


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Union[str, Any]=5      ):
      """simple docstring"""




      assert masked_input.count("""<mask>"""      ) == 1
      __lowerCamelCase     :      int       						=						torch.tensor(tokenizer.encode(UpperCAmelCase						,	add_special_tokens=UpperCAmelCase      )      ).unsqueeze(0      )  # Batch size 1
      __lowerCamelCase     :      Optional[int]       						=						model(UpperCAmelCase      )[0]  # The last hidden-state is the first element of the output tuple
      __lowerCamelCase     :      List[Any]       						=						(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
      __lowerCamelCase     :      List[str]       						=						logits[0, masked_index, :]
      __lowerCamelCase     :      Optional[Any]       						=						logits.softmax(dim=0      )
      __lowerCamelCase						, __lowerCamelCase     :      str       						=						prob.topk(k=UpperCAmelCase						,	dim=0      )
      __lowerCamelCase     :      Optional[int]       						=						""" """.join(
          [tokenizer.convert_ids_to_tokens(indices[i].item()      ) for i in range(len(UpperCAmelCase      )      )]      )
      __lowerCamelCase     :      int       						=						tokenizer.mask_token
      __lowerCamelCase     :      List[Any]       						=						[]
      for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """      )      ):
            __lowerCamelCase     :      Tuple       						=						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


__UpperCamelCase    :						Any     =							CamembertTokenizer.from_pretrained('camembert-base')
__UpperCamelCase    :						List[str]     =							CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

__UpperCamelCase    :						Optional[Any]     =							'Le camembert est <mask> :)'




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







from typing import TYPE_CHECKING

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


__UpperCamelCase    :						Union[str, Any]     =							{
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[str]     =							['LayoutLMv2TokenizerFast']

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[str]     =							['LayoutLMv2FeatureExtractor']
    __UpperCamelCase    :						Union[str, Any]     =							['LayoutLMv2ImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						Dict     =							[
        'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'LayoutLMv2ForQuestionAnswering',
        'LayoutLMv2ForSequenceClassification',
        'LayoutLMv2ForTokenClassification',
        'LayoutLMv2Layer',
        'LayoutLMv2Model',
        'LayoutLMv2PreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
    from .processing_layoutlmva import LayoutLMvaProcessor
    from .tokenization_layoutlmva import LayoutLMvaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlmva import (
            LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMvaForQuestionAnswering,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaLayer,
            LayoutLMvaModel,
            LayoutLMvaPreTrainedModel,
        )
else:
    import sys

    __UpperCamelCase    :						Optional[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



