



from __future__ import annotations

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





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , ):
    __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      __snake_case	(			_UpperCAmelCase				):
    __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()











from typing import TYPE_CHECKING

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


__lowerCAmelCase		:				List[str]       			=		{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Dict       			=		[
		    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ResNetForImageClassification',
		    'ResNetModel',
		    'ResNetPreTrainedModel',
		    'ResNetBackbone',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				int       			=		[
		    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFResNetForImageClassification',
		    'TFResNetModel',
		    'TFResNetPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Optional[Any]       			=		[
		    'FlaxResNetForImageClassification',
		    'FlaxResNetModel',
		    'FlaxResNetPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		__lowerCAmelCase		:				int       			=		_LazyModule(__name__, globals()['__file__'], _import_structure)






