





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCAmelCase_   				=  False
@nightly
@require_torch_gpu
class       UpperCamelCase_		(	unittest.TestCase       ):




					def 						lowerCAmelCase      (		self	)		->				str:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




					def 						lowerCAmelCase      (		self	)		->				Union[str, Any]:
										_snake_case						=					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'	)
										# remove text_unet
										pipe.remove_unused_weights()
										pipe.to(lowerCAmelCase_	)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_snake_case						=					load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)

										_snake_case						=					torch.manual_seed(0	)
										_snake_case						=					pipe(
										    prompt='first prompt'					,    image=lowerCAmelCase_					,    text_to_image_strength=0.75					,    generator=lowerCAmelCase_					,    guidance_scale=7.5					,    num_inference_steps=2					,    output_type='numpy'					,    ).images

										with tempfile.TemporaryDirectory() as tmpdirname:
															pipe.save_pretrained(lowerCAmelCase_	)
															_snake_case						=					VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCAmelCase_	)

										pipe.to(lowerCAmelCase_	)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_snake_case						=					generator.manual_seed(0	)
										_snake_case						=					pipe(
										    prompt='first prompt'					,    image=lowerCAmelCase_					,    text_to_image_strength=0.75					,    generator=lowerCAmelCase_					,    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 						lowerCAmelCase      (		self	)		->				Any:
										_snake_case						=					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'	)
										pipe.remove_unused_weights()
										pipe.to(lowerCAmelCase_	)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

										_snake_case						=					'cyberpunk 2077'
										_snake_case						=					load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)
										_snake_case						=					torch.manual_seed(0	)
										_snake_case						=					pipe(
										    prompt=lowerCAmelCase_					,    image=lowerCAmelCase_					,    text_to_image_strength=0.75					,    generator=lowerCAmelCase_					,    guidance_scale=7.5					,    num_inference_steps=50					,    output_type='numpy'					,    ).images

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

										assert image.shape == (1, 512, 512, 3)
										_snake_case						=					np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]	)

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










from typing import Any

import numpy as np





def     lowerCamelCase__  (  UpperCamelCase__      :							np.ndarray    )      ->			bool:



					'''simple docstring'''




					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[1]

					# If matrix not symmetric, exit right away.
					if np.allclose(UpperCamelCase__							,     matrix.T    ) is False:
										return False

					# Get eigenvalues and eignevectors for a symmetric matrix.
					_snake_case				,  _snake_case						=					np.linalg.eigh(UpperCamelCase__    )

					# Check sign of all eigenvalues.
					# np.all returns a value of type np.bool_
					return bool(np.all(eigen_values > 0    )    )





def     lowerCamelCase__  (  UpperCamelCase__      :							int    )      ->			Any:



					'''simple docstring'''




					_snake_case						=					np.random.randn(UpperCamelCase__							,     UpperCamelCase__    )
					_snake_case						=					np.dot(UpperCamelCase__							,     random_matrix.T    )
					assert _is_matrix_spd(UpperCamelCase__    )
					return spd_matrix





def     lowerCamelCase__  (  UpperCamelCase__      :							np.ndarray							,     UpperCamelCase__      :							np.ndarray							,     UpperCamelCase__      :							int = 1_000							,     UpperCamelCase__      :							float = 1e-8							,     )      ->			Any:



					'''simple docstring'''




					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[1]
					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[0]
					assert _is_matrix_spd(UpperCamelCase__    )

					# Initialize solution guess, residual, search direction.
					_snake_case						=					np.zeros((np.shape(UpperCamelCase__    )[0], 1)    )
					_snake_case						=					np.copy(UpperCamelCase__    )
					_snake_case						=					np.copy(UpperCamelCase__    )

					# Set initial errors in solution guess and residual.
					_snake_case						=					1e9
					_snake_case						=					1e9
					_snake_case						=					1e9

					# Set iteration counter to threshold number of iterations.
					_snake_case						=					0

					while error > tol:
										# Save this value so we only calculate the matrix-vector product once.
										_snake_case						=					np.dot(UpperCamelCase__							,     UpperCamelCase__    )

										# The main algorithm.

										# Update search direction magnitude.
										_snake_case						=					np.dot(ra.T							,     UpperCamelCase__    ) / np.dot(pa.T							,     UpperCamelCase__    )
										# Update solution guess.
										_snake_case						=					xa + alpha * pa
										# Calculate new residual.
										_snake_case						=					ra - alpha * w
										# Calculate new Krylov subspace scale.
										_snake_case						=					np.dot(r.T							,     UpperCamelCase__    ) / np.dot(ra.T							,     UpperCamelCase__    )
										# Calculate new A conjuage search direction.
										_snake_case						=					r + beta * pa

										# Calculate errors.
										_snake_case						=					np.linalg.norm(r - ra    )
										_snake_case						=					np.linalg.norm(x - xa    )
										_snake_case						=					np.maximum(UpperCamelCase__							,     UpperCamelCase__    )

										# Update variables.
										_snake_case						=					np.copy(UpperCamelCase__    )
										_snake_case						=					np.copy(UpperCamelCase__    )
										_snake_case						=					np.copy(UpperCamelCase__    )

										# Update number of iterations.
										iterations += 1
										if iterations > max_iterations:
															break

					return x





def     lowerCamelCase__  (  )      ->			None:



					'''simple docstring'''




					_snake_case						=					3
					_snake_case						=					_create_spd_matrix(UpperCamelCase__    )
					_snake_case						=					np.random.randn(UpperCamelCase__							,     1    )
					_snake_case						=					np.dot(UpperCamelCase__							,     UpperCamelCase__    )

					# Numpy solution.
					_snake_case						=					np.linalg.solve(UpperCamelCase__							,     UpperCamelCase__    )

					# Our implementation.
					_snake_case						=					conjugate_gradient(UpperCamelCase__							,     UpperCamelCase__    )

					# Ensure both solutions are close to x_true (and therefore one another).
					assert np.linalg.norm(x_numpy - x_true    ) <= 1e-6
					assert np.linalg.norm(x_conjugate_gradient - x_true    ) <= 1e-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_conjugate_gradient()



