



from __future__ import annotations

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






def      __lowercase       (			a__							,       a__							,       a__							,       a__							,       )    ->			list[float]:
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= coefficient_matrix.shape
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= constant_matrix.shape

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

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

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

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

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

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

							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= table.shape

							strictly_diagonally_dominant(a__       )

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

							return [float(a__       ) for i in new_val]






def      __lowercase       (			a__       )    ->			bool:
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= table.shape

							__SCREAMING_SNAKE_CASE									= True

							for i in range(0							,       a__       ):
														__SCREAMING_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()











import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class        UpperCAmelCase_  (       UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  LayoutLMTokenizer
						UpperCamelCase__      :			Any      =  LayoutLMTokenizerFast
						UpperCamelCase__      :			Optional[int]      =  True
						UpperCamelCase__      :			int      =  True






						def 	_A     (   self		):







													'''simple docstring'''

													super().setUp()

													__SCREAMING_SNAKE_CASE									= [
													    '[UNK]',
													    '[CLS]',
													    '[SEP]',
													    'want',
													    '##want',
													    '##ed',
													    'wa',
													    'un',
													    'runn',
													    '##ing',
													    ',',
													    'low',
													    'lowest',
													]
													__SCREAMING_SNAKE_CASE									= os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['vocab_file']		)
													with open(self.vocab_file				,							'w'				,							encoding='utf-8'		) as vocab_writer:
																				vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)






						def 	_A     (   self				,							**_A		):







													'''simple docstring'''

													return LayoutLMTokenizer.from_pretrained(self.tmpdirname				,							**_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'UNwant\u00E9d,running'
													__SCREAMING_SNAKE_CASE									= 'unwanted, running'
													return input_text, output_text






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.tokenizer_class(self.vocab_file		)

													__SCREAMING_SNAKE_CASE									= tokenizer.tokenize('UNwant\u00E9d,running'		)
													self.assertListEqual(_A				,							['un', '##want', '##ed', ',', 'runn', '##ing']		)
													self.assertListEqual(tokenizer.convert_tokens_to_ids(_A		)				,							[7, 4, 5, 10, 8, 9]		)






						def 	_A     (   self		):







													'''simple docstring'''

													pass






