .navbar-vertical-align(22);\n }\n}\n\n\n// Text in navbars\n//\n// Add a class to make any element properly align itself vertically within the navbars.\n\n.navbar-text {\n .navbar-vertical-align(@line-height-computed);\n\n @media (min-width: @grid-float-breakpoint) {\n float: left;\n margin-left: @navbar-padding-horizontal;\n margin-right: @navbar-padding-horizontal;\n }\n}\n\n\n// Component alignment\n//\n// Repurpose the pull utilities as their own navbar utilities to avoid specificity\n// issues with parents and chaining. Only do this when the navbar is uncollapsed\n// though so that navbar contents properly stack and align in mobile.\n//\n// Declared after the navbar components to ensure more specificity on the margins.\n\n@media (min-width: @grid-float-breakpoint) {\n .navbar-left { .pull-left(); }\n .navbar-right {\n .pull-right();\n margin-right: -@navbar-padding-horizontal;\n\n ~ .navbar-right {\n margin-right: 0;\n }\n }\n}\n\n\n// Alternate navbars\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n background-color: @navbar-default-bg;\n border-color: @navbar-default-border;\n\n .navbar-brand {\n color: @navbar-default-brand-color;\n &:hover,\n &:focus {\n color: @navbar-default-brand-hover-color;\n background-color: @navbar-default-brand-hover-bg;\n }\n }\n\n .navbar-text {\n color: @navbar-default-color;\n }\n\n .navbar-nav {\n > li > a {\n color: @navbar-default-link-color;\n\n &:hover,\n &:focus {\n color: @navbar-default-link-hover-color;\n background-color: @navbar-default-link-hover-bg;\n }\n }\n > .active > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-default-link-active-color;\n background-color: @navbar-default-link-active-bg;\n }\n }\n > .disabled > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-default-link-disabled-color;\n background-color: @navbar-default-link-disabled-bg;\n }\n }\n }\n\n .navbar-toggle {\n border-color: @navbar-default-toggle-border-color;\n &:hover,\n &:focus {\n background-color: @navbar-default-toggle-hover-bg;\n }\n .icon-bar {\n background-color: @navbar-default-toggle-icon-bar-bg;\n }\n }\n\n .navbar-collapse,\n .navbar-form {\n border-color: @navbar-default-border;\n }\n\n // Dropdown menu items\n .navbar-nav {\n // Remove background color from open dropdown\n > .open > a {\n &,\n &:hover,\n &:focus {\n background-color: @navbar-default-link-active-bg;\n color: @navbar-default-link-active-color;\n }\n }\n\n @media (max-width: @grid-float-breakpoint-max) {\n // Dropdowns get custom display when collapsed\n .open .dropdown-menu {\n > li > a {\n color: @navbar-default-link-color;\n &:hover,\n &:focus {\n color: @navbar-default-link-hover-color;\n background-color: @navbar-default-link-hover-bg;\n }\n }\n > .active > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-default-link-active-color;\n background-color: @navbar-default-link-active-bg;\n }\n }\n > .disabled > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-default-link-disabled-color;\n background-color: @navbar-default-link-disabled-bg;\n }\n }\n }\n }\n }\n\n\n // Links in navbars\n //\n // Add a class to ensure links outside the navbar nav are colored correctly.