

import numpy

# List of input, output pairs
__UpperCAmelCase							:   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),
)
__UpperCAmelCase							:   int							=  (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
__UpperCAmelCase							:   List[Any]							=  [2, 4, 1, 5]
__UpperCAmelCase							:   Optional[Any]							=  len(train_data)
__UpperCAmelCase							:   Tuple							=  0.009





def 					lowercase_ (    __snake_case     :							int	,	__snake_case     :							int="train"      )     -> Any:





				'''simple docstring'''


				return calculate_hypothesis_value(__snake_case	,	__snake_case      ) - output(
				    __snake_case	,	__snake_case      )





def 					lowercase_ (    __snake_case     :							int      )     -> Tuple:





				'''simple docstring'''


				snake_case__      :Union[str, Any]         =   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 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Tuple      )     -> str:





				'''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 					lowercase_ (    __snake_case     :							str	,	__snake_case     :							List[str]      )     -> Union[str, Any]:





				'''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 					lowercase_ (    __snake_case     :							Union[str, Any]	,	__snake_case     :							Union[str, Any]=m      )     -> Tuple:





				'''simple docstring'''


				snake_case__      :Union[str, Any]         =   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 					lowercase_ (    __snake_case     :							List[str]      )     -> Any:





				'''simple docstring'''


				snake_case__      :str         =   summation_of_cost_derivative(__snake_case	,	__snake_case      ) / m
				return cost_derivative_value





def 					lowercase_ (    )     -> Union[str, Any]:





				'''simple docstring'''


				global parameter_vector
				# Tune these values to set a tolerance value for predicted output
				snake_case__      :Dict         =   0.0_0_0_0_0_2
				snake_case__      :List[str]         =   0
				snake_case__      :Tuple         =   0
				while True:
								j += 1
								snake_case__      :str         =   [0, 0, 0, 0]
								for i in range(0	,	len(__snake_case      )      ):
												snake_case__      :Any         =   get_cost_derivative(i - 1      )
												snake_case__      :str         =   (
												    parameter_vector[i] - LEARNING_RATE * cost_derivative
												)
								if numpy.allclose(
								    __snake_case	,	__snake_case	,	atol=__snake_case	,	rtol=__snake_case	,	):
												break
								snake_case__      :List[str]         =   temp_parameter_vector
				print(("Number of iterations:", j)      )





def 					lowercase_ (    )     -> Union[str, Any]:





				'''simple docstring'''


				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()







from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
	from .multicontrolnet import MultiControlNetModel
	from .pipeline_controlnet import StableDiffusionControlNetPipeline
	from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
	from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
	from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline