
'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_snake_case				:   Union[str, Any]     =				False


class 			lowerCAmelCase (				unittest.TestCase			):
		pass


@nightly
@require_torch_gpu
class 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self      ):
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()




		def     lowercase       (				self      ):
					_SCREAMING_SNAKE_CASE				= VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"      )
					# remove text_unet
					pipe.remove_unused_weights()
					pipe.to(_UpperCamelCase      )
					pipe.set_progress_bar_config(disable=_UpperCamelCase      )

					_SCREAMING_SNAKE_CASE				= """A painting of a squirrel eating a burger """
					_SCREAMING_SNAKE_CASE				= torch.manual_seed(0      )
					_SCREAMING_SNAKE_CASE				= pipe(
					    prompt=_UpperCamelCase					,						generator=_UpperCamelCase					,						guidance_scale=7.5					,						num_inference_steps=2					,						output_type="numpy"      ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
								pipe.save_pretrained(_UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= VersatileDiffusionTextToImagePipeline.from_pretrained(_UpperCamelCase      )
					pipe.to(_UpperCamelCase      )
					pipe.set_progress_bar_config(disable=_UpperCamelCase      )

					_SCREAMING_SNAKE_CASE				= generator.manual_seed(0      )
					_SCREAMING_SNAKE_CASE				= pipe(
					    prompt=_UpperCamelCase					,						generator=_UpperCamelCase					,						guidance_scale=7.5					,						num_inference_steps=2					,						output_type="numpy"      ).images

					assert np.abs(image - new_image      ).sum() < 1e-5, "Models don't have the same forward pass"




		def     lowercase       (				self      ):
					_SCREAMING_SNAKE_CASE				= VersatileDiffusionTextToImagePipeline.from_pretrained(
					    "shi-labs/versatile-diffusion"					,						torch_dtype=torch.floataa      )
					pipe.to(_UpperCamelCase      )
					pipe.set_progress_bar_config(disable=_UpperCamelCase      )

					_SCREAMING_SNAKE_CASE				= """A painting of a squirrel eating a burger """
					_SCREAMING_SNAKE_CASE				= torch.manual_seed(0      )
					_SCREAMING_SNAKE_CASE				= pipe(
					    prompt=_UpperCamelCase					,						generator=_UpperCamelCase					,						guidance_scale=7.5					,						num_inference_steps=50					,						output_type="numpy"      ).images

					_SCREAMING_SNAKE_CASE				= image[0, 253:256, 253:256, -1]

					assert image.shape == (1, 512, 512, 3)
					_SCREAMING_SNAKE_CASE				= np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]      )

					assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2

'''simple docstring'''







import numpy

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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   List[Any]="train"  ):
						return calculate_hypothesis_value(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) - output(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						_SCREAMING_SNAKE_CASE				= 0
						for i in range(len(_SCREAMING_SNAKE_CASE  ) - 1  ):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]  ):
						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None



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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						_SCREAMING_SNAKE_CASE				= summation_of_cost_derivative(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) / m
						return cost_derivative_value



def 			_a		(	):
						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						_SCREAMING_SNAKE_CASE				= 0.000_002
						_SCREAMING_SNAKE_CASE				= 0
						_SCREAMING_SNAKE_CASE				= 0
						while True:
												j += 1
												_SCREAMING_SNAKE_CASE				= [0, 0, 0, 0]
												for i in range(0	,			len(_SCREAMING_SNAKE_CASE  )  ):
																		_SCREAMING_SNAKE_CASE				= get_cost_derivative(i - 1  )
																		_SCREAMING_SNAKE_CASE				= (
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			atol=_SCREAMING_SNAKE_CASE	,			rtol=_SCREAMING_SNAKE_CASE	,			):
																		break
												_SCREAMING_SNAKE_CASE				= temp_parameter_vector
						print(("Number of iterations:", j)  )



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


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