



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

import requests




def      __UpperCamelCase     (  _lowerCAmelCase = "isbn/0140328726"		)	->  dict:



    """simple docstring"""






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




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  dict:



    """simple docstring"""






    A			:	List[str]										=       {
        """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			:	int										=       {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
    A			:	str										=       [
        get_openlibrary_data(author["""key"""]		)["""name"""] for author in data["""Authors"""]
    ]
    A			:	Tuple										=       data["""First sentence"""]["""value"""]
    for key, value in data.items():
        if isinstance(_lowerCAmelCase				,					_lowerCAmelCase		):
            A			:	Dict										=       """, """.join(_lowerCAmelCase		)
    return data


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     while True:
          SCREAMING_SNAKE_CASE_:Union[str, 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:
               SCREAMING_SNAKE_CASE_: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}.""")







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_:Any					=						{
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:List[Any]					=						["""MobileNetV2FeatureExtractor"""]
     SCREAMING_SNAKE_CASE_:Tuple					=						["""MobileNetV2ImageProcessor"""]


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Any					=						[
         """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MobileNetV2ForImageClassification""",
         """MobileNetV2ForSemanticSegmentation""",
         """MobileNetV2Model""",
         """MobileNetV2PreTrainedModel""",
         """load_tf_weights_in_mobilenet_v2""",
     ]


if TYPE_CHECKING:
     from .configuration_mobilenet_va import (
         MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileNetVaConfig,
         MobileNetVaOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
          from .image_processing_mobilenet_va import MobileNetVaImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilenet_va import (
              MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileNetVaForImageClassification,
              MobileNetVaForSemanticSegmentation,
              MobileNetVaModel,
              MobileNetVaPreTrainedModel,
              load_tf_weights_in_mobilenet_va,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE_:Optional[Any]					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


