



'''simple docstring'''





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

import requests





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





def   a_   (		_UpperCAmelCase    :      dict				)    ->  dict:
      __snake_case :   List[Any]													=    {
          '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)',
      }
      __snake_case :   str													=    {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
      __snake_case :   Optional[Any]													=    [
          get_openlibrary_data(author['key']				)['name'] for author in data['Authors']
      ]
      __snake_case :   str													=    data['First sentence']['value']
      for key, value in data.items():
            if isinstance(_UpperCAmelCase ,_UpperCAmelCase				):
                  __snake_case :   Optional[Any]													=    ', '.join(_UpperCAmelCase				)
      return data


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    while True:
        A__    :   Dict           =					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 (1_0, 1_3) 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__    :   Optional[int]           =					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  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= ['''image_processor''', '''tokenizer''']
 A__				= '''CLIPImageProcessor'''
 A__				= ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
 def __init__(   self						:							Any      ,     __a						:							str=None      ,     __a						:							List[Any]=None      ,     **__a						:							List[str]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'      ,     __a      ,     )
             __snake_case :   List[str]													=    kwargs.pop('feature_extractor'  )

       __snake_case :   List[Any]													=    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__(__a      ,     __a  )
 def __call__(   self						:							List[Any]      ,     __a						:							Optional[int]=None      ,     __a						:							Optional[int]=None      ,     __a						:							Union[str, Any]=None      ,     **__a						:							Union[str, Any]  )      ->   List[Any]:







       '''simple docstring'''

       if text is None and images is None:
             raise ValueError('You have to specify either text or images. Both cannot be none.'  )

       if text is not None:
             __snake_case :   Any													=    self.tokenizer(__a      ,     return_tensors=__a      ,     **__a  )

       if images is not None:
             __snake_case :   str													=    self.image_processor(__a      ,     return_tensors=__a      ,     **__a  )

       if text is not None and images is not None:
             __snake_case :   Optional[Any]													=    image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**__a  )      ,     tensor_type=__a  )
 def A_   (   self						:							List[Any]      ,     *__a						:							Dict      ,     **__a						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.tokenizer.batch_decode(*__a      ,     **__a  )
 def A_   (   self						:							str      ,     *__a						:							Tuple      ,     **__a						:							List[str]  )      ->   int:







       '''simple docstring'''
       return self.tokenizer.decode(*__a      ,     **__a  )
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Tuple													=    self.tokenizer.model_input_names
       __snake_case :   Tuple													=    self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
 @property
 def A_   (   self						:							Any  )      ->   Any:







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





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







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



