

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

import requests

def 						UpperCamelCase (lowercase_:			str = "isbn/0140328726" )					->				dict:
	A__							:   Tuple					       =		olid.strip().strip("""/""" )  # Remove leading/trailing whitespace & slashes
	if new_olid.count("""/""" ) != 1:
		A__							:   Optional[int]					       =		f"""{olid} is not a valid Open Library olid"""
		raise ValueError(lowercase__ )
	return requests.get(f"""https://openlibrary.org/{new_olid}.json""" ).json()

def 						UpperCamelCase (lowercase_:			dict )					->				dict:
	A__							:   Optional[int]					       =		{
	    """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)""",
	}
	A__							:   Any					       =		{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
	A__							:   int					       =		[
	    get_openlibrary_data(author["""key"""] )["""name"""] for author in data["""Authors"""]
	]
	A__							:   Union[str, Any]					       =		data["""First sentence"""]["""value"""]
	for key, value in data.items():
		if isinstance(lowercase__   ,				lowercase__ ):
			A__							:   int					       =		""", """.join(lowercase__ )
	return data


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				while True:
								A_	:			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:
												A_	:			List[Any]  	=      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}.''')






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	'''MCTCTFeatureExtractor'''
					UpperCAmelCase__:       Optional[int]  							=	'''AutoTokenizer'''






					def __init__(			self   ,	A__   ,	A__      ):
						super().__init__(A__   ,	A__      )
						A__							:   List[str]					       =		self.feature_extractor
						A__							:   Optional[int]					       =		False






					def __call__(			self   ,	*A__   ,	**A__      ):
						# For backward compatibility
						if self._in_target_context_manager:
							return self.current_processor(*A__   ,	**A__      )

						if "raw_speech" in kwargs:
							warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""      )
							A__							:   Dict					       =		kwargs.pop("""raw_speech"""      )
						else:
							A__							:   Tuple					       =		kwargs.pop("""audio"""   ,	A__      )
						A__							:   Union[str, Any]					       =		kwargs.pop("""sampling_rate"""   ,	A__      )
						A__							:   int					       =		kwargs.pop("""text"""   ,	A__      )
						if len(A__      ) > 0:
							A__							:   Optional[int]					       =		args[0]
							A__							:   Dict					       =		args[1:]

						if audio is None and text is None:
							raise ValueError("""You need to specify either an `audio` or `text` input to process."""      )

						if audio is not None:
							A__							:   List[str]					       =		self.feature_extractor(A__   ,	*A__   ,	sampling_rate=A__   ,	**A__      )
						if text is not None:
							A__							:   Optional[Any]					       =		self.tokenizer(A__   ,	**A__      )

						if text is None:
							return inputs
						elif audio is None:
							return encodings
						else:
							A__							:   List[Any]					       =		encodings["""input_ids"""]
							return inputs






					def   __A      (			self   ,	*A__   ,	**A__      ):
						return self.tokenizer.batch_decode(*A__   ,	**A__      )






					def   __A      (			self   ,	*A__   ,	**A__      ):
						# For backward compatibility
						if self._in_target_context_manager:
							return self.current_processor.pad(*A__   ,	**A__      )

						A__							:   Optional[Any]					       =		kwargs.pop("""input_features"""   ,	A__      )
						A__							:   Union[str, Any]					       =		kwargs.pop("""labels"""   ,	A__      )
						if len(A__      ) > 0:
							A__							:   List[Any]					       =		args[0]
							A__							:   Optional[int]					       =		args[1:]

						if input_features is not None:
							A__							:   Union[str, Any]					       =		self.feature_extractor.pad(A__   ,	*A__   ,	**A__      )
						if labels is not None:
							A__							:   List[Any]					       =		self.tokenizer.pad(A__   ,	**A__      )

						if labels is None:
							return input_features
						elif input_features is None:
							return labels
						else:
							A__							:   Dict					       =		labels["""input_ids"""]
							return input_features






					def   __A      (			self   ,	*A__   ,	**A__      ):
						return self.tokenizer.decode(*A__   ,	**A__      )






					@contextmanager
					def   __A      (			self      ):
						warnings.warn(
						    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
						    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
						    """your audio inputs, or in a separate call."""      )
						A__							:   int					       =		True
						A__							:   List[Any]					       =		self.tokenizer
						yield
						A__							:   Tuple					       =		self.feature_extractor
						A__							:   Dict					       =		False



