
import numpy

# List of input, output pairs
__snake_case    :    List[str]         =     (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
__snake_case    :    str         =     (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
__snake_case    :    List[str]         =     [2, 4, 1, 5]
__snake_case    :    str         =     len(train_data)
__snake_case    :    Any         =     0.009







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Dict="train"		)  ->   str:

						"""simple docstring"""



						return calculate_hypothesis_value(UpperCamelCase_    ,       UpperCamelCase_		) - output(
						    UpperCamelCase_    ,       UpperCamelCase_		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]		)  ->   List[Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						0
						for i in range(len(UpperCamelCase_		) - 1		):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val







def 	_UpperCamelCase   (    UpperCamelCase_       :					Any    ,       UpperCamelCase_       :					Dict		)  ->   List[Any]:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					Union[str, Any]		)  ->   int:

						"""simple docstring"""



						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 	_UpperCamelCase   (    UpperCamelCase_       :					Tuple    ,       UpperCamelCase_       :					List[Any]=m		)  ->   Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						0
						for i in range(UpperCamelCase_		):
												if index == -1:
																		summation_value += _error(UpperCamelCase_		)
												else:
																		summation_value += _error(UpperCamelCase_		) * train_data[i][0][index]
						return summation_value







def 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]		)  ->   Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__    						=						summation_of_cost_derivative(UpperCamelCase_    ,       UpperCamelCase_		) / m
						return cost_derivative_value







def 	_UpperCamelCase   (    )  ->   Optional[int]:

						"""simple docstring"""



						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						lowerCAmelCase__    						=						0.00_0002
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						while True:
												j += 1
												lowerCAmelCase__    						=						[0, 0, 0, 0]
												for i in range(0    ,       len(UpperCamelCase_		)		):
																		lowerCAmelCase__    						=						get_cost_derivative(i - 1		)
																		lowerCAmelCase__    						=						(
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    UpperCamelCase_    ,       UpperCamelCase_    ,       atol=UpperCamelCase_    ,       rtol=UpperCamelCase_    ,       ):
																		break
												lowerCAmelCase__    						=						temp_parameter_vector
						print(('Number of iterations:', j)		)







def 	_UpperCamelCase   (    )  ->   Dict:

						"""simple docstring"""



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


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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__snake_case    :    Dict         =     {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Union[str, Any]         =     ["""SpeechEncoderDecoderModel"""]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    str         =     ["""FlaxSpeechEncoderDecoderModel"""]

if TYPE_CHECKING:
					from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
					import sys

					__snake_case    :    Tuple         =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