\n\n .navbar-link {\n color: @navbar-default-link-color;\n &:hover {\n color: @navbar-default-link-hover-color;\n }\n }\n\n .btn-link {\n color: @navbar-default-link-color;\n &:hover,\n &:focus {\n color: @navbar-default-link-hover-color;\n }\n &[disabled],\n fieldset[disabled] & {\n &:hover,\n &:focus {\n color: @navbar-default-link-disabled-color;\n }\n }\n }\n}\n\n// Inverse navbar\n\n.navbar-inverse {\n background-color: @navbar-inverse-bg;\n border-color: @navbar-inverse-border;\n\n .navbar-brand {\n color: @navbar-inverse-brand-color;\n &:hover,\n &:focus {\n color: @navbar-inverse-brand-hover-color;\n background-color: @navbar-inverse-brand-hover-bg;\n }\n }\n\n .navbar-text {\n color: @navbar-inverse-color;\n }\n\n .navbar-nav {\n > li > a {\n color: @navbar-inverse-link-color;\n\n &:hover,\n &:focus {\n color: @navbar-inverse-link-hover-color;\n background-color: @navbar-inverse-link-hover-bg;\n }\n }\n > .active > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-inverse-link-active-color;\n background-color: @navbar-inverse-link-active-bg;\n }\n }\n > .disabled > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-inverse-link-disabled-color;\n background-color: @navbar-inverse-link-disabled-bg;\n }\n }\n }\n\n // Darken the responsive nav toggle\n .navbar-toggle {\n border-color: @navbar-inverse-toggle-border-color;\n &:hover,\n &:focus {\n background-color: @navbar-inverse-toggle-hover-bg;\n }\n .icon-bar {\n background-color: @navbar-inverse-toggle-icon-bar-bg;\n }\n }\n\n .navbar-collapse,\n .navbar-form {\n border-color: darken(@navbar-inverse-bg, 7%);\n }\n\n // Dropdowns\n .navbar-nav {\n > .open > a {\n &,\n &:hover,\n &:focus {\n background-color: @navbar-inverse-link-active-bg;\n color: @navbar-inverse-link-active-color;\n }\n }\n\n @media (max-width: @grid-float-breakpoint-max) {\n // Dropdowns get custom display\n .open .dropdown-menu {\n > .dropdown-header {\n border-color: @navbar-inverse-border;\n }\n .divider {\n background-color: @navbar-inverse-border;\n }\n > li > a {\n color: @navbar-inverse-link-color;\n &:hover,\n &:focus {\n color: @navbar-inverse-link-hover-color;\n background-color: @navbar-inverse-link-hover-bg;\n }\n }\n > .active > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-inverse-link-active-color;\n background-color: @navbar-inverse-link-active-bg;\n }\n }\n > .disabled > a {\n &,\n &:hover,\n &:focus {\n color: @navbar-inverse-link-disabled-color;\n background-color: @navbar-inverse-link-disabled-bg;\n }\n }\n }\n }\n }\n\n .navbar-link {\n color: @navbar-inverse-link-color;\n &:hover {\n color: @navbar-inverse-link-hover-color;\n }\n }\n\n .btn-link {\n color: @navbar-inverse-link-color;\n &:hover,\n &:focus {\n color: @navbar-inverse-link-hover-color;\n }\n &[disabled],\n fieldset[disabled] & {\n &:hover,\n &:focus {\n color: @navbar-inverse-link-disabled-color;\n }\n }\n }\n}\n","// Navbar vertical align\n//\n// Vertically center elements in the navbar.\n// Example: an element has a height of 30px, so write out `.navbar-vertical-align(30px);` to calculate the appropriate top margin.\n\n.navbar-vertical-align(@element-height) {\n margin-top: ((@navbar-height - @element-height) / 2);\n margin-bottom: ((@navbar-height - @element-height) / 2);\n}\n","//\n// Utility classes\n// --------------------------------------------------\n\n\n// Floats\n// -------------------------\n\n.clearfix {\n .clearfix();\n}\n.center-block {\n .center-block();\n}\n.pull-right {\n float: right !important;\n}\n.pull-left {\n float: left !important;\n}\n\n\n// Toggling content\n// -------------------------\n\n// Note: Deprecated .hide in favor of .hidden or .sr-only (as appropriate) in v3.0.1\n.hide {\n display: none !important;\n}\n.show {\n display: block !important;\n}\n.invisible {\n visibility: hidden;\n}\n.text-hide {\n .text-hide();\n}\n\n\n// Hide from screenreaders and browsers\n//\n// Credit: HTML5 Boilerplate\n\n.hidden {\n display: none !important;\n}\n\n\n// For Affix