


from __future__ import annotations

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


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			):
  A_     ,       A_      :				Any		     =					coefficient_matrix.shape
  A_     ,       A_      :				List[Any]		     =					constant_matrix.shape

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

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

  if rowsa != rowsa:
    A_      :				Tuple		     =					(
        '''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:
    A_      :				Union[str, 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'''       )

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

  A_     ,       A_      :				int		     =					table.shape

  strictly_diagonally_dominant(SCREAMING_SNAKE_CASE       )

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

  return [float(SCREAMING_SNAKE_CASE       ) for i in new_val]


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_     ,       A_      :				Union[str, Any]		     =					table.shape

  A_      :				int		     =					True

  for i in range(0 ,			SCREAMING_SNAKE_CASE       ):
    A_      :				Optional[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()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase								=	{
    """configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfig""", """MobileViTOnnxConfig"""],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	["""MobileViTFeatureExtractor"""]
     UpperCamelCase								=	["""MobileViTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MobileViTForImageClassification""",
         """MobileViTForSemanticSegmentation""",
         """MobileViTModel""",
         """MobileViTPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFMobileViTForImageClassification""",
         """TFMobileViTForSemanticSegmentation""",
         """TFMobileViTModel""",
         """TFMobileViTPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_mobilevit import MobileViTFeatureExtractor
          from .image_processing_mobilevit import MobileViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilevit import (
              MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileViTForImageClassification,
              MobileViTForSemanticSegmentation,
              MobileViTModel,
              MobileViTPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilevit import (
              TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileViTForImageClassification,
              TFMobileViTForSemanticSegmentation,
              TFMobileViTModel,
              TFMobileViTPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




