



import numpy

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

def      lowerCAmelCase_					(  _lowercase    :       List[Any]						,       _lowercase    :       str="train")							->	Tuple:







			"""simple docstring"""
			return calculate_hypothesis_value(_lowercase						,       _lowercase) - output(
			    _lowercase						,       _lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Dict)							->	List[str]:







			"""simple docstring"""
			a__       :						int							=  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    :       Any						,       _lowercase    :       int)							->	Tuple:







			"""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      lowerCAmelCase_					(  _lowercase    :       Optional[int]						,       _lowercase    :       Any)							->	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      lowerCAmelCase_					(  _lowercase    :       int						,       _lowercase    :       Union[str, Any]=m)							->	int:







			"""simple docstring"""
			a__       :						Optional[Any]							=  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    :       Tuple)							->	Any:







			"""simple docstring"""
			a__       :						Tuple							=  summation_of_cost_derivative(_lowercase						,       _lowercase) / m
			return cost_derivative_value

def      lowerCAmelCase_					(  )							->	str:







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

def      lowerCAmelCase_					(  )							->	List[Any]:







			"""simple docstring"""
			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()









import torch

from diffusers import StableDiffusionPipeline


_lowercase		:				Optional[int]      		="path-to-your-trained-model"
_lowercase		:				Union[str, Any]      		=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

_lowercase		:				Any      		="A photo of sks dog in a bucket"
_lowercase		:				Any      		=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")




