
'''simple docstring'''
from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE = "isbn/0140328726" ):
							_snake_case  =    olid.strip().strip("""/""" )  # Remove leading/trailing whitespace & slashes
							if new_olid.count("""/""" ) != 1:
														_snake_case  =    f"""{olid} is not a valid Open Library olid"""
														raise ValueError(__snake_case )
							return requests.get(f"""https://openlibrary.org/{new_olid}.json""" ).json()






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):
							_snake_case  =    {
							    """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  =    {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
							_snake_case  =    [
							    get_openlibrary_data(author["""key"""] )["""name"""] for author in data["""Authors"""]
							]
							_snake_case  =    data["""First sentence"""]["""value"""]
							for key, value in data.items():
														if isinstance(__snake_case				,   __snake_case ):
																					_snake_case  =    """, """.join(__snake_case )
							return data


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				while True:
								__lowerCAmelCase					=			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:
												__lowerCAmelCase					=			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 typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				["""image_processor""", """tokenizer"""]
      snake_case_	=				"""FlavaImageProcessor"""
      snake_case_	=				("""BertTokenizer""", """BertTokenizerFast""")


      def __init__(						self       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    **__UpperCamelCase						)->						Tuple:
           __lowerCAmelCase             =       None
           if "feature_extractor" in kwargs:
                warnings.warn(
                    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                    " instead."       ,    __UpperCamelCase       ,    )
                __lowerCAmelCase             =       kwargs.pop("feature_extractor"						)

           __lowerCAmelCase             =       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__(__UpperCamelCase       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       self.image_processor


      def __call__(						self       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = True       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = None       ,    __UpperCamelCase = 0       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = True       ,    __UpperCamelCase = None       ,    **__UpperCamelCase       ,    )->						int:

           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:
                __lowerCAmelCase             =       self.tokenizer(
                    text=__UpperCamelCase       ,    add_special_tokens=__UpperCamelCase       ,    padding=__UpperCamelCase       ,    truncation=__UpperCamelCase       ,    max_length=__UpperCamelCase       ,    stride=__UpperCamelCase       ,    pad_to_multiple_of=__UpperCamelCase       ,    return_token_type_ids=__UpperCamelCase       ,    return_attention_mask=__UpperCamelCase       ,    return_overflowing_tokens=__UpperCamelCase       ,    return_special_tokens_mask=__UpperCamelCase       ,    return_offsets_mapping=__UpperCamelCase       ,    return_length=__UpperCamelCase       ,    verbose=__UpperCamelCase       ,    return_tensors=__UpperCamelCase       ,    **__UpperCamelCase       ,    )
           if images is not None:
                __lowerCAmelCase             =       self.image_processor(
                    __UpperCamelCase       ,    return_image_mask=__UpperCamelCase       ,    return_codebook_pixels=__UpperCamelCase       ,    return_tensors=__UpperCamelCase       ,    **__UpperCamelCase       ,    )

           if text is not None and images is not None:
                encoding.update(__UpperCamelCase						)
                return encoding
           elif text is not None:
                return encoding
           else:
                return BatchEncoding(data=dict(**__UpperCamelCase						)       ,    tensor_type=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						int:
           return self.tokenizer.batch_decode(*__UpperCamelCase       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						Optional[int]:
           return self.tokenizer.decode(*__UpperCamelCase       ,    **__UpperCamelCase						)


      @property
      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase             =       self.tokenizer.model_input_names
           __lowerCAmelCase             =       self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)


      @property
      def 			__UpperCAmelCase		(						self						)->						Dict:
           warnings.warn(
               "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."       ,    __UpperCamelCase       ,    )
           return self.image_processor_class


      @property
      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           warnings.warn(
               "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,    __UpperCamelCase       ,    )
           return self.image_processor






