

'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCamelCase						    =      {
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      ['''BloomTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BloomForCausalLM''',
          '''BloomModel''',
          '''BloomPreTrainedModel''',
          '''BloomForSequenceClassification''',
          '''BloomForTokenClassification''',
          '''BloomForQuestionAnswering''',
      ]

if TYPE_CHECKING:
      from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bloom_fast import BloomTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bloom import (
                BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                BloomForCausalLM,
                BloomForQuestionAnswering,
                BloomForSequenceClassification,
                BloomForTokenClassification,
                BloomModel,
                BloomPreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase						    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



'''simple docstring'''







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    )     ->     list[float]:
       A_   ,     A_		   = coefficient_matrix.shape
       A_   ,     A_		   = constant_matrix.shape

       if rowsa != colsa:
              A_		   = F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
              raise ValueError(UpperCAmelCase__			)

       if colsa != 1:
              A_		   = F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
              raise ValueError(UpperCAmelCase__			)

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

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

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

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

       A_   ,     A_		   = table.shape

       strictly_diagonally_dominant(UpperCAmelCase__			)

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

       return [float(UpperCAmelCase__			) for i in new_val]
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     bool:
       A_   ,     A_		   = table.shape

       A_		   = True

       for i in range(0,    UpperCAmelCase__			):
              A_		   = 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()
