
"""simple docstring"""


from __future__ import annotations

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






def 					_snake_case (						snake_case__      :   Dict		,					snake_case__      :   List[str]		,					snake_case__      :   Union[str, Any]		,					snake_case__      :   List[str]		,					):
	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(snake_case__   )

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

	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(snake_case__   )

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

	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(snake_case__   )

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

	return [float(snake_case__   ) for i in new_val]






def 					_snake_case (						snake_case__      :   List[str]   ):
	A      ,					A        =      table.shape

	A        =      True

	for i in range(0		,					snake_case__   ):
		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()
"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCAmelCase__  :  Tuple						     =	{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  List[Any]						     =	[
						    'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTMAEForPreTraining',
						    'ViTMAELayer',
						    'ViTMAEModel',
						    'ViTMAEPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  int						     =	[
						    'TFViTMAEForPreTraining',
						    'TFViTMAEModel',
						    'TFViTMAEPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_mae import (
												    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMAEForPreTraining,
												    ViTMAELayer,
												    ViTMAEModel,
												    ViTMAEPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
						import sys

						lowerCAmelCase__  :  int						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



