




import numpy

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case="train"       )						->				Optional[int]:
						return calculate_hypothesis_value(snake_case			,     snake_case       ) - output(
						    snake_case			,     snake_case       )




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[str]:
						_UpperCAmelCase      =			0
						for i in range(len(snake_case       ) - 1       ):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Tuple:
						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				List[str]:
						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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case=m       )						->				Tuple:
						_UpperCAmelCase      =			0
						for i in range(snake_case       ):
												if index == -1:
																		summation_value += _error(snake_case       )
												else:
																		summation_value += _error(snake_case       ) * train_data[i][0][index]
						return summation_value




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				int:
						_UpperCAmelCase      =			summation_of_cost_derivative(snake_case			,     snake_case       ) / m
						return cost_derivative_value




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




def        _SCREAMING_SNAKE_CASE      (		)						->				str:
						for i in range(len(snake_case       )       ):
												print(("""Actual output value:""", output(snake_case			,     """test"""       ))       )
												print(("""Hypothesis output:""", calculate_hypothesis_value(snake_case			,     """test"""       ))       )


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





import logging

from transformers.configuration_utils import PretrainedConfig


a													=			logging.getLogger(__name__)




class       _A				(		__lowercase     ):
		__a					    =       """masked_bert"""






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=3_0522						,		_SCREAMING_SNAKE_CASE=768						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=3072						,		_SCREAMING_SNAKE_CASE="gelu"						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=512						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=0.02						,		_SCREAMING_SNAKE_CASE=1e-12						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE="topK"						,		_SCREAMING_SNAKE_CASE="constant"						,		_SCREAMING_SNAKE_CASE=0.0						,		**_SCREAMING_SNAKE_CASE						,		):
								super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			vocab_size
								_UpperCAmelCase      =			hidden_size
								_UpperCAmelCase      =			num_hidden_layers
								_UpperCAmelCase      =			num_attention_heads
								_UpperCAmelCase      =			hidden_act
								_UpperCAmelCase      =			intermediate_size
								_UpperCAmelCase      =			hidden_dropout_prob
								_UpperCAmelCase      =			attention_probs_dropout_prob
								_UpperCAmelCase      =			max_position_embeddings
								_UpperCAmelCase      =			type_vocab_size
								_UpperCAmelCase      =			initializer_range
								_UpperCAmelCase      =			layer_norm_eps
								_UpperCAmelCase      =			pruning_method
								_UpperCAmelCase      =			mask_init
								_UpperCAmelCase      =			mask_scale