



"""simple docstring"""

import numpy

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





def       __magic_name__    (   _lowerCamelCase				: Dict  ,       _lowerCamelCase				: List[Any]="train"					):
							return calculate_hypothesis_value(_lowerCamelCase  ,       _lowerCamelCase					) - output(
							    _lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optional[int]					):
							__a		:		List[Any]									=       0
							for i in range(len(_lowerCamelCase					) - 1					):
														hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
							hyp_val += parameter_vector[0]
							return hyp_val





def       __magic_name__    (   _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: Tuple					):
							if data_set == "train":
														return train_data[example_no][1]
							elif data_set == "test":
														return test_data[example_no][1]
							return None





def       __magic_name__    (   _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: 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       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]=m					):
							__a		:		Union[str, Any]									=       0
							for i in range(_lowerCamelCase					):
														if index == -1:
																					summation_value += _error(_lowerCamelCase					)
														else:
																					summation_value += _error(_lowerCamelCase					) * train_data[i][0][index]
							return summation_value





def       __magic_name__    (   _lowerCamelCase				: List[str]					):
							__a		:		List[str]									=       summation_of_cost_derivative(_lowerCamelCase  ,       _lowerCamelCase					) / m
							return cost_derivative_value





def       __magic_name__    (   ):
							global parameter_vector
							# Tune these values to set a tolerance value for predicted output
							__a		:		Union[str, Any]									=       0.00_00_02
							__a		:		Dict									=       0
							__a		:		Optional[int]									=       0
							while True:
														j += 1
														__a		:		List[Any]									=       [0, 0, 0, 0]
														for i in range(0  ,       len(_lowerCamelCase					)					):
																					__a		:		List[str]									=       get_cost_derivative(i - 1					)
																					__a		:		Any									=       (
																					    parameter_vector[i] - LEARNING_RATE * cost_derivative
																					)
														if numpy.allclose(
														    _lowerCamelCase  ,       _lowerCamelCase  ,       atol=_lowerCamelCase  ,       rtol=_lowerCamelCase  ,       ):
																					break
														__a		:		List[Any]									=       temp_parameter_vector
							print(("""Number of iterations:""", j)					)





def       __magic_name__    (   ):
							for i in range(len(_lowerCamelCase					)					):
														print(("""Actual output value:""", output(_lowerCamelCase  ,       """test"""					))					)
														print(("""Hypothesis output:""", calculate_hypothesis_value(_lowerCamelCase  ,       """test"""					))					)


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






"""simple docstring"""

from typing import TYPE_CHECKING

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


lowercase__			   =  {
    "configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__			   =  [
	    "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ErnieForCausalLM",
	    "ErnieForMaskedLM",
	    "ErnieForMultipleChoice",
	    "ErnieForNextSentencePrediction",
	    "ErnieForPreTraining",
	    "ErnieForQuestionAnswering",
	    "ErnieForSequenceClassification",
	    "ErnieForTokenClassification",
	    "ErnieModel",
	    "ErniePreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ernie import (
		    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ErnieForCausalLM,
		    ErnieForMaskedLM,
		    ErnieForMultipleChoice,
		    ErnieForNextSentencePrediction,
		    ErnieForPreTraining,
		    ErnieForQuestionAnswering,
		    ErnieForSequenceClassification,
		    ErnieForTokenClassification,
		    ErnieModel,
		    ErniePreTrainedModel,
		)

else:
	import sys

	lowercase__			   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

