




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


UpperCAmelCase_		:				Optional[Any]									=     logging.get_logger(__name__)

UpperCAmelCase_		:				str									=     {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_		:				List[Any]									=     {
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/tokenizer.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/tokenizer.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase_		:				Optional[Any]									=     {
    'gpt2': 1024,
    'gpt2-medium': 1024,
    'gpt2-large': 1024,
    'gpt2-xl': 1024,
    'distilgpt2': 1024,
}




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					VOCAB_FILES_NAMES
						UpperCamelCase     						=					PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase     						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase     						=					["""input_ids""", """attention_mask"""]
						UpperCamelCase     						=					GPTaTokenizer

						def __init__(		self   :Optional[int]    , __snake_case   :str=None    , __snake_case   :Tuple=None    , __snake_case   :Union[str, Any]=None    , __snake_case   :str="<|endoftext|>"    , __snake_case   :List[Any]="<|endoftext|>"    , __snake_case   :List[str]="<|endoftext|>"    , __snake_case   :Dict=False    , **__snake_case   :Union[str, Any]    , ):
										'''simple docstring'''

										super().__init__(
										    __A    , __A    , tokenizer_file=__A    , unk_token=__A    , bos_token=__A    , eos_token=__A    , add_prefix_space=__A    , **__A    , )

										__magic_name__	:	Any								=kwargs.pop("""add_bos_token"""    , __A )

										__magic_name__	:	Dict								=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
										if pre_tok_state.get("""add_prefix_space"""    , __A ) != add_prefix_space:
														__magic_name__	:	Dict								=getattr(__A    , pre_tok_state.pop("""type""" ) )
														__magic_name__	:	int								=add_prefix_space
														__magic_name__	:	Union[str, Any]								=pre_tok_class(**__A )

										__magic_name__	:	Tuple								=add_prefix_space

						def     A__						(		self   :Tuple    , *__snake_case   :Tuple    , **__snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	int								=kwargs.get("""is_split_into_words"""    , __A )
										assert self.add_prefix_space or not is_split_into_words, (
										    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*__A    , **__A )

						def     A__						(		self   :List[Any]    , *__snake_case   :Optional[int]    , **__snake_case   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=kwargs.get("""is_split_into_words"""    , __A )

										assert self.add_prefix_space or not is_split_into_words, (
										    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*__A    , **__A )

						def     A__						(		self   :Optional[Any]    , __snake_case   :str    , __snake_case   :Optional[str] = None ):
										'''simple docstring'''

										__magic_name__	:	int								=self._tokenizer.model.save(__A    , name=__A )
										return tuple(__A )


						def     A__						(		self   :Optional[Any]    , __snake_case   :"Conversation" ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=[]
										for is_user, text in conversation.iter_texts():
														input_ids.extend(self.encode(__A    , add_special_tokens=__A ) + [self.eos_token_id] )

										if len(__A ) > self.model_max_length:
														__magic_name__	:	Tuple								=input_ids[-self.model_max_length :]
										return input_ids










from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests




def 		lowerCAmelCase_   (	lowerCamelCase = "isbn/0140328726"   ):
				__magic_name__	:	Optional[Any]								=olid.strip().strip("""/"""   )  # Remove leading/trailing whitespace & slashes
				if new_olid.count("""/"""   ) != 1:
								__magic_name__	:	Union[str, Any]								=F"{olid} is not a valid Open Library olid"
								raise ValueError(lowerCamelCase   )
				return requests.get(F"https://openlibrary.org/{new_olid}.json"   ).json()




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Dict								={
				    """title""": """Title""",
				    """publish_date""": """Publish date""",
				    """authors""": """Authors""",
				    """number_of_pages""": """Number of pages:""",
				    """first_sentence""": """First sentence""",
				    """isbn_10""": """ISBN (10)""",
				    """isbn_13""": """ISBN (13)""",
				}
				__magic_name__	:	List[Any]								={better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
				__magic_name__	:	Dict								=[
				    get_openlibrary_data(author["""key"""]   )["""name"""] for author in data["""Authors"""]
				]
				__magic_name__	:	List[str]								=data["""First sentence"""]["""value"""]
				for key, value in data.items():
								if isinstance(lowerCamelCase	,     lowerCamelCase   ):
												__magic_name__	:	Tuple								=""", """.join(lowerCamelCase   )
				return data


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							while True:
														UpperCAmelCase_		:				List[Any]									=     input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
														if isbn.lower() in ("", "q", "quit", "exit", "stop"):
																					break

														if len(isbn) not in (10, 13) or not isbn.isdigit():
																					print(F"""Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.""")
																					continue

														print(F"""\nSearching Open Library for ISBN: {isbn}...\n""")

														try:
																					UpperCAmelCase_		:				str									=     summarize_book(get_openlibrary_data(F"""isbn/{isbn}"""))
																					print("\n".join(F"""{key}: {value}""" for key, value in book_summary.items()))
														except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
																					print(F"""Sorry, there are no results for ISBN: {isbn}.""")




