
'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFFunnelBaseModel,
					    TFFunnelForMaskedLM,
					    TFFunnelForMultipleChoice,
					    TFFunnelForPreTraining,
					    TFFunnelForQuestionAnswering,
					    TFFunnelForSequenceClassification,
					    TFFunnelForTokenClassification,
					    TFFunnelModel,
					)


class 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=13					,     lowerCAmelCase_=7					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=99					,     lowerCAmelCase_=[1, 1, 2]					,     lowerCAmelCase_=1					,     lowerCAmelCase_=32					,     lowerCAmelCase_=4					,     lowerCAmelCase_=8					,     lowerCAmelCase_=37					,     lowerCAmelCase_="gelu_new"					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=0.0					,     lowerCAmelCase_=5_12					,     lowerCAmelCase_=3					,     lowerCAmelCase_=0.02					,     lowerCAmelCase_=3					,     lowerCAmelCase_=4					,     lowerCAmelCase_=None					,     lowerCAmelCase_=False					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	seq_length
								_snake_case     		=	is_training
								_snake_case     		=	use_input_mask
								_snake_case     		=	use_token_type_ids
								_snake_case     		=	use_labels
								_snake_case     		=	vocab_size
								_snake_case     		=	block_sizes
								_snake_case     		=	num_decoder_layers
								_snake_case     		=	d_model
								_snake_case     		=	n_head
								_snake_case     		=	d_head
								_snake_case     		=	d_inner
								_snake_case     		=	hidden_act
								_snake_case     		=	hidden_dropout
								_snake_case     		=	attention_dropout
								_snake_case     		=	activation_dropout
								_snake_case     		=	max_position_embeddings
								_snake_case     		=	type_vocab_size
								_snake_case     		=	2
								_snake_case     		=	num_labels
								_snake_case     		=	num_choices
								_snake_case     		=	scope
								_snake_case     		=	initializer_std

								# Used in the tests to check the size of the first attention layer
								_snake_case     		=	n_head
								# Used in the tests to check the size of the first hidden state
								_snake_case     		=	self.d_model
								# Used in the tests to check the number of output hidden states/attentions
								_snake_case     		=	sum(self.block_sizes						) + (0 if base else self.num_decoder_layers)
								# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
								# the last hidden state of the first block (which is the first hidden state of the decoder).
								if not base:
									_snake_case     		=	self.num_hidden_layers + 2


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	ids_tensor([self.batch_size, self.seq_length]					,     self.vocab_size						)

								_snake_case     		=	None
								if self.use_input_mask:
									_snake_case     		=	random_attention_mask([self.batch_size, self.seq_length]						)

								_snake_case     		=	None
								if self.use_token_type_ids:
									_snake_case     		=	ids_tensor([self.batch_size, self.seq_length]					,     self.type_vocab_size						)

								_snake_case     		=	None
								_snake_case     		=	None
								_snake_case     		=	None
								if self.use_labels:
									_snake_case     		=	ids_tensor([self.batch_size]					,     self.type_sequence_label_size						)
									_snake_case     		=	ids_tensor([self.batch_size, self.seq_length]					,     self.num_labels						)
									_snake_case     		=	ids_tensor([self.batch_size]					,     self.num_choices						)

								_snake_case     		=	FunnelConfig(
								    vocab_size=self.vocab_size					,     block_sizes=self.block_sizes					,     num_decoder_layers=self.num_decoder_layers					,     d_model=self.d_model					,     n_head=self.n_head					,     d_head=self.d_head					,     d_inner=self.d_inner					,     hidden_act=self.hidden_act					,     hidden_dropout=self.hidden_dropout					,     attention_dropout=self.attention_dropout					,     activation_dropout=self.activation_dropout					,     max_position_embeddings=self.max_position_embeddings					,     type_vocab_size=self.type_vocab_size					,     initializer_std=self.initializer_std					,     )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_mask,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	TFFunnelModel(config=lowerCAmelCase_						)
								_snake_case     		=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
								_snake_case     		=	model(lowerCAmelCase_						)

