
import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  str=5   )				->		Any:







	'''simple docstring'''


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


snake_case :		Dict							      =  CamembertTokenizer.from_pretrained("camembert-base")
snake_case :		Optional[int]							      =  CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

snake_case :		str							      =  "Le camembert est <mask> :)"





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


def   lowerCAmelCase_						(					_snake_case      :  list[list[int | float]]   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Any	   =     len(_snake_case   )
	__magic_name__		:		Optional[Any]	   =     len(matrix[0]   )
	__magic_name__		:		Union[str, Any]	   =     min(_snake_case      ,   _snake_case   )

	for row in range(_snake_case   ):
		# Check if diagonal element is not zero
		if matrix[row][row] != 0:
			# Eliminate all the elements below the diagonal
			for col in range(row + 1      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     matrix[col][row] / matrix[row][row]
				for i in range(_snake_case      ,   _snake_case   ):
					matrix[col][i] -= multiplier * matrix[row][i]
		else:
			# Find a non-zero diagonal element to swap rows
			__magic_name__		:		str	   =     True
			for i in range(row + 1      ,   _snake_case   ):
				if matrix[i][row] != 0:
					__magic_name__   ,    __magic_name__		:		List[str]	   =     matrix[i], matrix[row]
					__magic_name__		:		Union[str, Any]	   =     False
					break
			if reduce:
				rank -= 1
				for i in range(_snake_case   ):
					__magic_name__		:		Any	   =     matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
			row -= 1

	return rank


if __name__ == "__main__":
				import doctest

				doctest.testmod()
