






"""simple docstring"""


from __future__ import annotations

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




def       snake_case  (A_				:NDArray[floataa]     ,      A_				:NDArray[floataa]     ,      A_				:list[int]     ,      A_				:int     ,      ):







  '''simple docstring'''

  a					:      Optional[Any]    =       coefficient_matrix.shape
  a					:      Any    =       constant_matrix.shape

  if rowsa != colsa:
    a					:      Optional[int]    =       f'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
    raise ValueError(A_				)

  if colsa != 1:
    a					:      Optional[Any]    =       f'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
    raise ValueError(A_				)

  if rowsa != rowsa:
    a					:      List[Any]    =       (
        '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:
    a					:      List[str]    =       (
        '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'				)

  a					:      NDArray[floataa]    =       np.concatenate(
      (coefficient_matrix, constant_matrix)     ,      axis=1				)

  a					:      Dict    =       table.shape

  strictly_diagonally_dominant(A_				)

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

  return [float(A_				) for i in new_val]




def       snake_case  (A_				:NDArray[floataa]				):







  '''simple docstring'''

  a					:      List[Any]    =       table.shape

  a					:      List[Any]    =       True

  for i in range(0     ,      A_				):
    a					:      Any    =       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 unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
   import jax.numpy as jnp

   from transformers import FlaxXLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_flax
class 						snake_case  (  unittest.TestCase	):





    @slow
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Optional[int]    =       FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'      )
      a					:      int    =       AutoTokenizer.from_pretrained('xlm-roberta-base'      )
      a					:      int    =       'The dog is cute and lives in the garden house'
      a					:      List[Any]    =       jnp.array([tokenizer.encode(A      )]      )

      a					:      int    =       (1, 1_2, 7_6_8)  # batch_size, sequence_length, embedding_vector_dim
      a					:      Dict    =       jnp.array(
          [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]      )

      a					:      Any    =       model(A      )['last_hidden_state']
      self.assertEqual(output.shape    ,		A      )
      # compare the actual values for a slice of last dim
      self.assertTrue(jnp.allclose(output[:, :, -1]    ,		A    ,		atol=1E-3      )      )




