fix: code format

book_maker/cli.py

@@ -186,7 +186,6 @@ So you are close to reaching the limit. You have to choose your own value, there
         "--batch_size",
         dest="batch_size",
         type=int,
-        default=10,
         help="how many lines will be translated by aggregated translation(This options currently only applies to txt files)",
     )
 
@@ -260,12 +259,17 @@ So you are close to reaching the limit. You have to choose your own value, there
         model_api_base=model_api_base,
         is_test=options.test,
         test_num=options.test_num,
-        translate_tags=options.translate_tags,
-        allow_navigable_strings=options.allow_navigable_strings,
-        accumulated_num=options.accumulated_num,
         prompt_config=parse_prompt_arg(options.prompt_arg),
-        batch_size=options.batch_size,
     )
+    # other options
+    if options.allow_navigable_strings:
+        e.allow_navigable_strings = True
+    if options.translate_tags:
+        e.translate_tags = options.translate_tags
+    if options.accumulated_num > 1:
+        e.accumulated_num = options.accumulated_num
+    if options.batch_size:
+        e.batch_size = options.batch_size
     e.make_bilingual_book()
 
 

book_maker/loader/epub_loader.py

@@ -1,7 +1,6 @@
 import os
-import re
 import pickle
-import tiktoken
+import string
 import sys
 from copy import copy
 from pathlib import Path
@@ -12,98 +11,11 @@ from ebooklib import ITEM_DOCUMENT, epub
 from rich import print
 from tqdm import tqdm
 
-from book_maker.utils import prompt_config_to_kwargs
+from book_maker.utils import num_tokens_from_text, prompt_config_to_kwargs
 
 from .base_loader import BaseBookLoader
-
+from .helper import (EPUBBookLoaderHelper, is_text_figure, is_text_link,
-
+                     is_text_list, is_text_source, is_text_tail_link)
-class EPUBBookLoaderHelper:
-    def __init__(self, translate_model, accumulated_num):
-        self.translate_model = translate_model
-        self.accumulated_num = accumulated_num
-
-    def deal_new(self, p, wait_p_list):
-        self.deal_old(wait_p_list)
-        new_p = copy(p)
-        new_p.string = self.translate_model.translate(p.text)
-        p.insert_after(new_p)
-
-    def deal_old(self, wait_p_list):
-        if len(wait_p_list) == 0:
-            return
-
-        result_txt_list = self.translate_model.translate_list(wait_p_list)
-
-        for i in range(len(wait_p_list)):
-            if i < len(result_txt_list):
-                p = wait_p_list[i]
-                new_p = copy(p)
-                new_p.string = result_txt_list[i]
-                p.insert_after(new_p)
-
-        wait_p_list.clear()
-
-
-# ref: https://platform.openai.com/docs/guides/chat/introduction
-def num_tokens_from_text(text, model="gpt-3.5-turbo-0301"):
-    messages = (
-        {
-            "role": "user",
-            "content": text,
-        },
-    )
-
-    """Returns the number of tokens used by a list of messages."""
-    try:
-        encoding = tiktoken.encoding_for_model(model)
-    except KeyError:
-        encoding = tiktoken.get_encoding("cl100k_base")
-    if model == "gpt-3.5-turbo-0301":  # note: future models may deviate from this
-        num_tokens = 0
-        for message in messages:
-            num_tokens += (
-                4  # every message follows <im_start>{role/name}\n{content}<im_end>\n
-            )
-            for key, value in message.items():
-                num_tokens += len(encoding.encode(value))
-                if key == "name":  # if there's a name, the role is omitted
-                    num_tokens += -1  # role is always required and always 1 token
-        num_tokens += 2  # every reply is primed with <im_start>assistant
-        return num_tokens
-    else:
-        raise NotImplementedError(
-            f"""num_tokens_from_messages() is not presently implemented for model {model}.
-  See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens."""
-        )
-
-
-def is_link(text):
-    url_pattern = re.compile(
-        r"http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+"
-    )
-    return bool(url_pattern.match(text.strip()))
-
-
-def is_tail_Link(text, num=100):
-    text = text.strip()
-    url_pattern = re.compile(
-        r".*http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+$"
-    )
-    return bool(url_pattern.match(text)) and len(text) < num
-
-
-def is_source(text):
-    return text.strip().startswith("Source: ")
-
-
-def is_list(text, num=80):
-    text = text.strip()
-    return re.match(r"^Listing\s*\d+", text) and len(text) < num
-
-
-def is_figure(text, num=80):
-    text = text.strip()
-    return re.match(r"^Figure\s*\d+", text) and len(text) < num
 
