

import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer

def 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=5    ):




			'''simple docstring'''


			assert masked_input.count("""<mask>"""    ) == 1
			SCREAMING_SNAKE_CASE__								=      torch.tensor(tokenizer.encode(snake_case__		,				add_special_tokens=snake_case__    )    ).unsqueeze(0    )  # Batch size 1
			SCREAMING_SNAKE_CASE__								=      model(snake_case__    )[0]  # The last hidden-state is the first element of the output tuple
			SCREAMING_SNAKE_CASE__								=      (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
			SCREAMING_SNAKE_CASE__								=      logits[0, masked_index, :]
			SCREAMING_SNAKE_CASE__								=      logits.softmax(dim=0    )
			SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      prob.topk(k=snake_case__		,				dim=0    )
			SCREAMING_SNAKE_CASE__								=      """ """.join(
			    [tokenizer.convert_ids_to_tokens(indices[i].item()    ) for i in range(len(snake_case__    )    )]    )
			SCREAMING_SNAKE_CASE__								=      tokenizer.mask_token
			SCREAMING_SNAKE_CASE__								=      []
			for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """    )    ):
						SCREAMING_SNAKE_CASE__								=      predicted_token_bpe.replace("""\u2581"""		,				""" """    )
						if " {0}".format(snake_case__    ) in masked_input:
									topk_filled_outputs.append(
									    (
									        masked_input.replace(""" {0}""".format(snake_case__    )		,				snake_case__    ),
									        values[index].item(),
									        predicted_token,
									    )    )
						else:
									topk_filled_outputs.append(
									    (
									        masked_input.replace(snake_case__		,				snake_case__    ),
									        values[index].item(),
									        predicted_token,
									    )    )
			return topk_filled_outputs


A_							:			Dict											= CamembertTokenizer.from_pretrained("camembert-base")
A_							:			Union[str, Any]											= CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

A_							:			Optional[Any]											= "Le camembert est <mask> :)"




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





"""simple docstring"""




def 	A       (     snake_case__    ):




			'''simple docstring'''


			if not nums:  # Makes sure that the list is not empty
						raise ValueError("""List is empty"""    )

			SCREAMING_SNAKE_CASE__								=      sum(snake_case__    ) / len(snake_case__    )  # Calculate the average
			return sum(abs(x - average    ) for x in nums    ) / len(snake_case__    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()


