

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

import requests

def 	__snake_case		(      __UpperCamelCase     :      str = "isbn/0140328726"  ):



      """simple docstring"""

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

def 	__snake_case		(      __UpperCamelCase     :      Union[str, Any]  ):



      """simple docstring"""

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


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      while True:
            __a  :str										=       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  :Dict										=       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"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a      =				{
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["MobileBertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "MobileBertForMaskedLM",
						    "MobileBertForMultipleChoice",
						    "MobileBertForNextSentencePrediction",
						    "MobileBertForPreTraining",
						    "MobileBertForQuestionAnswering",
						    "MobileBertForSequenceClassification",
						    "MobileBertForTokenClassification",
						    "MobileBertLayer",
						    "MobileBertModel",
						    "MobileBertPreTrainedModel",
						    "load_tf_weights_in_mobilebert",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFMobileBertForMaskedLM",
						    "TFMobileBertForMultipleChoice",
						    "TFMobileBertForNextSentencePrediction",
						    "TFMobileBertForPreTraining",
						    "TFMobileBertForQuestionAnswering",
						    "TFMobileBertForSequenceClassification",
						    "TFMobileBertForTokenClassification",
						    "TFMobileBertMainLayer",
						    "TFMobileBertModel",
						    "TFMobileBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_mobilebert import (
						    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileBertConfig,
						    MobileBertOnnxConfig,
						)
						from .tokenization_mobilebert import MobileBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mobilebert_fast import MobileBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilebert import (
												    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileBertForMaskedLM,
												    MobileBertForMultipleChoice,
												    MobileBertForNextSentencePrediction,
												    MobileBertForPreTraining,
												    MobileBertForQuestionAnswering,
												    MobileBertForSequenceClassification,
												    MobileBertForTokenClassification,
												    MobileBertLayer,
												    MobileBertModel,
												    MobileBertPreTrainedModel,
												    load_tf_weights_in_mobilebert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilebert import (
												    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileBertForMaskedLM,
												    TFMobileBertForMultipleChoice,
												    TFMobileBertForNextSentencePrediction,
												    TFMobileBertForPreTraining,
												    TFMobileBertForQuestionAnswering,
												    TFMobileBertForSequenceClassification,
												    TFMobileBertForTokenClassification,
												    TFMobileBertMainLayer,
												    TFMobileBertModel,
												    TFMobileBertPreTrainedModel,
												)

else:
						import sys

						__a      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
