

from __future__ import annotations

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			)  ->					Any:
	__lowerCamelCase    :    List[Any] 	=			coefficient_matrix.shape
	__lowerCamelCase    :    Union[str, Any] 	=			constant_matrix.shape

	if rowsa != colsa:
		__lowerCamelCase    :    Optional[int] 	=			F"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
		raise ValueError(lowerCamelCase__      )

	if colsa != 1:
		__lowerCamelCase    :    Any 	=			F"Constant matrix must be nx1 but received {rowsa}x{colsa}"
		raise ValueError(lowerCamelCase__      )

	if rowsa != rowsa:
		__lowerCamelCase    :    int 	=			(
		    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
		    F"received {rowsa}x{colsa} and {rowsa}x{colsa}"
		)
		raise ValueError(lowerCamelCase__      )

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

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

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

	__lowerCamelCase    :    Optional[Any] 	=			table.shape

	strictly_diagonally_dominant(lowerCamelCase__      )

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

	return [float(lowerCamelCase__      ) for i in new_val]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    List[str] 	=			table.shape

	__lowerCamelCase    :    Tuple 	=			True

	for i in range(0     ,			lowerCamelCase__      ):
		__lowerCamelCase    :    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_torch_available, is_vision_available


a		={
    """configuration_mask2former""": [
        """MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Mask2FormerConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""Mask2FormerImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Mask2FormerForUniversalSegmentation""",
					    """Mask2FormerModel""",
					    """Mask2FormerPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure)