plugin\n// -------------------------\n\n.affix {\n position: fixed;\n}\n","//\n// Breadcrumbs\n// --------------------------------------------------\n\n\n.breadcrumb {\n padding: @breadcrumb-padding-vertical @breadcrumb-padding-horizontal;\n margin-bottom: @line-height-computed;\n list-style: none;\n background-color: @breadcrumb-bg;\n border-radius: @border-radius-base;\n\n > li {\n display: inline-block;\n\n + li:before {\n content: \"@{breadcrumb-separator}\\00a0\"; // Unicode space added since inline-block means non-collapsing white-space\n padding: 0 5px;\n color: @breadcrumb-color;\n }\n }\n\n > .active {\n color: @breadcrumb-active-color;\n }\n}\n","//\n// Pagination (multiple pages)\n// --------------------------------------------------\n.pagination {\n display: inline-block;\n padding-left: 0;\n margin: @line-height-computed 0;\n border-radius: @border-radius-base;\n\n > li {\n display: inline; // Remove list-style and block-level defaults\n > a,\n > span {\n position: relative;\n float: left; // Collapse white-space\n padding: @padding-base-vertical @padding-base-horizontal;\n line-height: @line-height-base;\n text-decoration: none;\n color: @pagination-color;\n background-color: @pagination-bg;\n border: 1px solid @pagination-border;\n margin-left: -1px;\n }\n &:first-child {\n > a,\n > span {\n margin-left: 0;\n .border-left-radius(@border-radius-base);\n }\n }\n &:last-child {\n > a,\n > span {\n .border-right-radius(@border-radius-base);\n }\n }\n }\n\n > li > a,\n > li > span {\n &:hover,\n &:focus {\n z-index: 2;\n color: @pagination-hover-color;\n background-color: @pagination-hover-bg;\n border-color: @pagination-hover-border;\n }\n }\n\n > .active > a,\n > .active > span {\n &,\n &:hover,\n &:focus {\n z-index: 3;\n color: @pagination-active-color;\n background-color: @pagination-active-bg;\n border-color: @pagination-active-border;\n cursor: default;\n }\n }\n\n > .disabled {\n > span,\n > span:hover,\n > span:focus,\n > a,\n > a:hover,\n > a:focus {\n color: @pagination-disabled-color;\n background-color: @pagination-disabled-bg;\n border-color: @pagination-disabled-border;\n cursor: @cursor-disabled;\n }\n }\n}\n\n// Sizing\n// --------------------------------------------------\n\n// Large\n.pagination-lg {\n .pagination-size(@padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @border-radius-large);\n}\n\n// Small\n.pagination-sm {\n .pagination-size(@padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @border-radius-small);\n}\n","// Pagination\n\n.pagination-size(@padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n > li {\n > a,\n > span {\n padding: @padding-vertical @padding-horizontal;\n font-size: @font-size;\n line-height: @line-height;\n }\n &:first-child {\n > a,\n > span {\n .border-left-radius(@border-radius);\n }\n }\n &:last-child {\n > a,\n > span {\n .border-right-radius(@border-radius);\n }\n }\n }\n}\n","//\n// Pager pagination\n// --------------------------------------------------\n\n\n.pager {\n padding-left: 0;\n margin: @line-height-computed 0;\n list-style: none;\n text-align: center;\n &:extend(.clearfix all);\n li {\n display: inline;\n > a,\n > span {\n display: inline-block;\n padding: 5px 14px;\n background-color: @pager-bg;\n border: 1px solid @pager-border;\n border-radius: @pager-border-radius;\n }\n\n > a:hover,\n > a:focus {\n text-decoration: none;\n background-color: @pager-hover-bg;\n }\n }\n\n .next {\n > a,\n > span {\n float: right;\n }\n }\n\n .previous {\n > a,\n > span {\n float: left;\n }\n }\n\n .disabled {\n > a,\n > a:hover,\n > a:focus,\n > span {\n color: @pager-disabled-color;\n background-color: @pager-bg;\n cursor: @cursor-disabled;\n }\n }\n}\n","//\n// Labels\n// --------------------------------------------------\n\n.label {\n display: inline;\n padding: .2em .6em .3em;\n font-size: 75%;\n font-weight: bold;\n line-height: 1;\n color: @label-color;\n text-align: center;\n white-space: nowrap;\n vertical-align: baseline;\n border-radius: .25em;\n\n // Add hover effects, but only for links\n a& {\n &:hover,\n &:focus {\n color: @label-link-hover-color;\n text-decoration: none;\n cursor: pointer;\n }\n }\n\n // Empty labels collapse automatically (not available in IE8)\n &:empty {\n display: none;\n }\n\n // Quick fix for labels in buttons\n .btn & {\n position: relative;\n top: -1px;\n }\n}\n\n// Colors\n// Contextual variations (linked labels get darker on :hover)\n\n.label-default {\n .label-variant(@label-default-bg);\n}\n\n.label-primary {\n .label-variant(@label-primary-bg);\n}\n\n.label-success {\n .label-variant(@label-success-bg);\n}\n\n.label-info {\n .label-variant(@label-info-bg);\n}\n\n.label-warning {\n .label-variant(@label-warning-bg);\n}\n\n.label-danger {\n .label-variant(@label-danger-bg);\n}\n","// Labels\n\n.label-variant(@color) {\n background-color: @color;\n\n &[href] {\n &:hover,\n &:focus {\n background-color: darken(@color, 10%);\n }\n }\n}\n","//\n// Badges\n// --------------------------------------------------\n\n\n// Base class\n.badge {\n display: inline-block;\n min-width: 10px;\n padding: 3px 7px;\n font-size: @font-size-small;\n font-weight: @badge-font-weight;\n color: @badge-color;\n line-height: @badge-line-height;\n vertical-align: middle;\n white-space: nowrap;\n text-align: center;\n background-color: @badge-bg;\n border-radius: @badge-border-radius;\n\n // Empty badges collapse automatically (not available in IE8)\n &:empty {\n display: none;\n }\n\n // Quick fix for badges in buttons\n .btn & {\n position: relative;\n top: -1px;\n }\n\n .btn-xs &,\n .btn-group-xs > .btn & {\n top: 0;\n padding: 1px 5px;\n }\n\n // Hover state, but only for links\n a& {\n &:hover,\n &:focus {\n color: @badge-link-hover-color;\n text-decoration: none;\n cursor: pointer;\n }\n }\n\n // Account for badges in navs\n .list-group-item.active > &,\n .nav-pills > .active > a > & {\n color: @badge-active-color;\n background-color: @badge-active-bg;\n }\n\n .list-group-item > & {\n float: right;\n }\n\n .list-group-item > & + & {\n margin-right: 5px;\n }\n\n .nav-pills > li > a > & {\n margin-left: 3px;\n }\n}\n","//\n// Jumbotron\n// --------------------------------------------------\n\n\n.jumbotron {\n padding-top: @jumbotron-padding;\n padding-bottom: @jumbotron-padding;\n margin-bottom: @jumbotron-padding;\n color: @jumbotron-color;\n background-color: @jumbotron-bg;\n\n h1,\n .h1 {\n color: @jumbotron-heading-color;\n }\n\n p {\n margin-bottom: (@jumbotron-padding / 2);\n font-size: @jumbotron-font-size;\n font-weight: 200;\n }\n\n > hr {\n border-top-color: darken(@jumbotron-bg, 10%);\n }\n\n .container &,\n .container-fluid & {\n border-radius: @border-radius-large; // Only round corners at higher resolutions if contained in a container\n padding-left: (@grid-gutter-width / 2);\n padding-right: (@grid-gutter-width / 2);\n }\n\n .container {\n max-width: 100%;\n }\n\n @media screen and (min-width: @screen-sm-min) {\n padding-top: (@jumbotron-padding * 1.6);\n padding-bottom: (@jumbotron-padding * 1.6);\n\n .container &,\n .container-fluid & {\n padding-left: (@jumbotron-padding * 2);\n padding-right: (@jumbotron-padding * 2);\n }\n\n h1,\n .h1 {\n font-size: @jumbotron-heading-font-size;\n }\n }\n}\n","//\n// Thumbnails\n// --------------------------------------------------\n\n\n// Mixin and adjust the regular image class\n.thumbnail {\n display: block;\n padding: @thumbnail-padding;\n margin-bottom: @line-height-computed;\n line-height: @line-height-base;\n background-color: @thumbnail-bg;\n border: 1px solid @thumbnail-border;\n border-radius: @thumbnail-border-radius;\n .transition(border .2s ease-in-out);\n\n > img,\n a > img {\n &:extend(.img-responsive);\n margin-left: auto;\n margin-right: auto;\n }\n\n // Add a hover state for linked versions only\n a&:hover,\n a&:focus,\n a&.active {\n border-color: @link-color;\n }\n\n // Image captions\n .caption {\n padding: @thumbnail-caption-padding;\n color: @thumbnail-caption-color;\n }\n}\n","//\n// Alerts\n// --------------------------------------------------\n\n\n// Base styles\n// -------------------------\n\n.alert {\n padding: @alert-padding;\n margin-bottom: @line-height-computed;\n border: 1px solid transparent;\n border-radius: @alert-border-radius;\n\n // Headings for larger alerts\n h4 {\n margin-top: 0;\n // Specified for the h4 to prevent conflicts of changing @headings-color\n color: inherit;\n }\n\n // Provide class for links that match alerts\n .alert-link {\n font-weight: @alert-link-font-weight;\n }\n\n // Improve alignment and spacing of inner content\n > p,\n > ul {\n margin-bottom: 0;\n }\n\n > p + p {\n margin-top: 5px;\n }\n}\n\n// Dismissible alerts\n//\n// Expand the right padding and account for the close button's positioning.\n\n.alert-dismissable, // The misspelled .alert-dismissable was deprecated in 3.2.0.\n.alert-dismissible {\n padding-right: (@alert-padding + 20);\n\n // Adjust close link position\n .close {\n position: relative;\n top: -2px;\n right: -21px;\n color: inherit;\n }\n}\n\n// Alternate styles\n//\n// Generate contextual modifier classes for colorizing the alert.\n\n.alert-success {\n .alert-variant(@alert-success-bg; @alert-success-border; @alert-success-text);\n}\n\n.alert-info {\n .alert-variant(@alert-info-bg; @alert-info-border; @alert-info-text);\n}\n\n.alert-warning {\n .alert-variant(@alert-warning-bg; @alert-warning-border; @alert-warning-text);\n}\n\n.alert-danger {\n .alert-variant(@alert-danger-bg; @alert-danger-border; @alert-danger-text);\n}\n","// Alerts\n\n.alert-variant(@background; @border; @text-color) {\n background-color: @background;\n border-color: @border;\n color: @text-color;\n\n hr {\n border-top-color: darken(@border, 5%);\n }\n .alert-link {\n color: darken(@text-color, 10%);\n }\n}\n","//\n// Progress bars\n// --------------------------------------------------\n\n\n// Bar animations\n// -------------------------\n\n// WebKit\n@-webkit-keyframes progress-bar-stripes {\n from { background-position: 40px 0; }\n to { background-position: 0 0; }\n}\n\n// Spec and IE10+\n@keyframes progress-bar-stripes {\n from { background-position: 40px 0; }\n to { background-position: 0 0; }\n}\n\n\n// Bar itself\n// -------------------------\n\n// Outer container\n.progress {\n overflow: hidden;\n height: @line-height-computed;\n margin-bottom: @line-height-computed;\n background-color: @progress-bg;\n border-radius: @progress-border-radius;\n .box-shadow(inset 0 1px 2px rgba(0,0,0,.1));\n}\n\n// Bar of progress\n.progress-bar {\n float: left;\n width: 0%;\n height: 100%;\n font-size: @font-size-small;\n line-height: @line-height-computed;\n color: @progress-bar-color;\n text-align: center;\n background-color: @progress-bar-bg;\n .box-shadow(inset 0 -1px 0 rgba(0,0,0,.15));\n .transition(width .6s ease);\n}\n\n// Striped bars\n//\n// `.progress-striped .progress-bar` is deprecated as of v3.2.0 in favor of the\n// `.progress-bar-striped` class, which you just add to an existing\n// `.progress-bar`.