




from __future__ import annotations

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_			,    )       ->	list[float]:



       """simple docstring"""

       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(lowercase_						)

       if colsa != 1:
              A__							=   f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
              raise ValueError(lowercase_						)

       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(lowercase_						)

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

       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(lowercase_						)

       # Iterates the whole matrix for given number of times
       for _ in range(lowercase_						):
              A__							=   []
              for row in range(lowercase_						):
                     A__							=   0
                     for col in range(lowercase_						):
                            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(lowercase_						)
              A__							=   new_val

       return [float(lowercase_						) for i in new_val]







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	bool:



       """simple docstring"""

       A__			,							A__							=   table.shape

       A__							=   True

       for i in range(0			,    lowercase_						):
              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()








from typing import TYPE_CHECKING

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


_lowerCamelCase     :		Any								=       {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		str								=       [
      """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """NezhaForNextSentencePrediction""",
      """NezhaForMaskedLM""",
      """NezhaForPreTraining""",
      """NezhaForMultipleChoice""",
      """NezhaForQuestionAnswering""",
      """NezhaForSequenceClassification""",
      """NezhaForTokenClassification""",
      """NezhaModel""",
      """NezhaPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_nezha import (
        NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
        NezhaPreTrainedModel,
    )


else:
  import sys

  _lowerCamelCase     :		List[str]								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


