
"""simple docstring"""
import numpy as np
from numpy import floataa
from numpy.typing import NDArray


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			NDArray[floataa]:


					'''simple docstring'''
					__a:						Optional[int]		    =					np.shape(lowerCamelCase_ )

					__a:						NDArray[floataa]		    =					np.zeros((rows, 1)	,				dtype=lowerCamelCase_ )
					for row in reversed(range(lowerCamelCase_ ) ):
										__a:						Tuple		    =					0
										for col in range(row + 1	,				lowerCamelCase_ ):
															total += coefficients[row, col] * x[col]

										__a:						Any		    =					(vector[row] - total) / coefficients[row, row]

					return x


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			NDArray[floataa]:


					'''simple docstring'''
					__a:						Union[str, Any]		    =					np.shape(lowerCamelCase_ )
					if rows != columns:
										return np.array(()	,				dtype=lowerCamelCase_ )

					# augmented matrix
					__a:						NDArray[floataa]		    =					np.concatenate((coefficients, vector)	,				axis=1 )
					__a:						int		    =					augmented_mat.astype("""float64""" )

					# scale the matrix leaving it triangular
					for row in range(rows - 1 ):
										__a:						Union[str, Any]		    =					augmented_mat[row, row]
										for col in range(row + 1	,				lowerCamelCase_ ):
															__a:						Union[str, Any]		    =					augmented_mat[col, row] / pivot
															augmented_mat[col, :] -= factor * augmented_mat[row, :]

					__a:						Any		    =					retroactive_resolution(
					    augmented_mat[:, 0:columns]	,				augmented_mat[:, columns : columns + 1] )

					return x


if __name__ == "__main__":
			import doctest

			doctest.testmod()


"""simple docstring"""
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			import torch

			from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class    _snake_case  (						unittest.TestCase       ):






							@slow
							def 							__lowercase  (					self						:			Optional[int]    ):






												"""simple docstring"""



												__a:						Any		    =					CamembertModel.from_pretrained("""camembert-base"""    )
												model.to(snake_case__    )

												__a:						str		    =					torch.tensor(
												    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]						, device=snake_case__						, dtype=torch.long						, )  # J'aime le camembert !
												with torch.no_grad():
																	__a:						str		    =					model(snake_case__    )["""last_hidden_state"""]
												__a:						Optional[Any]		    =					torch.Size((1, 1_0, 7_6_8)    )
												self.assertEqual(output.shape						, snake_case__    )
												# compare the actual values for a slice.
												__a:						Union[str, Any]		    =					torch.tensor(
												    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]						, device=snake_case__						, dtype=torch.float						, )
												# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
												# camembert.eval()
												# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

												self.assertTrue(torch.allclose(output[:, :3, :3]						, snake_case__						, atol=1e-4    )    )
