
from typing import Any

import numpy as np




def 		A_	(							snake_case							:				np.ndarray	) ->      bool:



						'''simple docstring'''

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

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

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

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




def 		A_	(							snake_case							:				int	) ->      Any:



						'''simple docstring'''

						__UpperCamelCase       					=     np.random.randn(snake_case	, snake_case	)
						__UpperCamelCase       					=     np.dot(snake_case	, random_matrix.T	)
						assert _is_matrix_spd(snake_case	)
						return spd_matrix




def 		A_	(							snake_case							:				np.ndarray	, snake_case							:				np.ndarray	, snake_case							:				int = 1000	, snake_case							:				float = 1e-8	, ) ->      Any:



						'''simple docstring'''

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

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

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

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

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

												# The main algorithm.

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

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

												# Update variables.
												__UpperCamelCase       					=     np.copy(snake_case	)
												__UpperCamelCase       					=     np.copy(snake_case	)
												__UpperCamelCase       					=     np.copy(snake_case	)

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

						return x




def 		A_	(							) ->      None:



						'''simple docstring'''

						__UpperCamelCase       					=     3
						__UpperCamelCase       					=     _create_spd_matrix(snake_case	)
						__UpperCamelCase       					=     np.random.randn(snake_case	, 1	)
						__UpperCamelCase       					=     np.dot(snake_case	, snake_case	)

						# Numpy solution.
						__UpperCamelCase       					=     np.linalg.solve(snake_case	, snake_case	)

						# Our implementation.
						__UpperCamelCase       					=     conjugate_gradient(snake_case	, snake_case	)

						# 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()






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						unittest.TestCase      ):

							"""simple docstring"""






							_snake_case       						=					StableDiffusionParadigmsPipeline
							_snake_case       						=					TEXT_TO_IMAGE_PARAMS
							_snake_case       						=					TEXT_TO_IMAGE_BATCH_PARAMS
							_snake_case       						=					TEXT_TO_IMAGE_IMAGE_PARAMS
							_snake_case       						=					TEXT_TO_IMAGE_IMAGE_PARAMS



							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													torch.manual_seed(0     )
													__UpperCamelCase       					=     UNetaDConditionModel(
													    block_out_channels=(32, 64)       ,		layers_per_block=2       ,		sample_size=32       ,		in_channels=4       ,		out_channels=4       ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,		cross_attention_dim=32       ,		attention_head_dim=(2, 4)       ,		use_linear_projection=SCREAMING_SNAKE_CASE_       ,		)
													__UpperCamelCase       					=     DDIMParallelScheduler(
													    beta_start=0.0_0_0_8_5       ,		beta_end=0.0_1_2       ,		beta_schedule='''scaled_linear'''       ,		clip_sample=SCREAMING_SNAKE_CASE_       ,		set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,		)
													torch.manual_seed(0     )
													__UpperCamelCase       					=     AutoencoderKL(
													    block_out_channels=[32, 64]       ,		in_channels=3       ,		out_channels=3       ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,		latent_channels=4       ,		sample_size=128       ,		)
													torch.manual_seed(0     )
													__UpperCamelCase       					=     CLIPTextConfig(
													    bos_token_id=0       ,		eos_token_id=2       ,		hidden_size=32       ,		intermediate_size=37       ,		layer_norm_eps=1E-05       ,		num_attention_heads=4       ,		num_hidden_layers=5       ,		pad_token_id=1       ,		vocab_size=1000       ,		hidden_act='''gelu'''       ,		projection_dim=512       ,		)
													__UpperCamelCase       					=     CLIPTextModel(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

													__UpperCamelCase       					=     {
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''vae''': vae,
													    '''text_encoder''': text_encoder,
													    '''tokenizer''': tokenizer,
													    '''safety_checker''': None,
													    '''feature_extractor''': None,
													}
													return components



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=0     )->				List[Any]:





													'''simple docstring'''






													if str(SCREAMING_SNAKE_CASE_     ).startswith('''mps'''     ):
																			__UpperCamelCase       					=     torch.manual_seed(SCREAMING_SNAKE_CASE_     )
													else:
																			__UpperCamelCase       					=     torch.Generator(device=SCREAMING_SNAKE_CASE_     ).manual_seed(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {
													    '''prompt''': '''a photograph of an astronaut riding a horse''',
													    '''generator''': generator,
													    '''num_inference_steps''': 10,
													    '''guidance_scale''': 6.0,
													    '''output_type''': '''numpy''',
													    '''parallel''': 3,
													    '''debug''': True,
													}
													return inputs



							def       A__						(					self     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
													__UpperCamelCase       					=     self.get_dummy_components()
													__UpperCamelCase       					=     StableDiffusionParadigmsPipeline(**SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     sd_pipe.to(SCREAMING_SNAKE_CASE_     )
													sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     sd_pipe(**SCREAMING_SNAKE_CASE_     ).images
													__UpperCamelCase       					=     image[0, -3:, -3:, -1]
													assert image.shape == (1, 64, 64, 3)

