



"""simple docstring"""
import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 							lowerCamelCase_(   _a,				_a,				_a,				unittest.TestCase    ):





				'''simple docstring'''



				lowercase__   :       Tuple										= StableDiffusionInstructPixaPixPipeline
				lowercase__   :       Optional[Any]										= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
				lowercase__   :       Any										= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				lowercase__   :       Dict										= IMAGE_TO_IMAGE_IMAGE_PARAMS
				lowercase__   :       List[str]										= IMAGE_TO_IMAGE_IMAGE_PARAMS

				def 			snake_case__      (    self   ):
								torch.manual_seed(0   )
								_lowerCamelCase        =				UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       sample_size=3_2				,       in_channels=8				,       out_channels=4				,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,       cross_attention_dim=3_2				,       )
								_lowerCamelCase        =				PNDMScheduler(skip_prk_steps=snake_case_   )
								torch.manual_seed(0   )
								_lowerCamelCase        =				AutoencoderKL(
								    block_out_channels=[3_2, 6_4]				,       in_channels=3				,       out_channels=3				,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,       latent_channels=4				,       )
								torch.manual_seed(0   )
								_lowerCamelCase        =				CLIPTextConfig(
								    bos_token_id=0				,       eos_token_id=2				,       hidden_size=3_2				,       intermediate_size=3_7				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1_0_0_0				,       )
								_lowerCamelCase        =				CLIPTextModel(snake_case_   )
								_lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

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

				def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=0   ):
								_lowerCamelCase        =				floats_tensor((1, 3, 3_2, 3_2)				,       rng=random.Random(snake_case_   )   ).to(snake_case_   )
								_lowerCamelCase        =				image.cpu().permute(0				,       2				,       3				,       1   )[0]
								_lowerCamelCase        =				Image.fromarray(np.uinta(snake_case_   )   ).convert('''RGB'''   )
								if str(snake_case_   ).startswith('''mps'''   ):
												_lowerCamelCase        =				torch.manual_seed(snake_case_   )
								else:
												_lowerCamelCase        =				torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )
								_lowerCamelCase        =				{
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''image_guidance_scale''': 1,
								    '''output_type''': '''numpy''',
								}
								return inputs

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								_lowerCamelCase        =				self.get_dummy_components()
								_lowerCamelCase        =				StableDiffusionInstructPixaPixPipeline(**snake_case_   )
								_lowerCamelCase        =				sd_pipe.to(snake_case_   )
								sd_pipe.set_progress_bar_config(disable=snake_case_   )

								_lowerCamelCase        =				self.get_dummy_inputs(snake_case_   )
								_lowerCamelCase        =				sd_pipe(**snake_case_   ).images
								_lowerCamelCase        =				image[0, -3:, -3:, -1]
								assert image.shape == (1, 3_2, 3_2, 3)
								_lowerCamelCase        =				np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								_lowerCamelCase        =				self.get_dummy_components()
								_lowerCamelCase        =				StableDiffusionInstructPixaPixPipeline(**snake_case_   )
								_lowerCamelCase        =				sd_pipe.to(snake_case_   )
								sd_pipe.set_progress_bar_config(disable=snake_case_   )

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

								assert image.shape == (1, 3_2, 3_2, 3)
								_lowerCamelCase        =				np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								_lowerCamelCase        =				self.get_dummy_components()
								_lowerCamelCase        =				StableDiffusionInstructPixaPixPipeline(**snake_case_   )
								_lowerCamelCase        =				sd_pipe.to(snake_case_   )
								sd_pipe.set_progress_bar_config(disable=snake_case_   )

								_lowerCamelCase        =				self.get_dummy_inputs(snake_case_   )
								_lowerCamelCase        =				[inputs['''prompt''']] * 2

								_lowerCamelCase        =				np.array(inputs['''image''']   ).astype(np.floataa   ) / 2_5_5.0
								_lowerCamelCase        =				torch.from_numpy(snake_case_   ).unsqueeze(0   ).to(snake_case_   )
								_lowerCamelCase        =				image / 2 + 0.5
								_lowerCamelCase        =				image.permute(0				,       3				,       1				,       2   )
								_lowerCamelCase        =				image.repeat(2				,       1				,       1				,       1   )

