



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

import requests



def        lowerCAmelCase_(   lowercase_     :							str = "isbn/0140328726"							)	->       dict:
				_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(lowercase_							)
				return requests.get(F"""https://openlibrary.org/{new_olid}.json"""							).json()



def        lowerCAmelCase_(   lowercase_     :							dict							)	->       dict:
				_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(lowercase_						,   lowercase_							):
												_lowerCamelCase        =				''', '''.join(lowercase_							)
				return data


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			while True:
						__SCREAMING_SNAKE_CASE  :						int    							=      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:
									__SCREAMING_SNAKE_CASE  :						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_torch_available


__SCREAMING_SNAKE_CASE  :						List[Any]    							=      {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						int    							=      [
			    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMForMultipleChoice''',
			    '''XLMForQuestionAnswering''',
			    '''XLMForQuestionAnsweringSimple''',
			    '''XLMForSequenceClassification''',
			    '''XLMForTokenClassification''',
			    '''XLMModel''',
			    '''XLMPreTrainedModel''',
			    '''XLMWithLMHeadModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      [
			    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMForMultipleChoice''',
			    '''TFXLMForQuestionAnsweringSimple''',
			    '''TFXLMForSequenceClassification''',
			    '''TFXLMForTokenClassification''',
			    '''TFXLMMainLayer''',
			    '''TFXLMModel''',
			    '''TFXLMPreTrainedModel''',
			    '''TFXLMWithLMHeadModel''',
			]


if TYPE_CHECKING:
			from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
			from .tokenization_xlm import XLMTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm import (
						    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMForMultipleChoice,
						    XLMForQuestionAnswering,
						    XLMForQuestionAnsweringSimple,
						    XLMForSequenceClassification,
						    XLMForTokenClassification,
						    XLMModel,
						    XLMPreTrainedModel,
						    XLMWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm import (
						    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMForMultipleChoice,
						    TFXLMForQuestionAnsweringSimple,
						    TFXLMForSequenceClassification,
						    TFXLMForTokenClassification,
						    TFXLMMainLayer,
						    TFXLMModel,
						    TFXLMPreTrainedModel,
						    TFXLMWithLMHeadModel,
						)

else:
			import sys

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