													__UpperCamelCase       					=     np.array([0.4_7_7_3, 0.5_4_1_7, 0.4_7_2_3, 0.4_9_2_5, 0.5_6_3_1, 0.4_7_5_2, 0.5_2_4_0, 0.4_9_3_5, 0.5_0_2_3]     )

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



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
													__UpperCamelCase       					=     self.get_dummy_components()
													torch.manual_seed(0     )
													__UpperCamelCase       					=     DDPMParallelScheduler()
													torch.manual_seed(0     )
													__UpperCamelCase       					=     StableDiffusionParadigmsPipeline(**SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     sd_pipe.to(SCREAMING_SNAKE_CASE_     )
													sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     sd_pipe(**SCREAMING_SNAKE_CASE_     ).images
													__UpperCamelCase       					=     image[0, -3:, -3:, -1]
													assert image.shape == (1, 64, 64, 3)

													__UpperCamelCase       					=     np.array([0.3_5_7_3, 0.4_4_2_0, 0.4_9_6_0, 0.4_7_9_9, 0.3_7_9_6, 0.3_8_7_9, 0.4_8_1_9, 0.4_3_6_5, 0.4_4_6_8]     )

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



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





													'''simple docstring'''






													super().test_inference_batch_consistent(batch_sizes=[1, 2]     )



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





													'''simple docstring'''






													super().test_inference_batch_single_identical(batch_size=2       ,		expected_max_diff=3E-3     )



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
													__UpperCamelCase       					=     self.get_dummy_components()
													__UpperCamelCase       					=     StableDiffusionParadigmsPipeline(**SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     sd_pipe.to(SCREAMING_SNAKE_CASE_     )
													sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     '''french fries'''
													__UpperCamelCase       					=     sd_pipe(**SCREAMING_SNAKE_CASE_       ,		negative_prompt=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     output.images
													__UpperCamelCase       					=     image[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													__UpperCamelCase       					=     np.array([0.4_7_7_1, 0.5_4_2_0, 0.4_6_8_3, 0.4_9_1_8, 0.5_6_3_6, 0.4_7_2_5, 0.5_2_3_0, 0.4_9_2_3, 0.5_0_1_5]     )

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






@slow
@require_torch_gpu
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							def       A__						(					self     )->				Any:





													'''simple docstring'''






													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_=0     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     torch.Generator(device=SCREAMING_SNAKE_CASE_     ).manual_seed(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {
													    '''prompt''': '''a photograph of an astronaut riding a horse''',
													    '''generator''': generator,
													    '''num_inference_steps''': 10,
													    '''guidance_scale''': 7.5,
													    '''output_type''': '''numpy''',
													    '''parallel''': 3,
													    '''debug''': True,
													}
													return inputs



							def       A__						(					self     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     '''stabilityai/stable-diffusion-2-base'''
													__UpperCamelCase       					=     DDIMParallelScheduler.from_pretrained(SCREAMING_SNAKE_CASE_       ,		subfolder='''scheduler'''     )
													__UpperCamelCase       					=     StableDiffusionParadigmsPipeline.from_pretrained(SCREAMING_SNAKE_CASE_       ,		scheduler=SCREAMING_SNAKE_CASE_       ,		safety_checker=SCREAMING_SNAKE_CASE_     )
													pipe.to(SCREAMING_SNAKE_CASE_     )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_     )
													pipe.enable_attention_slicing()

													__UpperCamelCase       					=     self.get_inputs()
													__UpperCamelCase       					=     pipe(**SCREAMING_SNAKE_CASE_     ).images
													__UpperCamelCase       					=     image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 512, 512, 3)

													__UpperCamelCase       					=     np.array([0.9_6_2_2, 0.9_6_0_2, 0.9_7_4_8, 0.9_5_9_1, 0.9_6_3_0, 0.9_6_9_1, 0.9_6_6_1, 0.9_6_3_1, 0.9_7_4_1]     )

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




