
'''simple docstring'''







from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

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


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    LayoutLMvaConfig,
					    TFLayoutLMvaForQuestionAnswering,
					    TFLayoutLMvaForSequenceClassification,
					    TFLayoutLMvaForTokenClassification,
					    TFLayoutLMvaModel,
					)

if is_vision_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor


class 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=2					,     lowerCAmelCase_=3					,     lowerCAmelCase_=4					,     lowerCAmelCase_=2					,     lowerCAmelCase_=7					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=99					,     lowerCAmelCase_=36					,     lowerCAmelCase_=2					,     lowerCAmelCase_=4					,     lowerCAmelCase_=37					,     lowerCAmelCase_="gelu"					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=5_12					,     lowerCAmelCase_=16					,     lowerCAmelCase_=2					,     lowerCAmelCase_=0.02					,     lowerCAmelCase_=6					,     lowerCAmelCase_=6					,     lowerCAmelCase_=3					,     lowerCAmelCase_=4					,     lowerCAmelCase_=None					,     lowerCAmelCase_=10_00					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	num_channels
								_snake_case     		=	image_size
								_snake_case     		=	patch_size
								_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     		=	hidden_size
								_snake_case     		=	num_hidden_layers
								_snake_case     		=	num_attention_heads
								_snake_case     		=	intermediate_size
								_snake_case     		=	hidden_act
								_snake_case     		=	hidden_dropout_prob
								_snake_case     		=	attention_probs_dropout_prob
								_snake_case     		=	max_position_embeddings
								_snake_case     		=	type_vocab_size
								_snake_case     		=	type_sequence_label_size
								_snake_case     		=	initializer_range
								_snake_case     		=	coordinate_size
								_snake_case     		=	shape_size
								_snake_case     		=	num_labels
								_snake_case     		=	num_choices
								_snake_case     		=	scope
								_snake_case     		=	range_bbox

								# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
								_snake_case     		=	text_seq_length
								_snake_case     		=	(image_size // patch_size) ** 2 + 1
								_snake_case     		=	self.text_seq_length + self.image_seq_length


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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

								_snake_case     		=	ids_tensor([self.batch_size, self.text_seq_length, 4]					,     self.range_bbox						)
								_snake_case     		=	bbox.numpy()
								# Ensure that bbox is legal
								for i in range(bbox.shape[0]						):
									for j in range(bbox.shape[1]						):
										if bbox[i, j, 3] < bbox[i, j, 1]:
											_snake_case     		=	bbox[i, j, 3]
											_snake_case     		=	bbox[i, j, 1]
											_snake_case     		=	tmp_coordinate
										if bbox[i, j, 2] < bbox[i, j, 0]:
											_snake_case     		=	bbox[i, j, 2]
											_snake_case     		=	bbox[i, j, 0]
											_snake_case     		=	tmp_coordinate
								_snake_case     		=	tf.constant(lowerCAmelCase_						)

								_snake_case     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

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

								_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.text_seq_length]					,     self.num_labels						)

								_snake_case     		=	LayoutLMvaConfig(
								    vocab_size=self.vocab_size					,     hidden_size=self.hidden_size					,     num_hidden_layers=self.num_hidden_layers					,     num_attention_heads=self.num_attention_heads					,     intermediate_size=self.intermediate_size					,     hidden_act=self.hidden_act					,     hidden_dropout_prob=self.hidden_dropout_prob					,     attention_probs_dropout_prob=self.attention_probs_dropout_prob					,     max_position_embeddings=self.max_position_embeddings					,     type_vocab_size=self.type_vocab_size					,     initializer_range=self.initializer_range					,     coordinate_size=self.coordinate_size					,     shape_size=self.shape_size					,     input_size=self.image_size					,     patch_size=self.patch_size					,     )

								return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels


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





								"""simple docstring"""






								_snake_case     		=	TFLayoutLMvaModel(config=lowerCAmelCase_						)

								# text + image
								_snake_case     		=	model(lowerCAmelCase_					,     pixel_values=lowerCAmelCase_					,     training=lowerCAmelCase_						)
								_snake_case     		=	model(
								    lowerCAmelCase_					,     bbox=lowerCAmelCase_					,     pixel_values=lowerCAmelCase_					,     attention_mask=lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     training=lowerCAmelCase_					,     )
								_snake_case     		=	model(lowerCAmelCase_					,     bbox=lowerCAmelCase_					,     pixel_values=lowerCAmelCase_					,     training=lowerCAmelCase_						)

								self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)						)

