



"""simple docstring"""





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Any       ,			__UpperCamelCase   :      Dict       ,			__UpperCamelCase   :      Any       ,			__UpperCamelCase   :      Dict=5     ):


						'''simple docstring'''






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


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

__lowerCAmelCase							:       str          =       '''Le camembert est <mask> :)'''






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



"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					value
          UpperCAmelCase__	:      Node | None     							=					None
          UpperCAmelCase__	:      Node | None     							=					None





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					tree



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left      ) + self.depth_first_search(node.right      )
          )



      def __iter__(   self      ):
          yield self.depth_first_search(self.tree      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

