
"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def       _snake_case				(		snake_case__   :     Tuple	,      snake_case__   :     int	,      snake_case__   :     List[str]	,      snake_case__   :     Any=5     ):
	# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
	assert masked_input.count('<mask>'     ) == 1
	A						   =   torch.tensor(tokenizer.encode(snake_case__	,      add_special_tokens=snake_case__     )     ).unsqueeze(0     )  # Batch size 1
	A						   =   model(snake_case__     )[0]  # The last hidden-state is the first element of the output tuple
	A						   =   (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	A						   =   logits[0, masked_index, :]
	A						   =   logits.softmax(dim=0     )
	A   ,      A						   =   prob.topk(k=snake_case__	,      dim=0     )
	A						   =   ' '.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()     ) for i in range(len(snake_case__     )     )]     )
	A						   =   tokenizer.mask_token
	A						   =   []
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' '     )     ):
		A						   =   predicted_token_bpe.replace('\u2581'	,      ' '     )
		if " {0}".format(snake_case__     ) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(' {0}'.format(snake_case__     )	,      snake_case__     ),
			        values[index].item(),
			        predicted_token,
			    )     )
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(snake_case__	,      snake_case__     ),
			        values[index].item(),
			        predicted_token,
			    )     )
	return topk_filled_outputs


_lowercase							   =				CamembertTokenizer.from_pretrained('''camembert-base''')
_lowercase							   =				CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

_lowercase							   =				'''Le camembert est <mask> :)'''


print(fill_mask(masked_input, model, tokenizer, topk=3))

"""simple docstring"""


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

import requests






def       _snake_case				(		snake_case__   :     str = "isbn/0140328726"     ):
	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(snake_case__     )
	return requests.get(F'https://openlibrary.org/{new_olid}.json'     ).json()






def       _snake_case				(		snake_case__   :     dict     ):
	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(snake_case__	,      snake_case__     ):
			A						   =   ', '.join(snake_case__     )
	return data


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		while True:
				_lowercase							   =				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:
						_lowercase							   =				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}.""")