



import numpy

# List of input, output pairs
a     :    Optional[int]      						=					(
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
a     :    List[str]      						=					(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
a     :    int      						=					[2, 4, 1, 5]
a     :    Optional[Any]      						=					len(train_data)
a     :    List[Any]      						=					0.0_0_9



def    __lowerCamelCase						(      _lowercase			,       _lowercase="train"	)   ->    Dict:
							return calculate_hypothesis_value(_lowercase			,       _lowercase	) - output(
							    _lowercase			,       _lowercase	)



def    __lowerCamelCase						(      _lowercase	)   ->    int:
							UpperCAmelCase  :	List[str]							  =	0
							for i in range(len(_lowercase	) - 1	):
														hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
							hyp_val += parameter_vector[0]
							return hyp_val



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Optional[int]:
							if data_set == "train":
														return train_data[example_no][1]
							elif data_set == "test":
														return test_data[example_no][1]
							return None



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Dict:
							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    __lowerCamelCase						(      _lowercase			,       _lowercase=m	)   ->    Any:
							UpperCAmelCase  :	Dict							  =	0
							for i in range(_lowercase	):
														if index == -1:
																					summation_value += _error(_lowercase	)
														else:
																					summation_value += _error(_lowercase	) * train_data[i][0][index]
							return summation_value



def    __lowerCamelCase						(      _lowercase	)   ->    Optional[int]:
							UpperCAmelCase  :	Tuple							  =	summation_of_cost_derivative(_lowercase			,       _lowercase	) / m
							return cost_derivative_value



def    __lowerCamelCase						(      )   ->    List[Any]:
							global parameter_vector
							# Tune these values to set a tolerance value for predicted output
							UpperCAmelCase  :	int							  =	0.00_0002
							UpperCAmelCase  :	str							  =	0
							UpperCAmelCase  :	Tuple							  =	0
							while True:
														j += 1
														UpperCAmelCase  :	Tuple							  =	[0, 0, 0, 0]
														for i in range(0			,       len(_lowercase	)	):
																					UpperCAmelCase  :	Dict							  =	get_cost_derivative(i - 1	)
																					UpperCAmelCase  :	List[str]							  =	(
																					    parameter_vector[i] - LEARNING_RATE * cost_derivative
																					)
														if numpy.allclose(
														    _lowercase			,       _lowercase			,       atol=_lowercase			,       rtol=_lowercase			,       ):
																					break
														UpperCAmelCase  :	List[Any]							  =	temp_parameter_vector
							print(("""Number of iterations:""", j)	)



def    __lowerCamelCase						(      )   ->    List[str]:
							for i in range(len(_lowercase	)	):
														print(("""Actual output value:""", output(_lowercase			,       """test"""	))	)
														print(("""Hypothesis output:""", calculate_hypothesis_value(_lowercase			,       """test"""	))	)


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






'''simple docstring'''




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


a     :    str      						=					logging.get_logger(__name__)

a     :    Any      						=					{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'fnet'





	def __init__(						self	,	A=32000	,	A=768	,	A=12	,	A=3072	,	A="gelu_new"	,	A=0.1	,	A=512	,	A=4	,	A=0.0_2	,	A=1e-12	,	A=False	,	A=512	,	A=3	,	A=1	,	A=2	,	**A	,	)    ->    int:
								super().__init__(pad_token_id=A	,	bos_token_id=A	,	eos_token_id=A	,	**A					)

								UpperCAmelCase  :	Optional[Any]							  =	vocab_size
								UpperCAmelCase  :	Dict							  =	max_position_embeddings
								UpperCAmelCase  :	List[str]							  =	hidden_size
								UpperCAmelCase  :	int							  =	num_hidden_layers
								UpperCAmelCase  :	Any							  =	intermediate_size
								UpperCAmelCase  :	List[str]							  =	hidden_act
								UpperCAmelCase  :	Tuple							  =	hidden_dropout_prob
								UpperCAmelCase  :	List[str]							  =	initializer_range
								UpperCAmelCase  :	List[Any]							  =	type_vocab_size
								UpperCAmelCase  :	int							  =	layer_norm_eps
								UpperCAmelCase  :	Optional[Any]							  =	use_tpu_fourier_optimizations
								UpperCAmelCase  :	List[Any]							  =	tpu_short_seq_length

