




"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=5				)						->    Union[str, Any]:
      # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
      assert masked_input.count("""<mask>"""				) == 1
      lowerCAmelCase__      :			Tuple   =	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__      :			str   =	(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
      lowerCAmelCase__      :			List[Any]   =	logits[0, masked_index, :]
      lowerCAmelCase__      :			List[Any]   =	logits.softmax(dim=0				)
      lowerCAmelCase__      :			str   =	prob.topk(k=__UpperCAmelCase    ,		dim=0				)
      lowerCAmelCase__      :			int   =	""" """.join(
          [tokenizer.convert_ids_to_tokens(indices[i].item()				) for i in range(len(__UpperCAmelCase				)				)]				)
      lowerCAmelCase__      :			Dict   =	tokenizer.mask_token
      lowerCAmelCase__      :			Dict   =	[]
      for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """				)				):
            lowerCAmelCase__      :			Optional[Any]   =	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


_A													=      CamembertTokenizer.from_pretrained("""camembert-base""")
_A													=      CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

_A													=      """Le camembert est <mask> :)"""

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











"""simple docstring"""


from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Dict     				=     ["image_processor", "tokenizer"]
   _lowerCamelCase    :Dict     				=     "BlipImageProcessor"
   _lowerCamelCase    :Any     				=     ("BertTokenizer", "BertTokenizerFast")






   def __init__(		self       :		Dict   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		Union[str, Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	False
         super().__init__(UpperCamelCase   ,     UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	self.image_processor






   def __call__(		self       :		int   ,     UpperCamelCase       :		ImageInput = None   ,     UpperCamelCase       :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Union[bool, str, PaddingStrategy] = False   ,     UpperCamelCase       :		Union[bool, str, TruncationStrategy] = None   ,     UpperCamelCase       :		Optional[int] = None   ,     UpperCamelCase       :		int = 0   ,     UpperCamelCase       :		Optional[int] = None   ,     UpperCamelCase       :		Optional[bool] = None   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Optional[Union[str, TensorType]] = None   ,     **UpperCamelCase       :		Optional[int]   ,     )			->						BatchEncoding:

         """simple docstring"""
         if images is None and text is None:
               raise ValueError("""You have to specify either images or text."""   )

         # Get only text
         if images is None:
               lowerCAmelCase__      :			Any   =	self.tokenizer
               lowerCAmelCase__      :			Optional[int]   =	self.tokenizer(
                   text=UpperCamelCase   ,     add_special_tokens=UpperCamelCase   ,     padding=UpperCamelCase   ,     truncation=UpperCamelCase   ,     max_length=UpperCamelCase   ,     stride=UpperCamelCase   ,     pad_to_multiple_of=UpperCamelCase   ,     return_attention_mask=UpperCamelCase   ,     return_overflowing_tokens=UpperCamelCase   ,     return_special_tokens_mask=UpperCamelCase   ,     return_offsets_mapping=UpperCamelCase   ,     return_token_type_ids=UpperCamelCase   ,     return_length=UpperCamelCase   ,     verbose=UpperCamelCase   ,     return_tensors=UpperCamelCase   ,     **UpperCamelCase   ,     )
               return text_encoding

         # add pixel_values
         lowerCAmelCase__      :			Tuple   =	self.image_processor(UpperCamelCase   ,     return_tensors=UpperCamelCase   )

         if text is not None:
               lowerCAmelCase__      :			Optional[int]   =	self.tokenizer(
                   text=UpperCamelCase   ,     add_special_tokens=UpperCamelCase   ,     padding=UpperCamelCase   ,     truncation=UpperCamelCase   ,     max_length=UpperCamelCase   ,     stride=UpperCamelCase   ,     pad_to_multiple_of=UpperCamelCase   ,     return_attention_mask=UpperCamelCase   ,     return_overflowing_tokens=UpperCamelCase   ,     return_special_tokens_mask=UpperCamelCase   ,     return_offsets_mapping=UpperCamelCase   ,     return_token_type_ids=UpperCamelCase   ,     return_length=UpperCamelCase   ,     verbose=UpperCamelCase   ,     return_tensors=UpperCamelCase   ,     **UpperCamelCase   ,     )
         else:
               lowerCAmelCase__      :			Tuple   =	None

         if text_encoding is not None:
               encoding_image_processor.update(UpperCamelCase   )

         return encoding_image_processor






   def 	_lowerCAmelCase   (		self       :		int   ,     *UpperCamelCase       :		Union[str, Any]   ,     **UpperCamelCase       :		List[str]   )			->						Dict:

         """simple docstring"""
         return self.tokenizer.batch_decode(*UpperCamelCase   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     *UpperCamelCase       :		Tuple   ,     **UpperCamelCase       :		List[str]   )			->						List[Any]:

         """simple docstring"""
         return self.tokenizer.decode(*UpperCamelCase   ,     **UpperCamelCase   )






   @property
   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.tokenizer.model_input_names
         lowerCAmelCase__      :			Union[str, Any]   =	self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





