
from __future__ import annotations

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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						NDArray[floataa]							,						__UpperCamelCase :						NDArray[floataa]							,						__UpperCamelCase :						list[int]							,						__UpperCamelCase :						int							,						) ->       list[float]:

				"""simple docstring"""







				A__       ,							A__  :	Tuple         =							coefficient_matrix.shape
				A__       ,							A__  :	List[str]         =							constant_matrix.shape

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

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

				if rowsa != rowsa:
								A__  :	Union[str, Any]         =							(
								    '''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__  :	Dict         =							(
								    '''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__  :	NDArray[floataa]         =							np.concatenate(
				    (coefficient_matrix, constant_matrix)							,						axis=1       )

				A__       ,							A__  :	Union[str, Any]         =							table.shape

				strictly_diagonally_dominant(__UpperCamelCase       )

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

				return [float(__UpperCamelCase       ) for i in new_val]

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						NDArray[floataa]       ) ->       bool:

				"""simple docstring"""







				A__       ,							A__  :	Union[str, Any]         =							table.shape

				A__  :	Union[str, Any]         =							True

				for i in range(0							,						__UpperCamelCase       ):
								A__  :	Union[str, 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()

import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format







class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				None





	def   __snake_case				(			self		):
					A__  :	Dict         =							self.feature_extraction_class(**self.feat_extract_dict		)
					A__  :	Tuple         =							json.loads(feat_extract.to_json_string()		)
					for key, value in self.feat_extract_dict.items():
									self.assertEqual(obj[key]				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.feature_extraction_class(**self.feat_extract_dict		)

					with tempfile.TemporaryDirectory() as tmpdirname:
									A__  :	Any         =							os.path.join(UpperCamelCase__				,       '''feat_extract.json'''		)
									feat_extract_first.to_json_file(UpperCamelCase__		)
									A__  :	Dict         =							self.feature_extraction_class.from_json_file(UpperCamelCase__		)

					self.assertEqual(feat_extract_second.to_dict()				,       feat_extract_first.to_dict()		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.feature_extraction_class(**self.feat_extract_dict		)

					with tempfile.TemporaryDirectory() as tmpdirname:
									A__  :	Any         =							feat_extract_first.save_pretrained(UpperCamelCase__		)[0]
									check_json_file_has_correct_format(UpperCamelCase__		)
									A__  :	Optional[int]         =							self.feature_extraction_class.from_pretrained(UpperCamelCase__		)

					self.assertEqual(feat_extract_second.to_dict()				,       feat_extract_first.to_dict()		)





	def   __snake_case				(			self		):
					A__  :	str         =							self.feature_extraction_class()
					self.assertIsNotNone(UpperCamelCase__		)