
'''simple docstring'''





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase        =  {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  ['''DeiTFeatureExtractor''']
		lowercase        =  ['''DeiTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DeiTForImageClassification''',
		    '''DeiTForImageClassificationWithTeacher''',
		    '''DeiTForMaskedImageModeling''',
		    '''DeiTModel''',
		    '''DeiTPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDeiTForImageClassification''',
		    '''TFDeiTForImageClassificationWithTeacher''',
		    '''TFDeiTForMaskedImageModeling''',
		    '''TFDeiTModel''',
		    '''TFDeiTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

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





