
"""simple docstring"""




from typing import TYPE_CHECKING

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


lowerCAmelCase__				=					{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__				=					['RobertaTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__				=					[
						    'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RobertaForCausalLM',
						    'RobertaForMaskedLM',
						    'RobertaForMultipleChoice',
						    'RobertaForQuestionAnswering',
						    'RobertaForSequenceClassification',
						    'RobertaForTokenClassification',
						    'RobertaModel',
						    'RobertaPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__				=					[
						    'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFRobertaForCausalLM',
						    'TFRobertaForMaskedLM',
						    'TFRobertaForMultipleChoice',
						    'TFRobertaForQuestionAnswering',
						    'TFRobertaForSequenceClassification',
						    'TFRobertaForTokenClassification',
						    'TFRobertaMainLayer',
						    'TFRobertaModel',
						    'TFRobertaPreTrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__				=					[
						    'FlaxRobertaForCausalLM',
						    'FlaxRobertaForMaskedLM',
						    'FlaxRobertaForMultipleChoice',
						    'FlaxRobertaForQuestionAnswering',
						    'FlaxRobertaForSequenceClassification',
						    'FlaxRobertaForTokenClassification',
						    'FlaxRobertaModel',
						    'FlaxRobertaPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
						from .tokenization_roberta import RobertaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roberta_fast import RobertaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roberta import (
												    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RobertaForCausalLM,
												    RobertaForMaskedLM,
												    RobertaForMultipleChoice,
												    RobertaForQuestionAnswering,
												    RobertaForSequenceClassification,
												    RobertaForTokenClassification,
												    RobertaModel,
												    RobertaPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roberta import (
												    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRobertaForCausalLM,
												    TFRobertaForMaskedLM,
												    TFRobertaForMultipleChoice,
												    TFRobertaForQuestionAnswering,
												    TFRobertaForSequenceClassification,
												    TFRobertaForTokenClassification,
												    TFRobertaMainLayer,
												    TFRobertaModel,
												    TFRobertaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roberta import (
												    FlaxRobertaForCausalLM,
												    FlaxRobertaForMaskedLM,
												    FlaxRobertaForMultipleChoice,
												    FlaxRobertaForQuestionAnswering,
												    FlaxRobertaForSequenceClassification,
												    FlaxRobertaForTokenClassification,
												    FlaxRobertaModel,
												    FlaxRobertaPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase__				=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

"""simple docstring"""




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

import requests



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



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_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)''',
		}
		SCREAMING_SNAKE_CASE_    			=			{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
		SCREAMING_SNAKE_CASE_    			=			[
		    get_openlibrary_data(author['''key''']     )['''name'''] for author in data['''Authors''']
		]
		SCREAMING_SNAKE_CASE_    			=			data['''First sentence''']['''value''']
		for key, value in data.items():
				if isinstance(__a,				__a     ):
						SCREAMING_SNAKE_CASE_    			=			''', '''.join(__a     )
		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}.''')