

from __future__ import annotations

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







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

     """simple docstring"""







     _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    coefficient_matrix.shape
     _lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    constant_matrix.shape

     if rowsa != colsa:
          _lowerCAmelCase   :					List[str]        =    f'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
          raise ValueError(SCREAMING_SNAKE_CASE				)

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

     if rowsa != rowsa:
          _lowerCAmelCase   :					Dict        =    (
              "Coefficient and constant matrices dimensions must be nxn and nx1 but "
              f'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
          )
          raise ValueError(SCREAMING_SNAKE_CASE				)

     if len(SCREAMING_SNAKE_CASE				) != rowsa:
          _lowerCAmelCase   :					Optional[Any]        =    (
              "Number of initial values must be equal to number of rows in coefficient "
              f'''matrix but received {len(SCREAMING_SNAKE_CASE				)} and {rowsa}'''
          )
          raise ValueError(SCREAMING_SNAKE_CASE				)

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

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

     _lowerCAmelCase      ,			_lowerCAmelCase   :					Union[str, Any]        =    table.shape

     strictly_diagonally_dominant(SCREAMING_SNAKE_CASE				)

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

     return [float(SCREAMING_SNAKE_CASE				) for i in new_val]







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      bool:

     """simple docstring"""







     _lowerCAmelCase      ,			_lowerCAmelCase   :					Union[str, Any]        =    table.shape

     _lowerCAmelCase   :					int        =    True

     for i in range(0		,							SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					str        =    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_tensorflow_text_available, is_torch_available


__UpperCAmelCase    				=     {
    'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ErnieForCausalLM',
      'ErnieForMaskedLM',
      'ErnieForMultipleChoice',
      'ErnieForNextSentencePrediction',
      'ErnieForPreTraining',
      'ErnieForQuestionAnswering',
      'ErnieForSequenceClassification',
      'ErnieForTokenClassification',
      'ErnieModel',
      'ErniePreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_ernie import (
        ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
        ErnieForCausalLM,
        ErnieForMaskedLM,
        ErnieForMultipleChoice,
        ErnieForNextSentencePrediction,
        ErnieForPreTraining,
        ErnieForQuestionAnswering,
        ErnieForSequenceClassification,
        ErnieForTokenClassification,
        ErnieModel,
        ErniePreTrainedModel,
    )

else:
  import sys

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






