


import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase					,     __lowerCamelCase		:							Dict							=						np.shape(SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							NDArray[floataa]							=						np.zeros((rows, 1)			,       dtype=SCREAMING_SNAKE_CASE__						)
 for row in reversed(range(SCREAMING_SNAKE_CASE__						)						):
  __lowerCamelCase		:							Optional[int]							=						0
  for col in range(row + 1			,       SCREAMING_SNAKE_CASE__						):
   total += coefficients[row, col] * x[col]

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

 return x
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase					,     __lowerCamelCase		:							Union[str, Any]							=						np.shape(SCREAMING_SNAKE_CASE__						)
 if rows != columns:
  return np.array(()			,       dtype=SCREAMING_SNAKE_CASE__						)

 # augmented matrix
 __lowerCamelCase		:							NDArray[floataa]							=						np.concatenate((coefficients, vector)			,       axis=1						)
 __lowerCamelCase		:							str							=						augmented_mat.astype('float64'						)

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

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

 return x


if __name__ == "__main__":
       import doctest

       doctest.testmod()





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        A_					(       unittest.TestCase    ):




      '''simple docstring'''


      @slow
      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							Dict							=						CamembertModel.from_pretrained('camembert-base'				)
       model.to(a				)

       __lowerCamelCase		:							List[Any]							=						torch.tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]						,      device=a						,      dtype=torch.long						,      )  # J'aime le camembert !
       with torch.no_grad():
        __lowerCamelCase		:							Tuple							=						model(a				)['last_hidden_state']
       __lowerCamelCase		:							Union[str, Any]							=						torch.Size((1, 10, 768)				)
       self.assertEqual(output.shape						,      a				)
       # compare the actual values for a slice.
       __lowerCamelCase		:							Tuple							=						torch.tensor(
           [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,      device=a						,      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]						,      a						,      atol=1e-4				)				)