								# text only
								_snake_case     		=	model(lowerCAmelCase_					,     training=lowerCAmelCase_						)
								self.parent.assertEqual(
								    result.last_hidden_state.shape					,     (self.batch_size, self.text_seq_length, self.hidden_size)						)

								# image only
								_snake_case     		=	model({'pixel_values': pixel_values}					,     training=lowerCAmelCase_						)
								self.parent.assertEqual(
								    result.last_hidden_state.shape					,     (self.batch_size, self.image_seq_length, self.hidden_size)						)


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





								"""simple docstring"""






								_snake_case     		=	self.num_labels
								_snake_case     		=	TFLayoutLMvaForSequenceClassification(config=lowerCAmelCase_						)
								_snake_case     		=	model(
								    lowerCAmelCase_					,     bbox=lowerCAmelCase_					,     pixel_values=lowerCAmelCase_					,     attention_mask=lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     labels=lowerCAmelCase_					,     training=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_labels
								_snake_case     		=	TFLayoutLMvaForTokenClassification(config=lowerCAmelCase_						)
								_snake_case     		=	model(
								    lowerCAmelCase_					,     bbox=lowerCAmelCase_					,     pixel_values=lowerCAmelCase_					,     attention_mask=lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     labels=lowerCAmelCase_					,     training=lowerCAmelCase_					,     )
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.text_seq_length, self.num_labels)						)


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





								"""simple docstring"""






								_snake_case     		=	2
								_snake_case     		=	TFLayoutLMvaForQuestionAnswering(config=lowerCAmelCase_						)
								_snake_case     		=	model(
								    lowerCAmelCase_					,     bbox=lowerCAmelCase_					,     pixel_values=lowerCAmelCase_					,     attention_mask=lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     start_positions=lowerCAmelCase_					,     end_positions=lowerCAmelCase_					,     training=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)		,				(_snake_case))     		=	config_and_inputs
								_snake_case     		=	{
								    'input_ids': input_ids,
								    'bbox': bbox,
								    'pixel_values': pixel_values,
								    'token_type_ids': token_type_ids,
								    'attention_mask': input_mask,
								}
								return config, inputs_dict


@require_tf
class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= (
							    (
							        TFLayoutLMvaModel,
							        TFLayoutLMvaForQuestionAnswering,
							        TFLayoutLMvaForSequenceClassification,
							        TFLayoutLMvaForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							__lowercase   	= (
							    {"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
							    if is_tf_available()
							    else {}
							)

							__lowercase   	= False
							__lowercase   	= False
							__lowercase   	= False


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





								"""simple docstring"""






								return True


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





								"""simple docstring"""






								_snake_case     		=	copy.deepcopy(lowerCAmelCase_						)

								if model_class in get_values(lowerCAmelCase_						):
									_snake_case     		=	{
									    k: tf.tile(tf.expand_dims(lowerCAmelCase_					,     1						)					,     (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)						)
									    if isinstance(lowerCAmelCase_					,     tf.Tensor						) and v.ndim > 0
									    else v
									    for k, v in inputs_dict.items()
									}

								if return_labels:
									if model_class in get_values(lowerCAmelCase_						):
										_snake_case     		=	tf.ones(self.model_tester.batch_size					,     dtype=tf.intaa						)
									elif model_class in get_values(lowerCAmelCase_						):
										_snake_case     		=	tf.zeros(self.model_tester.batch_size					,     dtype=tf.intaa						)
										_snake_case     		=	tf.zeros(self.model_tester.batch_size					,     dtype=tf.intaa						)
									elif model_class in get_values(lowerCAmelCase_						):
										_snake_case     		=	tf.zeros(self.model_tester.batch_size					,     dtype=tf.intaa						)
									elif model_class in get_values(lowerCAmelCase_						):
										_snake_case     		=	tf.zeros(
										    (self.model_tester.batch_size, self.model_tester.text_seq_length)					,     dtype=tf.intaa						)

								return inputs_dict


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	TFLayoutLMvaModelTester(self						)
								_snake_case     		=	ConfigTester(self					,     config_class=lowerCAmelCase_					,     hidden_size=37						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								self.config_tester.run_common_tests()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case		,				_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
									_snake_case     		=	model_class(lowerCAmelCase_						)
									if getattr(lowerCAmelCase_					,     'hf_compute_loss'					,     lowerCAmelCase_						):
										# The number of elements in the loss should be the same as the number of elements in the label
										_snake_case     		=	self._prepare_for_class(inputs_dict.copy()					,     lowerCAmelCase_					,     return_labels=lowerCAmelCase_						)
										_snake_case     		=	prepared_for_class[
										    sorted(prepared_for_class.keys() - inputs_dict.keys()					,     reverse=lowerCAmelCase_						)[0]
										]
										_snake_case     		=	added_label.shape.as_list()[:1]