								_snake_case     		=	[input_ids, input_mask]
								_snake_case     		=	model(lowerCAmelCase_						)

								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.d_model)						)

								_snake_case     		=	False
								_snake_case     		=	TFFunnelModel(config=lowerCAmelCase_						)
								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.d_model)						)

								_snake_case     		=	False
								_snake_case     		=	TFFunnelModel(config=lowerCAmelCase_						)
								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.d_model)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	TFFunnelBaseModel(config=lowerCAmelCase_						)
								_snake_case     		=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
								_snake_case     		=	model(lowerCAmelCase_						)

								_snake_case     		=	[input_ids, input_mask]
								_snake_case     		=	model(lowerCAmelCase_						)

								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, 2, self.d_model)						)

								_snake_case     		=	False
								_snake_case     		=	TFFunnelBaseModel(config=lowerCAmelCase_						)
								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, 3, self.d_model)						)

								_snake_case     		=	False
								_snake_case     		=	TFFunnelBaseModel(config=lowerCAmelCase_						)
								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, 2, self.d_model)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	TFFunnelForPreTraining(config=lowerCAmelCase_						)
								_snake_case     		=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	TFFunnelForMaskedLM(config=lowerCAmelCase_						)
								_snake_case     		=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.vocab_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	self.num_labels
								_snake_case     		=	TFFunnelForSequenceClassification(config=lowerCAmelCase_						)
								_snake_case     		=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_labels)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	self.num_choices
								_snake_case     		=	TFFunnelForMultipleChoice(config=lowerCAmelCase_						)
								_snake_case     		=	tf.tile(tf.expand_dims(lowerCAmelCase_					,     1						)					,     (1, self.num_choices, 1)						)
								_snake_case     		=	tf.tile(tf.expand_dims(lowerCAmelCase_					,     1						)					,     (1, self.num_choices, 1)						)
								_snake_case     		=	tf.tile(tf.expand_dims(lowerCAmelCase_					,     1						)					,     (1, self.num_choices, 1)						)
								_snake_case     		=	{
								    'input_ids': multiple_choice_inputs_ids,
								    'attention_mask': multiple_choice_input_mask,
								    'token_type_ids': multiple_choice_token_type_ids,
								}
								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_choices)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	self.num_labels
								_snake_case     		=	TFFunnelForTokenClassification(config=lowerCAmelCase_						)
								_snake_case     		=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.num_labels)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	TFFunnelForQuestionAnswering(config=lowerCAmelCase_						)
								_snake_case     		=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(result.start_logits.shape					,     (self.batch_size, self.seq_length)						)
								self.parent.assertEqual(result.end_logits.shape					,     (self.batch_size, self.seq_length)						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.prepare_config_and_inputs()
								(
								    (
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				
								)     		=	config_and_inputs
								_snake_case     		=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
								return config, inputs_dict


@require_tf
class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= (
							    (
							        TFFunnelModel,
							        TFFunnelForMaskedLM,
							        TFFunnelForPreTraining,
							        TFFunnelForQuestionAnswering,
							        TFFunnelForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							__lowercase   	= (
							    {
							        """feature-extraction""": (TFFunnelBaseModel, TFFunnelModel),
							        """fill-mask""": TFFunnelForMaskedLM,
							        """question-answering""": TFFunnelForQuestionAnswering,
							        """text-classification""": TFFunnelForSequenceClassification,
							        """token-classification""": TFFunnelForTokenClassification,
							        """zero-shot""": TFFunnelForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							__lowercase   	= False
							__lowercase   	= False


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	TFFunnelModelTester(self						)
								_snake_case     		=	ConfigTester(self					,     config_class=lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								self.config_tester.run_common_tests()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase_						)


@require_tf
class 		__UpperCAmelCase   (      _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= (
							    (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
							)
							__lowercase   	= False
							__lowercase   	= False


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	TFFunnelModelTester(self					,     base=lowerCAmelCase_						)
								_snake_case     		=	ConfigTester(self					,     config_class=lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								self.config_tester.run_common_tests()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_base_model(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase_						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase_						)



'''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))