								_lowerCamelCase        =				sd_pipe(**snake_case_   ).images
								_lowerCamelCase        =				image[-1, -3:, -3:, -1]

								assert image.shape == (2, 3_2, 3_2, 3)
								_lowerCamelCase        =				np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								_lowerCamelCase        =				self.get_dummy_components()
								_lowerCamelCase        =				EulerAncestralDiscreteScheduler(
								    beta_start=0.0_0_0_8_5				,       beta_end=0.0_1_2				,       beta_schedule='''scaled_linear'''   )
								_lowerCamelCase        =				StableDiffusionInstructPixaPixPipeline(**snake_case_   )
								_lowerCamelCase        =				sd_pipe.to(snake_case_   )
								sd_pipe.set_progress_bar_config(disable=snake_case_   )

								_lowerCamelCase        =				self.get_dummy_inputs(snake_case_   )
								_lowerCamelCase        =				sd_pipe(**snake_case_   ).images
								_lowerCamelCase        =				image[0, -3:, -3:, -1]

								_lowerCamelCase        =				[round(snake_case_				,       4   ) for x in image_slice.flatten().tolist()]
								print(''','''.join([str(snake_case_   ) for x in slice]   )   )

								assert image.shape == (1, 3_2, 3_2, 3)
								_lowerCamelCase        =				np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3

				def 			snake_case__      (    self   ):
								super().test_inference_batch_single_identical(expected_max_diff=3e-3   )





				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.get_dummy_components()
								_lowerCamelCase        =				StableDiffusionInstructPixaPixPipeline(**snake_case_   )
								_lowerCamelCase        =				VaeImageProcessor(do_resize=snake_case_				,       do_normalize=snake_case_   )
								_lowerCamelCase        =				pipe.to(snake_case_   )
								pipe.set_progress_bar_config(disable=snake_case_   )

								_lowerCamelCase        =				pipe(**self.get_dummy_inputs_by_type(snake_case_				,       input_image_type='''pt'''   )   )[0]

								_lowerCamelCase        =				components['''vae''']
								_lowerCamelCase        =				self.get_dummy_inputs_by_type(snake_case_				,       input_image_type='''pt'''   )

								for image_param in self.image_latents_params:
												if image_param in inputs.keys():
																_lowerCamelCase        =				vae.encode(inputs[image_param]   ).latent_dist.mode()

								_lowerCamelCase        =				pipe(**snake_case_   )[0]

								_lowerCamelCase        =				np.abs(out - out_latents_inputs   ).max()
								self.assertLess(snake_case_				,       1e-4				,       '''passing latents as image input generate different result from passing image'''   )


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





				'''simple docstring'''



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

				def 			snake_case__      (    self				,       lowerCamelCase__=0   ):
								_lowerCamelCase        =				torch.manual_seed(snake_case_   )
								_lowerCamelCase        =				load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''   )
								_lowerCamelCase        =				{
								    '''prompt''': '''turn him into a cyborg''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 3,
								    '''guidance_scale''': 7.5,
								    '''image_guidance_scale''': 1.0,
								    '''output_type''': '''numpy''',
								}
								return inputs

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				,       safety_checker=snake_case_   )
								pipe.to(snake_case_   )
								pipe.set_progress_bar_config(disable=snake_case_   )
								pipe.enable_attention_slicing()

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

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								_lowerCamelCase        =				np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]   )

								assert np.abs(expected_slice - image_slice   ).max() < 1e-3

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				,       safety_checker=snake_case_   )
								_lowerCamelCase        =				LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
								pipe.to(snake_case_   )
								pipe.set_progress_bar_config(disable=snake_case_   )
								pipe.enable_attention_slicing()

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

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								_lowerCamelCase        =				np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]   )

								assert np.abs(expected_slice - image_slice   ).max() < 1e-3

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				,       safety_checker=snake_case_   )
								_lowerCamelCase        =				DDIMScheduler.from_config(pipe.scheduler.config   )
								pipe.to(snake_case_   )
								pipe.set_progress_bar_config(disable=snake_case_   )
								pipe.enable_attention_slicing()

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

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								_lowerCamelCase        =				np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]   )

								assert np.abs(expected_slice - image_slice   ).max() < 1e-3

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				0

								def callback_fn(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ) -> None:
												_lowerCamelCase        =				True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
																_lowerCamelCase        =				latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 6_4, 6_4)
																_lowerCamelCase        =				latents[0, -3:, -3:, -1]
																_lowerCamelCase        =				np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]   )

																assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5e-2
												elif step == 2:
																_lowerCamelCase        =				latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 6_4, 6_4)
																_lowerCamelCase        =				latents[0, -3:, -3:, -1]
																_lowerCamelCase        =				np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]   )

																assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5e-2

								_lowerCamelCase        =				False

								_lowerCamelCase        =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				,       safety_checker=snake_case_				,       torch_dtype=torch.floataa   )
								_lowerCamelCase        =				pipe.to(snake_case_   )
								pipe.set_progress_bar_config(disable=snake_case_   )
								pipe.enable_attention_slicing()

