


"""simple docstring"""
from __future__ import annotations

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







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	)      ->			list[float]:
							snake_case_    ,  snake_case_          =						coefficient_matrix.shape
							snake_case_    ,  snake_case_          =						constant_matrix.shape

							if rowsa != colsa:
														snake_case_          =						f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
														raise ValueError(_SCREAMING_SNAKE_CASE			)

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

							if rowsa != rowsa:
														snake_case_          =						(
														    """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:
														snake_case_          =						(
														    """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"""			)

							snake_case_          =						np.concatenate(
							    (coefficient_matrix, constant_matrix)			,	axis=1			)

							snake_case_    ,  snake_case_          =						table.shape

							strictly_diagonally_dominant(_SCREAMING_SNAKE_CASE			)

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

							return [float(_SCREAMING_SNAKE_CASE			) for i in new_val]







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			bool:
							snake_case_    ,  snake_case_          =						table.shape

							snake_case_          =						True

							for i in range(0			,	_SCREAMING_SNAKE_CASE			):
														snake_case_          =						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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE							:      Dict						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      Tuple						=      {
    'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
    'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[Any]							 =      """mobilenet_v1"""



							def __init__(			self							: Union[str, Any]						,    UpperCAmelCase_							: str=3						,    UpperCAmelCase_							: List[Any]=224						,    UpperCAmelCase_							: List[Any]=1.0						,    UpperCAmelCase_							: Any=8						,    UpperCAmelCase_							: int="relu6"						,    UpperCAmelCase_							: str=True						,    UpperCAmelCase_							: List[str]=0.999						,    UpperCAmelCase_							: Optional[Any]=0.02						,    UpperCAmelCase_							: List[Any]=0.001						,    **UpperCAmelCase_							: Any						,    )   ->Union[str, Any]:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_    )

														if depth_multiplier <= 0:
																					raise ValueError("""depth_multiplier must be greater than zero."""    )

														snake_case_          =						num_channels
														snake_case_          =						image_size
														snake_case_          =						depth_multiplier
														snake_case_          =						min_depth
														snake_case_          =						hidden_act
														snake_case_          =						tf_padding
														snake_case_          =						classifier_dropout_prob
														snake_case_          =						initializer_range
														snake_case_          =						layer_norm_eps





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						int							 =      version.parse("""1.11""")



							@property
							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



														return OrderedDict([("""pixel_values""", {0: """batch"""})]    )



							@property
							def 						lowerCAmelCase    (			self							: int    )   ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



														if self.task == "image-classification":
																					return OrderedDict([("""logits""", {0: """batch"""})]    )
														else:
																					return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]    )



							@property
							def 						lowerCAmelCase    (			self							: int    )   ->float:



														"""simple docstring"""



														return 1E-4



