import time
import datetime
import uuid
import os
import sys
import json
from typing import List, Tuple, Type, Dict
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

from sources.utility import timer_decorator, pretty_print, animate_thinking
from sources.logger import Logger

class Memory():
    """
    Memory is a class for managing the conversation memory
    It provides a method to compress the memory using summarization model.
    """
    def __init__(self, system_prompt: str,
                 recover_last_session: bool = False,
                 memory_compression: bool = True,
                 model_provider: str = "deepseek-r1:14b"):
        self.memory = [{'role': 'system', 'content': system_prompt}]
        
        self.logger = Logger("memory.log")
        self.session_time = datetime.datetime.now()
        self.session_id = str(uuid.uuid4())
        self.conversation_folder = f"conversations/"
        self.session_recovered = False
        if recover_last_session:
            self.load_memory()
            self.session_recovered = True
        # memory compression system
        self.model = None
        self.tokenizer = None
        self.device = self.get_cuda_device()
        self.memory_compression = memory_compression
        self.model_provider = model_provider
        if self.memory_compression:
            self.download_model()

    def get_ideal_ctx(self, model_name: str) -> int | None:
        """
        Estimate context size based on the model name.
        EXPERIMENTAL for memory compression
        """
        import re
        import math

        def extract_number_before_b(sentence: str) -> int:
            match = re.search(r'(\d+)b', sentence, re.IGNORECASE)
            return int(match.group(1)) if match else None

        model_size = extract_number_before_b(model_name)
        if not model_size:
            return None
        base_size = 7  # Base model size in billions
        base_context = 4096  # Base context size in tokens
        scaling_factor = 1.5  # Approximate scaling factor for context size growth
        context_size = int(base_context * (model_size / base_size) ** scaling_factor)
        context_size = 2 ** round(math.log2(context_size))
        self.logger.info(f"Estimated context size for {model_name}: {context_size} tokens.")
        return context_size
    
    def download_model(self):
        """Download the model if not already downloaded."""
        animate_thinking("Loading memory compression model...", color="status")
        self.tokenizer = AutoTokenizer.from_pretrained("pszemraj/led-base-book-summary")
        self.model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/led-base-book-summary")
        self.logger.info("Memory compression system initialized.")
    
    def get_filename(self) -> str:
        """Get the filename for the save file."""
        return f"memory_{self.session_time.strftime('%Y-%m-%d_%H-%M-%S')}.txt"
    
    def save_memory(self, agent_type: str = "casual_agent") -> None:
        """Save the session memory to a file."""
        if not os.path.exists(self.conversation_folder):
            self.logger.info(f"Created folder {self.conversation_folder}.")
            os.makedirs(self.conversation_folder)
        save_path = os.path.join(self.conversation_folder, agent_type)
        if not os.path.exists(save_path):
            os.makedirs(save_path)
        filename = self.get_filename()
        path = os.path.join(save_path, filename)
        json_memory = json.dumps(self.memory)
        with open(path, 'w') as f:
            self.logger.info(f"Saved memory json at {path}")
            f.write(json_memory)
    
    def find_last_session_path(self, path) -> str:
        """Find the last session path."""
        saved_sessions = []
        for filename in os.listdir(path):
            if filename.startswith('memory_'):
                date = filename.split('_')[1]
                saved_sessions.append((filename, date))
        saved_sessions.sort(key=lambda x: x[1], reverse=True)
        if len(saved_sessions) > 0:
            self.logger.info(f"Last session found at {saved_sessions[0][0]}")
            return saved_sessions[0][0]
        return None
    
    def save_json_file(self, path: str, json_memory: dict) -> None:
        """Save a JSON file."""
        try:
            with open(path, 'w') as f:
                json.dump(json_memory, f)
                self.logger.info(f"Saved memory json at {path}")
        except Exception as e:
            self.logger.warning(f"Error saving file {path}: {e}")
    
    def load_json_file(self, path: str) -> dict:
        """Load a JSON file."""
        json_memory = {}
        try:
            with open(path, 'r') as f:
                json_memory = json.load(f)
        except FileNotFoundError:
            self.logger.warning(f"File not found: {path}")
            return {}
        except json.JSONDecodeError:
            self.logger.warning(f"Error decoding JSON from file: {path}")
            return {}
        except Exception as e:
            self.logger.warning(f"Error loading file {path}: {e}")
            return {}
        return json_memory

    def load_memory(self, agent_type: str = "casual_agent") -> None:
        """Load the memory from the last session."""
        if self.session_recovered == True:
            return
        pretty_print(f"Loading {agent_type} past memories... ", color="status")
        save_path = os.path.join(self.conversation_folder, agent_type)
        if not os.path.exists(save_path):
            pretty_print("No memory to load.", color="success")
            return
        filename = self.find_last_session_path(save_path)
        if filename is None:
            pretty_print("Last session memory not found.", color="warning")
            return
        path = os.path.join(save_path, filename)
        self.memory = self.load_json_file(path) 
        if self.memory[-1]['role'] == 'user':
            self.memory.pop()
        self.compress()
        pretty_print("Session recovered successfully", color="success")
    
    def reset(self, memory: list = []) -> None:
        self.logger.info("Memory reset performed.")
        self.memory = memory
    