\n.progress-striped .progress-bar,\n.progress-bar-striped {\n #gradient > .striped();\n background-size: 40px 40px;\n}\n\n// Call animation for the active one\n//\n// `.progress.active .progress-bar` is deprecated as of v3.2.0 in favor of the\n// `.progress-bar.active` approach.\n.progress.active .progress-bar,\n.progress-bar.active {\n .animation(progress-bar-stripes 2s linear infinite);\n}\n\n\n// Variations\n// -------------------------\n\n.progress-bar-success {\n .progress-bar-variant(@progress-bar-success-bg);\n}\n\n.progress-bar-info {\n .progress-bar-variant(@progress-bar-info-bg);\n}\n\n.progress-bar-warning {\n .progress-bar-variant(@progress-bar-warning-bg);\n}\n\n.progress-bar-danger {\n .progress-bar-variant(@progress-bar-danger-bg);\n}\n","// Gradients\n\n#gradient {\n\n // Horizontal gradient, from left to right\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n background-repeat: repeat-x;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down\n }\n\n // Vertical gradient, from top to bottom\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n background-repeat: repeat-x;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down\n }\n\n .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n background-repeat: repeat-x;\n background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n }\n .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n background-repeat: no-repeat;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n }\n .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-repeat: no-repeat;\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\",argb(@start-color),argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n }\n .radial(@inner-color: #555; @outer-color: #333) {\n background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n background-image: radial-gradient(circle, @inner-color, @outer-color);\n background-repeat: no-repeat;\n }\n .striped(@color: rgba(255,255,255,.15); @angle: 45deg) {\n background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n }\n}\n","// Progress bars\n\n.progress-bar-variant(@color) {\n background-color: @color;\n\n // Deprecated parent class requirement as of v3.2.0\n .progress-striped & {\n #gradient > .striped();\n }\n}\n",".media {\n // Proper spacing between instances of .media\n margin-top: 15px;\n\n &:first-child {\n margin-top: 0;\n }\n}\n\n.media,\n.media-body {\n zoom: 1;\n overflow: hidden;\n}\n\n.media-body {\n width: 10000px;\n}\n\n.media-object {\n display: block;\n\n // Fix collapse in webkit from max-width: 100% and display: table-cell.\n &.img-thumbnail {\n max-width: none;\n }\n}\n\n.media-right,\n.media > .pull-right {\n padding-left: 10px;\n}\n\n.media-left,\n.media > .pull-left {\n padding-right: 10px;\n}\n\n.media-left,\n.media-right,\n.media-body {\n display: table-cell;\n vertical-align: top;\n}\n\n.media-middle {\n vertical-align: middle;\n}\n\n.media-bottom {\n vertical-align: bottom;\n}\n\n// Reset margins on headings for tighter default spacing\n.media-heading {\n margin-top: 0;\n margin-bottom: 5px;\n}\n\n// Media list variation\n//\n// Undo default ul/ol styles\n.media-list {\n padding-left: 0;\n list-style: none;\n}\n","//\n// List groups\n// --------------------------------------------------\n\n\n// Base class\n//\n// Easily usable on