 
 class EPUBBookLoader(BaseBookLoader):
@@ -114,14 +26,9 @@ class EPUBBookLoader(BaseBookLoader):
         key,
         resume,
         language,
-        batch_size,
         model_api_base=None,
         is_test=False,
         test_num=5,
-        translate_tags="p",
-        allow_navigable_strings=False,
-        accumulated_num=1,
-        prompt_template=None,
         prompt_config=None,
     ):
         self.epub_name = epub_name
@@ -134,9 +41,9 @@ class EPUBBookLoader(BaseBookLoader):
         )
         self.is_test = is_test
         self.test_num = test_num
-        self.translate_tags = translate_tags
+        self.translate_tags = "p"
-        self.allow_navigable_strings = allow_navigable_strings
+        self.allow_navigable_strings = False
-        self.accumulated_num = accumulated_num
+        self.accumulated_num = 1
         self.helper = EPUBBookLoaderHelper(self.translate_model, self.accumulated_num)
 
         try:
@@ -165,7 +72,12 @@ class EPUBBookLoader(BaseBookLoader):
 
     @staticmethod
     def _is_special_text(text):
-        return text.isdigit() or text.isspace() or is_link(text)
+        return (
+            text.isdigit()
+            or text.isspace()
+            or is_text_link(text)
+            or all(char in string.punctuation for char in text)
+        )
 
     def _make_new_book(self, book):
         new_book = epub.EpubBook()
@@ -206,13 +118,15 @@ class EPUBBookLoader(BaseBookLoader):
             temp_p = copy(p)
             for sup in temp_p.find_all("sup"):
                 sup.extract()
-            if (
+            if any(
-                not p.text
+                [
-                or self._is_special_text(temp_p.text)
+                    not p.text,
-                or is_source(temp_p.text)
+                    self._is_special_text(temp_p.text),
-                or is_list(temp_p.text)
+                    is_text_source(temp_p.text),
-                or is_figure(temp_p.text)
+                    is_text_list(temp_p.text),
-                or is_tail_Link(temp_p.text)
+                    is_text_figure(temp_p.text),
+                    is_text_tail_link(temp_p.text),
+                ]
             ):
                 if i == len(p_list) - 1:
                     self.helper.deal_old(wait_p_list)

book_maker/loader/helper.py

@@ -0,0 +1,58 @@
+import re
+from copy import copy
+
+
+class EPUBBookLoaderHelper:
+    def __init__(self, translate_model, accumulated_num):
+        self.translate_model = translate_model
+        self.accumulated_num = accumulated_num
+
+    def deal_new(self, p, wait_p_list):
+        self.deal_old(wait_p_list)
+        new_p = copy(p)
+        new_p.string = self.translate_model.translate(p.text)
+        p.insert_after(new_p)
+
+    def deal_old(self, wait_p_list):
+        if not wait_p_list:
+            return
+
+        result_txt_list = self.translate_model.translate_list(wait_p_list)
+
+        for i in range(len(wait_p_list)):
+            if i < len(result_txt_list):
+                p = wait_p_list[i]
+                new_p = copy(p)
+                new_p.string = result_txt_list[i]
+                p.insert_after(new_p)
+
+        wait_p_list.clear()
+
+
+def is_text_link(text):
+    url_pattern = re.compile(
+        r"http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+"
+    )
+    return bool(url_pattern.match(text.strip()))
+
+
+def is_text_tail_link(text, num=100):
+    text = text.strip()
+    url_pattern = re.compile(
+        r".*http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+$"
+    )
+    return bool(url_pattern.match(text)) and len(text) < num
+
+
+def is_text_source(text):
+    return text.strip().startswith("Source: ")
+
+
+def is_text_list(text, num=80):
+    text = text.strip()
+    return re.match(r"^Listing\s*\d+", text) and len(text) < num
+
+
+def is_text_figure(text, num=80):
+    text = text.strip()
+    return re.match(r"^Figure\s*\d+", text) and len(text) < num

