





'''simple docstring'''






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

import requests
def   UpperCamelCase__ (						lowerCAmelCase = "isbn/0140328726"    ):





      """simple docstring"""


      _lowerCAmelCase					=				olid.strip().strip("""/"""    )  # Remove leading/trailing whitespace & slashes
      if new_olid.count("""/"""    ) != 1:
            _lowerCAmelCase					=				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    ):





      """simple docstring"""


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


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      while True:
            A__							:  Optional[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:
                  A__							:  List[str]    =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_sentencepiece_available,
    is_torch_available,
)


A__							:  Any    ={
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Union[str, Any]    =['''SpeechT5Tokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[Any]    =[
          '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''SpeechT5ForSpeechToText''',
          '''SpeechT5ForSpeechToSpeech''',
          '''SpeechT5ForTextToSpeech''',
          '''SpeechT5Model''',
          '''SpeechT5PreTrainedModel''',
          '''SpeechT5HifiGan''',
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      A__							:  Tuple    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






