


'''simple docstring'''






from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray


def  _snake_case       (					A			,   A			,   A			,   A			,   )						->   list[float]:
							lowerCAmelCase__       , lowerCAmelCase__	       =     coefficient_matrix.shape
							lowerCAmelCase__       , lowerCAmelCase__	       =     constant_matrix.shape

							if rowsa != colsa:
														lowerCAmelCase__	       =     F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
														raise ValueError(A	)

							if colsa != 1:
														lowerCAmelCase__	       =     F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
														raise ValueError(A	)

							if rowsa != rowsa:
														lowerCAmelCase__	       =     (
														    '''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
														    F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
														)
														raise ValueError(A	)

							if len(A	) != rowsa:
														lowerCAmelCase__	       =     (
														    '''Number of initial values must be equal to number of rows in coefficient '''
														    F"""matrix but received {len(A	)} and {rowsa}"""
														)
														raise ValueError(A	)

							if iterations <= 0:
														raise ValueError('''Iterations must be at least 1'''	)

							lowerCAmelCase__	       =     np.concatenate(
							    (coefficient_matrix, constant_matrix)			,   axis=1	)

							lowerCAmelCase__       , lowerCAmelCase__	       =     table.shape

							strictly_diagonally_dominant(A	)

							# Iterates the whole matrix for given number of times
							for _ in range(A	):
														lowerCAmelCase__	       =     []
														for row in range(A	):
																					lowerCAmelCase__	       =     0
																					for col in range(A	):
																												if col == row:
																																			lowerCAmelCase__	       =     table[row][col]
																												elif col == cols - 1:
																																			lowerCAmelCase__	       =     table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					lowerCAmelCase__	       =     (temp + val) / denom
																					new_val.append(A	)
														lowerCAmelCase__	       =     new_val

							return [float(A	) for i in new_val]


def  _snake_case       (					A	)						->   bool:
							lowerCAmelCase__       , lowerCAmelCase__	       =     table.shape

							lowerCAmelCase__	       =     True

							for i in range(0			,   A	):
														lowerCAmelCase__	       =     0
														for j in range(0			,   cols - 1	):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('''Coefficient matrix is not strictly diagonally dominant'''	)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()



'''simple docstring'''






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def  _snake_case       (					A			,   A			,   A			,   A=5	)						->   List[str]:
							# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
							assert masked_input.count('''<mask>'''	) == 1
							lowerCAmelCase__	       =     torch.tensor(tokenizer.encode(A			,   add_special_tokens=A	)	).unsqueeze(0	)  # Batch size 1
							lowerCAmelCase__	       =     model(A	)[0]  # The last hidden-state is the first element of the output tuple
							lowerCAmelCase__	       =     (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
							lowerCAmelCase__	       =     logits[0, masked_index, :]
							lowerCAmelCase__	       =     logits.softmax(dim=0	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     prob.topk(k=A			,   dim=0	)
							lowerCAmelCase__	       =     ''' '''.join(
							    [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A	)	)]	)
							lowerCAmelCase__	       =     tokenizer.mask_token
							lowerCAmelCase__	       =     []
							for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''	)	):
														lowerCAmelCase__	       =     predicted_token_bpe.replace('''\u2581'''			,   ''' '''	)
														if " {0}".format(A	) in masked_input:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(''' {0}'''.format(A	)			,   A	),
																					        values[index].item(),
																					        predicted_token,
																					    )	)
														else:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(A			,   A	),
																					        values[index].item(),
																					        predicted_token,
																					    )	)
							return topk_filled_outputs


__UpperCAmelCase       =							CamembertTokenizer.from_pretrained('''camembert-base''')
__UpperCAmelCase       =							CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

__UpperCAmelCase       =							'''Le camembert est <mask> :)'''






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