





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_							=				logging.get_logger(__name__)

UpperCAmelCase_							=				{
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       Any       =	"""glpn"""






		def __init__(							self,   snake_case__=3,   snake_case__=4,   snake_case__=[2, 2, 2, 2],   snake_case__=[8, 4, 2, 1],   snake_case__=[32, 64, 1_60, 2_56],   snake_case__=[7, 3, 3, 3],   snake_case__=[4, 2, 2, 2],   snake_case__=[1, 2, 5, 8],   snake_case__=[4, 4, 4, 4],   snake_case__="gelu",   snake_case__=0.0,   snake_case__=0.0,   snake_case__=0.02,   snake_case__=0.1,   snake_case__=1E-6,   snake_case__=64,   snake_case__=10,   snake_case__=-1,   **snake_case__,   )       -> Union[str, Any]:






									"""simple docstring"""
									super().__init__(**snake_case__  )

									lowercase_	:   Any  				=     num_channels
									lowercase_	:   Tuple  				=     num_encoder_blocks
									lowercase_	:   Optional[Any]  				=     depths
									lowercase_	:   Optional[int]  				=     sr_ratios
									lowercase_	:   Optional[int]  				=     hidden_sizes
									lowercase_	:   Any  				=     patch_sizes
									lowercase_	:   Optional[Any]  				=     strides
									lowercase_	:   str  				=     mlp_ratios
									lowercase_	:   Optional[int]  				=     num_attention_heads
									lowercase_	:   List[str]  				=     hidden_act
									lowercase_	:   Dict  				=     hidden_dropout_prob
									lowercase_	:   Any  				=     attention_probs_dropout_prob
									lowercase_	:   List[Any]  				=     initializer_range
									lowercase_	:   Tuple  				=     drop_path_rate
									lowercase_	:   str  				=     layer_norm_eps
									lowercase_	:   List[Any]  				=     decoder_hidden_size
									lowercase_	:   Dict  				=     max_depth
									lowercase_	:   Dict  				=     head_in_index






from __future__ import annotations

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





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				,       )				-> list[float]:

							"""simple docstring"""



							lowercase_				,		lowercase_	:   List[str]  				=     coefficient_matrix.shape
							lowercase_				,		lowercase_	:   Any  				=     constant_matrix.shape

							if rowsa != colsa:
														lowercase_	:   List[Any]  				=     f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
														raise ValueError(lowercase				)

							if colsa != 1:
														lowercase_	:   Optional[int]  				=     f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
														raise ValueError(lowercase				)

							if rowsa != rowsa:
														lowercase_	:   Tuple  				=     (
														    """Coefficient and constant matrices dimensions must be nxn and nx1 but """
														    f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
														)
														raise ValueError(lowercase				)

							if len(lowercase				) != rowsa:
														lowercase_	:   int  				=     (
														    """Number of initial values must be equal to number of rows in coefficient """
														    f"""matrix but received {len(lowercase				)} and {rowsa}"""
														)
														raise ValueError(lowercase				)

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

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

							lowercase_				,		lowercase_	:   Dict  				=     table.shape

							strictly_diagonally_dominant(lowercase				)

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

							return [float(lowercase				) for i in new_val]





def  __magic_name__  (				lowercase				)				-> bool:

							"""simple docstring"""



							lowercase_				,		lowercase_	:   str  				=     table.shape

							lowercase_	:   str  				=     True

							for i in range(0				,       lowercase				):
														lowercase_	:   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()