




import numpy

# List of input, output pairs
lowerCAmelCase__		:					List[Any]							=(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCAmelCase__		:					List[Any]							=(((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase__		:					Tuple							=[2, 4, 1, 5]
lowerCAmelCase__		:					Any							=len(train_data)
lowerCAmelCase__		:					Union[str, Any]							=0.009






def      __lowercase       (			a__							,       a__="train"       )    ->			Any:
							return calculate_hypothesis_value(A__							,       A__       ) - output(
							    A__							,       A__       )






def      __lowercase       (			a__       )    ->			Tuple:
							__SCREAMING_SNAKE_CASE									= 0
							for i in range(len(A__       ) - 1       ):
														hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
							hyp_val += parameter_vector[0]
							return hyp_val






def      __lowercase       (			a__							,       a__       )    ->			Dict:



							if data_set == "train":
														return train_data[example_no][1]
							elif data_set == "test":
														return test_data[example_no][1]
							return None






def      __lowercase       (			a__							,       a__       )    ->			Optional[Any]:



							if data_set == "train":
														return _hypothesis_value(train_data[example_no][0]       )
							elif data_set == "test":
														return _hypothesis_value(test_data[example_no][0]       )
							return None






def      __lowercase       (			a__							,       a__=m       )    ->			Any:
							__SCREAMING_SNAKE_CASE									= 0
							for i in range(A__       ):
														if index == -1:
																					summation_value += _error(A__       )
														else:
																					summation_value += _error(A__       ) * train_data[i][0][index]
							return summation_value






def      __lowercase       (			a__       )    ->			List[str]:
							__SCREAMING_SNAKE_CASE									= summation_of_cost_derivative(A__							,       A__       ) / m
							return cost_derivative_value






def      __lowercase       (			)    ->			List[str]:
							global parameter_vector
							# Tune these values to set a tolerance value for predicted output
							__SCREAMING_SNAKE_CASE									= 0.000002
							__SCREAMING_SNAKE_CASE									= 0
							__SCREAMING_SNAKE_CASE									= 0
							while True:
														j += 1
														__SCREAMING_SNAKE_CASE									= [0, 0, 0, 0]
														for i in range(0							,       len(A__       )       ):
																					__SCREAMING_SNAKE_CASE									= get_cost_derivative(i - 1       )
																					__SCREAMING_SNAKE_CASE									= (
																					    parameter_vector[i] - LEARNING_RATE * cost_derivative
																					)
														if numpy.allclose(
														    A__							,       A__							,       atol=A__							,       rtol=A__							,       ):
																					break
														__SCREAMING_SNAKE_CASE									= temp_parameter_vector
							print(('Number of iterations:', j)       )






def      __lowercase       (			)    ->			Tuple:



							for i in range(len(A__       )       ):
														print(('Actual output value:', output(A__							,       'test'       ))       )
														print(('Hypothesis output:', calculate_hypothesis_value(A__							,       'test'       ))       )


if __name__ == "__main__":
			run_gradient_descent()
			print('''\nTesting gradient descent for a linear hypothesis function.\n''')
			test_gradient_descent()











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