										# Test that model correctly compute the loss with kwargs
										_snake_case     		=	self._prepare_for_class(inputs_dict.copy()					,     lowerCAmelCase_					,     return_labels=lowerCAmelCase_						)
										_snake_case     		=	prepared_for_class.pop('input_ids'						)

										_snake_case     		=	model(lowerCAmelCase_					,     **lowerCAmelCase_						)[0]
										self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]						)

										# Test that model correctly compute the loss when we mask some positions
										_snake_case     		=	self._prepare_for_class(inputs_dict.copy()					,     lowerCAmelCase_					,     return_labels=lowerCAmelCase_						)
										_snake_case     		=	prepared_for_class.pop('input_ids'						)
										if "labels" in prepared_for_class:
											_snake_case     		=	prepared_for_class['labels'].numpy()
											if len(labels.shape						) > 1 and labels.shape[1] != 1:
												_snake_case     		=	-1_00
												_snake_case     		=	tf.convert_to_tensor(lowerCAmelCase_						)
												_snake_case     		=	model(lowerCAmelCase_					,     **lowerCAmelCase_						)[0]
												self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]						)
												self.assertTrue(not np.any(np.isnan(loss.numpy()						)						)						)

                # Test that model correctly compute the loss with a dict
										_snake_case     		=	self._prepare_for_class(inputs_dict.copy()					,     lowerCAmelCase_					,     return_labels=lowerCAmelCase_						)
										_snake_case     		=	model(lowerCAmelCase_						)[0]
										self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]						)

										# Test that model correctly compute the loss with a tuple
										_snake_case     		=	self._prepare_for_class(inputs_dict.copy()					,     lowerCAmelCase_					,     return_labels=lowerCAmelCase_						)

										# Get keys that were added with the _prepare_for_class function
										_snake_case     		=	prepared_for_class.keys() - inputs_dict.keys()
										_snake_case     		=	inspect.signature(model.call						).parameters
										_snake_case     		=	list(signature.keys()						)

										# Create a dictionary holding the location of the tensors in the tuple
										_snake_case     		=	{0: 'input_ids'}
										for label_key in label_keys:
											_snake_case     		=	signature_names.index(lowerCAmelCase_						)
											_snake_case     		=	label_key
										_snake_case     		=	sorted(tuple_index_mapping.items()						)
										# Initialize a list with their default values, update the values and convert to a tuple
										_snake_case     		=	[]

										for name in signature_names:
											if name != "kwargs":
												list_input.append(signature[name].default						)

										for index, value in sorted_tuple_index_mapping:
											_snake_case     		=	prepared_for_class[value]

										_snake_case     		=	tuple(lowerCAmelCase_						)

										# Send to model
										_snake_case     		=	model(tuple_input[:-1]						)[0]

										self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								(
								    (
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				
								)     		=	self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
									_snake_case     		=	type
									self.model_tester.create_and_check_model(lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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




							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_snake_case     		=	TFLayoutLMvaModel.from_pretrained(lowerCAmelCase_						)
									self.assertIsNotNone(lowerCAmelCase_						)







def        SCREAMING_SNAKE_CASE__			(     ) ->			int:
	_snake_case     		=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
	return image





@require_tf
class 		__UpperCAmelCase   (      unittest.TestCase		):


							@cached_property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase_						) if is_vision_available() else None




							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'						)

								_snake_case     		=	self.default_image_processor
								_snake_case     		=	prepare_img()
								_snake_case     		=	image_processor(images=lowerCAmelCase_					,     return_tensors='tf'						).pixel_values

								_snake_case     		=	tf.constant([[1, 2]]						)
								_snake_case     		=	tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]						)					,     axis=0						)

								# forward pass
								_snake_case     		=	model(input_ids=lowerCAmelCase_					,     bbox=lowerCAmelCase_					,     pixel_values=lowerCAmelCase_					,     training=lowerCAmelCase_						)

								# verify the logits
								_snake_case     		=	(1, 1_99, 7_68)
								self.assertEqual(outputs.last_hidden_state.shape					,     lowerCAmelCase_						)

								_snake_case     		=	tf.constant(
								    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]						)

								self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]					,     lowerCAmelCase_					,     atol=1E-4						)						)



'''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))