    def push(self, role: str, content: str) -> int:
        """Push a message to the memory."""
        ideal_ctx = self.get_ideal_ctx(self.model_provider)
        if ideal_ctx is not None:
            if self.memory_compression and len(content) > ideal_ctx * 1.5:
                self.logger.info(f"Compressing memory: Content {len(content)} > {ideal_ctx} model context.")
                self.compress()
        curr_idx = len(self.memory)
        if self.memory[curr_idx-1]['content'] == content:
            pretty_print("Warning: same message have been pushed twice to memory", color="error")
        time_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        self.memory.append({'role': role, 'content': content, 'time': time_str, 'model_used': self.model_provider})
        return curr_idx-1
    
    def clear(self) -> None:
        """Clear all memory except system prompt"""
        self.logger.info("Memory clear performed.")
        self.memory = self.memory[:1]
    
    def clear_section(self, start: int, end: int) -> None:
        """
        Clear a section of the memory. Ignore system message index.
        Args:
            start (int): Starting bound of the section to clear.
            end (int): Ending bound of the section to clear.
        """
        self.logger.info(f"Clearing memory section {start} to {end}.")
        start = max(0, start) + 1
        end = min(end, len(self.memory)-1) + 2
        self.memory = self.memory[:start] + self.memory[end:]
    
    def get(self) -> list:
        return self.memory

    def get_cuda_device(self) -> str:
        if torch.backends.mps.is_available():
            return "mps"
        elif torch.cuda.is_available():
            return "cuda"
        else:
            return "cpu"

    def summarize(self, text: str, min_length: int = 64) -> str:
        """
        Summarize the text using the AI model.
        Args:
            text (str): The text to summarize
            min_length (int, optional): The minimum length of the summary. Defaults to 64.
        Returns:
            str: The summarized text
        """
        if self.tokenizer is None or self.model is None:
            self.logger.warning("No tokenizer or model to perform summarization.")
            return text
        if len(text) < min_length*1.5:
            return text
        max_length = len(text) // 2 if len(text) > min_length*2 else min_length*2
        input_text = "summarize: " + text
        inputs = self.tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
        summary_ids = self.model.generate(
            inputs['input_ids'],
            max_length=max_length,
            min_length=min_length,
            length_penalty=1.0,
            num_beams=4,
            early_stopping=True
        )
        summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True)
        summary.replace('summary:', '')
        self.logger.info(f"Memory summarized from len {len(text)} to {len(summary)}.")
        self.logger.info(f"Summarized text:\n{summary}")
        return summary
    
    #@timer_decorator
    def compress(self) -> str:
        """
        Compress (summarize) the memory using the model.
        """
        if self.tokenizer is None or self.model is None:
            self.logger.warning("No tokenizer or model to perform memory compression.")
            return
        for i in range(len(self.memory)):
            if self.memory[i]['role'] == 'system':
                continue
            if len(self.memory[i]['content']) > 1024:
                self.memory[i]['content'] = self.summarize(self.memory[i]['content'])
    
    def trim_text_to_max_ctx(self, text: str) -> str:
        """
        Truncate a text to fit within the maximum context size of the model.
        """
        ideal_ctx = self.get_ideal_ctx(self.model_provider)
        return text[:ideal_ctx] if ideal_ctx is not None else text
    
    #@timer_decorator
    def compress_text_to_max_ctx(self, text) -> str:
        """
        Compress a text to fit within the maximum context size of the model.
        """
        if self.tokenizer is None or self.model is None:
            self.logger.warning("No tokenizer or model to perform memory compression.")
            return text
        ideal_ctx = self.get_ideal_ctx(self.model_provider)
        if ideal_ctx is None:
            self.logger.warning("No ideal context size found.")
            return text
        while len(text) > ideal_ctx:
            self.logger.info(f"Compressing text: {len(text)} > {ideal_ctx} model context.")
            text = self.summarize(text)
        return text

if __name__ == "__main__":
    sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
    memory = Memory("You are a helpful assistant.",
                    recover_last_session=False, memory_compression=True)

    memory.push('user', "hello")
    memory.push('assistant', "how can i help you?")
    memory.push('user', "why do i get this cuda error?")
    sample_text = """
The error you're encountering:
cuda.cu:52:10: fatal error: helper_functions.h: No such file or directory
 #include <helper_functions.h>
indicates that the compiler cannot find the helper_functions.h file. This is because the #include <helper_functions.h> directive is looking for the file in the system's include paths, but the file is either not in those paths or is located in a different directory.
1. Use #include "helper_functions.h" Instead of #include <helper_functions.h>
Angle brackets (< >) are used for system or standard library headers.
Quotes (" ") are used for local or project-specific headers.
If helper_functions.h is in the same directory as cuda.cu, change the include directive to:
3. Verify the File Exists
Double-check that helper_functions.h exists in the specified location. If the file is missing, you'll need to obtain or recreate it.
4. Use the Correct CUDA Samples Path (if applicable)
If helper_functions.h is part of the CUDA Samples, ensure you have the CUDA Samples installed and include the correct path. For example, on Linux, the CUDA Samples are typically located in /usr/local/cuda/samples/common/inc. You can include this path like so:
Use #include "helper_functions.h" for local files.
Use the -I flag to specify the directory containing helper_functions.h.
Ensure the file exists in the specified location.
    """
    memory.push('assistant', sample_text)
    
    print("\n---\nmemory before:", memory.get())
    memory.compress()
    print("\n---\nmemory after:", memory.get())
    #memory.save_memory()