								_lowerCamelCase        =				self.get_inputs()
								pipe(**snake_case_				,       callback=snake_case_				,       callback_steps=1   )
								assert callback_fn.has_been_called
								assert number_of_steps == 3

				def 			snake_case__      (    self   ):
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								_lowerCamelCase        =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				,       safety_checker=snake_case_				,       torch_dtype=torch.floataa   )
								_lowerCamelCase        =				pipe.to(snake_case_   )
								pipe.set_progress_bar_config(disable=snake_case_   )
								pipe.enable_attention_slicing(1   )
								pipe.enable_sequential_cpu_offload()

								_lowerCamelCase        =				self.get_inputs()
								_lowerCamelCase        =				pipe(**snake_case_   )

								_lowerCamelCase        =				torch.cuda.max_memory_allocated()
								# make sure that less than 2.2 GB is allocated
								assert mem_bytes < 2.2 * 1_0**9





				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.get_inputs()
								# resize to resolution that is divisible by 8 but not 16 or 32
								_lowerCamelCase        =				inputs['''image'''].resize((5_0_4, 5_0_4)   )

								_lowerCamelCase        =				'''timbrooks/instruct-pix2pix'''
								_lowerCamelCase        =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    snake_case_				,       safety_checker=snake_case_				,       )
								pipe.to(snake_case_   )
								pipe.set_progress_bar_config(disable=snake_case_   )
								pipe.enable_attention_slicing()

								_lowerCamelCase        =				pipe(**snake_case_   )
								_lowerCamelCase        =				output.images[0]

								_lowerCamelCase        =				image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

								assert image.shape == (5_0_4, 5_0_4, 3)
								_lowerCamelCase        =				np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-3





"""simple docstring"""
import numpy as np



def        lowerCAmelCase_(   lowercase_     :							np.ndarray						,   lowercase_     :							np.ndarray						,   lowercase_     :							float = 1e-12						,   lowercase_     :							int = 1_00						,   )	->       tuple[float, np.ndarray]:
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[1]
				# Ensure proper dimensionality.
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(lowercase_							) == np.iscomplexobj(lowercase_							)
				_lowerCamelCase        =				np.iscomplexobj(lowercase_							)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(lowercase_						,   input_matrix.conj().T							)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				_lowerCamelCase        =				False
				_lowerCamelCase        =				0
				_lowerCamelCase        =				0
				_lowerCamelCase        =				1e12

				while not convergence:
								# Multiple matrix by the vector.
								_lowerCamelCase        =				np.dot(lowercase_						,   lowercase_							)
								# Normalize the resulting output vector.
								_lowerCamelCase        =				w / np.linalg.norm(lowercase_							)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								_lowerCamelCase        =				vector.conj().T if is_complex else vector.T
								_lowerCamelCase        =				np.dot(lowercase_						,   np.dot(lowercase_						,   lowercase_							)							)

								# Check convergence.
								_lowerCamelCase        =				np.abs(lambda_ - lambda_previous							) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												_lowerCamelCase        =				True

								_lowerCamelCase        =				lambda_

				if is_complex:
								_lowerCamelCase        =				np.real(lambda_							)

				return lambda_, vector



def        lowerCAmelCase_(   )	->       None:
				_lowerCamelCase        =				np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
				_lowerCamelCase        =				np.array([41, 4, 20]							)
				_lowerCamelCase        =				real_input_matrix.astype(np.complexaaa							)
				_lowerCamelCase        =				np.triu(1j * complex_input_matrix						,   1							)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				_lowerCamelCase        =				np.array([41, 4, 20]							).astype(np.complexaaa							)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												_lowerCamelCase        =				real_input_matrix
												_lowerCamelCase        =				real_vector
								elif problem_type == "complex":
												_lowerCamelCase        =				complex_input_matrix
												_lowerCamelCase        =				complex_vector

								# Our implementation.
								_lowerCamelCase  ,     _lowerCamelCase        =				power_iteration(lowercase_						,   lowercase_							)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								_lowerCamelCase  ,     _lowerCamelCase        =				np.linalg.eigh(lowercase_							)
								# Last eigenvalue is the maximum one.
								_lowerCamelCase        =				eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								_lowerCamelCase        =				eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max							) <= 1e-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(lowercase_							) - np.abs(lowercase_							)							) <= 1e-6


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			test_power_iteration()
