


'''simple docstring'''



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

import requests


def 				lowerCamelCase__				(	A       :      str = "isbn/0140328726"					):




						'''simple docstring'''






						UpperCAmelCase					    =     olid.strip().strip('''/'''					)  # Remove leading/trailing whitespace & slashes
						if new_olid.count('''/'''					) != 1:
												UpperCAmelCase					    =     f"""{olid} is not a valid Open Library olid"""
												raise ValueError(A					)
						return requests.get(f"""https://openlibrary.org/{new_olid}.json"""					).json()


def 				lowerCamelCase__				(	A       :      dict					):




						'''simple docstring'''






						UpperCAmelCase					    =     {
						    '''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)''',
						}
						UpperCAmelCase					    =     {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
						UpperCAmelCase					    =     [
						    get_openlibrary_data(author['''key''']					)['''name'''] for author in data['''Authors''']
						]
						UpperCAmelCase					    =     data['''First sentence''']['''value''']
						for key, value in data.items():
												if isinstance(A				,       A					):
																		UpperCAmelCase					    =     ''', '''.join(A					)
						return data


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	while True:
		_lowercase      :      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:
			_lowercase      :      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}.""")









'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				List[Any]  		=	["image_processor", "tokenizer"]
				__magic_name__    :				Tuple  		=	"ViTImageProcessor"
				__magic_name__    :				int  		=	("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(		self      :			List[str]				,   lowerCAmelCase      :			Tuple=None				,   lowerCAmelCase      :			List[str]=None				,   **lowerCAmelCase      :			Optional[int]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''				,   lowerCAmelCase				,   )
																UpperCAmelCase					    =     kwargs.pop('''feature_extractor'''				)

										UpperCAmelCase					    =     image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
																raise ValueError('''You need to specify an `image_processor`.'''				)
										if tokenizer is None:
																raise ValueError('''You need to specify a `tokenizer`.'''				)

										super().__init__(lowerCAmelCase				,   lowerCAmelCase				)




				def __call__(		self      :			int				,   lowerCAmelCase      :			Dict=None				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			Union[str, Any]=None				,   lowerCAmelCase      :			int=None				,   **lowerCAmelCase      :			Tuple				)->					Optional[int]:

										"""simple docstring"""




										if text is None and visual_prompt is None and images is None:
																raise ValueError('''You have to specify either text, visual prompt or images.'''				)

										if text is not None and visual_prompt is not None:
																raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''				)

										if text is not None:
																UpperCAmelCase					    =     self.tokenizer(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if visual_prompt is not None:
																UpperCAmelCase					    =     self.image_processor(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if images is not None:
																UpperCAmelCase					    =     self.image_processor(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if visual_prompt is not None and images is not None:
																UpperCAmelCase					    =     {
																    '''pixel_values''': image_features.pixel_values,
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										elif text is not None and images is not None:
																UpperCAmelCase					    =     image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase					    =     {
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase				)				,   tensor_type=lowerCAmelCase				)




				def 					a__(		self      :			Optional[int]				,   *lowerCAmelCase      :			List[str]				,   **lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			List[Any]				,   *lowerCAmelCase      :			str				,   **lowerCAmelCase      :			List[Any]				)->					Optional[Any]:

										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase				,   **lowerCAmelCase				)




				@property
				def 					a__(		self      :			Any				)->					Optional[int]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,   lowerCAmelCase				,   )
										return self.image_processor_class





				@property
				def 					a__(		self      :			str				)->					List[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,   lowerCAmelCase				,   )
										return self.image_processor