book_maker/loader/txt_loader.py

@@ -14,14 +14,9 @@ class TXTBookLoader(BaseBookLoader):
         key,
         resume,
         language,
-        batch_size,
-        translate_tags,
-        allow_navigable_strings,
         model_api_base=None,
         is_test=False,
         test_num=5,
-        accumulated_num=1,
-        prompt_template=None,
         prompt_config=None,
     ):
         self.txt_name = txt_name
@@ -36,7 +31,7 @@ class TXTBookLoader(BaseBookLoader):
         self.bilingual_result = []
         self.bilingual_temp_result = []
         self.test_num = test_num
-        self.batch_size = batch_size
+        self.batch_size = 10
 
         try:
             with open(f"{txt_name}", "r", encoding="utf-8") as f:

book_maker/obok.py

@@ -199,18 +199,8 @@ class ENCRYPTIONError(Exception):
 
 
 def _load_crypto_libcrypto():
-    from ctypes import (
+    from ctypes import (CDLL, POINTER, Structure, c_char_p, c_int, c_long,
-        CDLL,
+                        c_ulong, c_void_p, cast, create_string_buffer)
-        POINTER,
-        Structure,
-        c_char_p,
-        c_int,
-        c_long,
-        c_ulong,
-        c_void_p,
-        cast,
-        create_string_buffer,
-    )
     from ctypes.util import find_library
 
     if sys.platform.startswith("win"):

book_maker/translator/__init__.py

@@ -1,8 +1,8 @@
+from book_maker.translator.caiyun_translator import Caiyun
 from book_maker.translator.chatgptapi_translator import ChatGPTAPI
+from book_maker.translator.deepl_translator import DeepL
 from book_maker.translator.google_translator import Google
 from book_maker.translator.gpt3_translator import GPT3
-from book_maker.translator.caiyun_translator import Caiyun
-from book_maker.translator.deepl_translator import DeepL
 
 MODEL_DICT = {
     "chatgptapi": ChatGPTAPI,

book_maker/translator/chatgptapi_translator.py

@@ -1,5 +1,5 @@
-import time
 import re
+import time
 from copy import copy
 from os import environ
 

book_maker/translator/deepl_translator.py

@@ -3,7 +3,8 @@ import time
 
 import requests
 
-from book_maker.utils import TO_LANGUAGE_CODE, LANGUAGES
+from book_maker.utils import LANGUAGES, TO_LANGUAGE_CODE
+
 from .base_translator import Base
 
 

book_maker/utils.py

@@ -1,3 +1,5 @@
+import tiktoken
+
 # Borrowed from : https://github.com/openai/whisper
 LANGUAGES = {
     "en": "english",
@@ -126,3 +128,36 @@ def prompt_config_to_kwargs(prompt_config):
         prompt_template=prompt_config.get("user", None),
         prompt_sys_msg=prompt_config.get("system", None),
     )
+
+
+# ref: https://platform.openai.com/docs/guides/chat/introduction
+def num_tokens_from_text(text, model="gpt-3.5-turbo-0301"):
+    messages = (
+        {
+            "role": "user",
+            "content": text,
+        },
+    )
+
+    """Returns the number of tokens used by a list of messages."""
+    try:
+        encoding = tiktoken.encoding_for_model(model)
+    except KeyError:
+        encoding = tiktoken.get_encoding("cl100k_base")
+    if model == "gpt-3.5-turbo-0301":  # note: future models may deviate from this
+        num_tokens = 0
+        for message in messages:
+            num_tokens += (
+                4  # every message follows <im_start>{role/name}\n{content}<im_end>\n
+            )
+            for key, value in message.items():
+                num_tokens += len(encoding.encode(value))
+                if key == "name":  # if there's a name, the role is omitted
+                    num_tokens += -1  # role is always required and always 1 token
+        num_tokens += 2  # every reply is primed with <im_start>assistant
+        return num_tokens
+    else:
+        raise NotImplementedError(
+            f"""num_tokens_from_messages() is not presently implemented for model {model}.
+  See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens."""
+        )