
'''simple docstring'''







import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar


class 		__UpperCAmelCase   (      unittest.TestCase		):


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	logging.get_logger()

								# the current default level is logging.WARNING
								_snake_case     		=	logging.get_verbosity()

								logging.set_verbosity_error()
								self.assertEqual(logger.getEffectiveLevel()					,     logging.get_verbosity()						)

								logging.set_verbosity_warning()
								self.assertEqual(logger.getEffectiveLevel()					,     logging.get_verbosity()						)

								logging.set_verbosity_info()
								self.assertEqual(logger.getEffectiveLevel()					,     logging.get_verbosity()						)

								logging.set_verbosity_debug()
								self.assertEqual(logger.getEffectiveLevel()					,     logging.get_verbosity()						)

								# restore to the original level
								logging.set_verbosity(lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	logging.get_verbosity()

								_snake_case     		=	logging.get_logger('transformers.models.bart.tokenization_bart'						)
								_snake_case     		=	'Testing 1, 2, 3'

								# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
								if level_origin <= logging.WARNING:
									with CaptureLogger(lowerCAmelCase_						) as cl:
										logger.warning(lowerCAmelCase_						)
									self.assertEqual(cl.out					,     msg + '\n'						)

								# this is setting the level for all of `transformers.*` loggers
								logging.set_verbosity_error()

								# should not be able to log warnings
								with CaptureLogger(lowerCAmelCase_						) as cl:
									logger.warning(lowerCAmelCase_						)
								self.assertEqual(cl.out					,     ''						)

								# should be able to log warnings again
								logging.set_verbosity_warning()
								with CaptureLogger(lowerCAmelCase_						) as cl:
									logger.warning(lowerCAmelCase_						)
								self.assertEqual(cl.out					,     msg + '\n'						)

								# restore to the original level
								logging.set_verbosity(lowerCAmelCase_						)


							@mockenv(TRANSFORMERS_VERBOSITY='error'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								transformers.utils.logging._reset_library_root_logger()
								# this action activates the env var
								_snake_case     		=	logging.get_logger('transformers.models.bart.tokenization_bart'						)

								_snake_case     		=	os.getenv('TRANSFORMERS_VERBOSITY'					,     lowerCAmelCase_						)
								_snake_case     		=	logging.log_levels[env_level_str]

								_snake_case     		=	logging.get_verbosity()
								self.assertEqual(
								    lowerCAmelCase_					,     lowerCAmelCase_					,     F'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'					,     )

								# restore to the original level
								_snake_case     		=	''
								transformers.utils.logging._reset_library_root_logger()


							@mockenv(TRANSFORMERS_VERBOSITY='super-error'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								transformers.utils.logging._reset_library_root_logger()
								_snake_case     		=	logging.logging.getLogger()
								with CaptureLogger(lowerCAmelCase_						) as cl:
									# this action activates the env var
									logging.get_logger('transformers.models.bart.tokenization_bart'						)
								self.assertIn('Unknown option TRANSFORMERS_VERBOSITY=super-error'					,     cl.out						)

								# no need to restore as nothing was changed




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								transformers.utils.logging._reset_library_root_logger()

								_snake_case     		=	logging.get_logger('transformers.models.bart.tokenization_bart'						)
								_snake_case     		=	'Testing 1, 2, 3'

								with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='1'						):
									# nothing should be logged as env var disables this method
									with CaptureLogger(lowerCAmelCase_						) as cl:
										logger.warning_advice(lowerCAmelCase_						)
									self.assertEqual(cl.out					,     ''						)

								with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''						):
									# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
									with CaptureLogger(lowerCAmelCase_						) as cl:
										logger.warning_advice(lowerCAmelCase_						)
									self.assertEqual(cl.out					,     msg + '\n'						)







def        SCREAMING_SNAKE_CASE__			(     ) ->			Optional[int]:
	disable_progress_bar()
	assert are_progress_bars_disabled()

	enable_progress_bar()
	assert not are_progress_bars_disabled()



'''simple docstring'''







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer







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


lowercase					:							str   =      CamembertTokenizer.from_pretrained("camembert-base")
lowercase					:							str   =      CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowercase					:							Tuple   =      "Le camembert est <mask> :)"





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

