





"""simple docstring"""



import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






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





 """simple docstring"""


 _lowerCamelCase								=	StableDiffusionDiffEditPipeline
 _lowerCamelCase								=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
 _lowerCamelCase								=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
 _lowerCamelCase								=	frozenset(
     []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 _lowerCamelCase								=	frozenset([]	)

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     torch.manual_seed(0    )
     __A       :		List[Any]							=	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=snake_case__   ,		)
     __A       :		int							=	DDIMScheduler(
         beta_start=0.0_0_0_8_5   ,		beta_end=0.0_1_2   ,		beta_schedule='''scaled_linear'''   ,		clip_sample=snake_case__   ,		set_alpha_to_one=snake_case__   ,		)
     __A       :		Tuple							=	DDIMInverseScheduler(
         beta_start=0.0_0_0_8_5   ,		beta_end=0.0_1_2   ,		beta_schedule='''scaled_linear'''   ,		clip_sample=snake_case__   ,		set_alpha_to_zero=snake_case__   ,		)
     torch.manual_seed(0    )
     __A       :		Optional[Any]							=	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    )
     __A       :		Any							=	CLIPTextConfig(
         bos_token_id=0   ,		eos_token_id=2   ,		hidden_size=32   ,		intermediate_size=37   ,		layer_norm_eps=1e-0_5   ,		num_attention_heads=4   ,		num_hidden_layers=5   ,		pad_token_id=1   ,		vocab_size=1000   ,		hidden_act='''gelu'''   ,		projection_dim=512   ,		)
     __A       :		List[str]							=	CLIPTextModel(snake_case__    )
     __A       :		int							=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

     __A       :		int							=	{
         "unet": unet,
         "scheduler": scheduler,
         "inverse_scheduler": inverse_scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }

     return components

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=0    ):



     '''simple docstring'''




     __A       :		int							=	floats_tensor((1, 16, 16)   ,		rng=random.Random(snake_case__    )    ).to(snake_case__    )
     __A       :		Union[str, Any]							=	floats_tensor((1, 2, 4, 16, 16)   ,		rng=random.Random(snake_case__    )    ).to(snake_case__    )
     if str(snake_case__    ).startswith('''mps'''    ):
         __A       :		Optional[Any]							=	torch.manual_seed(snake_case__    )
     else:
         __A       :		Tuple							=	torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
     __A       :		Any							=	{
         "prompt": "a dog and a newt",
         "mask_image": mask,
         "image_latents": latents,
         "generator": generator,
         "num_inference_steps": 2,
         "inpaint_strength": 1.0,
         "guidance_scale": 6.0,
         "output_type": "numpy",
     }

     return inputs

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=0    ):



     '''simple docstring'''




     __A       :		Dict							=	floats_tensor((1, 3, 32, 32)   ,		rng=random.Random(snake_case__    )    ).to(snake_case__    )
     __A       :		Optional[int]							=	image.cpu().permute(0   ,		2   ,		3   ,		1    )[0]
     __A       :		List[Any]							=	Image.fromarray(np.uinta(snake_case__    )    ).convert('''RGB'''    )
     if str(snake_case__    ).startswith('''mps'''    ):
         __A       :		Tuple							=	torch.manual_seed(snake_case__    )
     else:
         __A       :		List[str]							=	torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
     __A       :		Optional[Any]							=	{
         "image": image,
         "source_prompt": "a cat and a frog",
         "target_prompt": "a dog and a newt",
         "generator": generator,
         "num_inference_steps": 2,
         "num_maps_per_mask": 2,
         "mask_encode_strength": 1.0,
         "guidance_scale": 6.0,
         "output_type": "numpy",
     }

     return inputs

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=0    ):



     '''simple docstring'''




     __A       :		Any							=	floats_tensor((1, 3, 32, 32)   ,		rng=random.Random(snake_case__    )    ).to(snake_case__    )
     __A       :		Tuple							=	image.cpu().permute(0   ,		2   ,		3   ,		1    )[0]
     __A       :		Any							=	Image.fromarray(np.uinta(snake_case__    )    ).convert('''RGB'''    )
     if str(snake_case__    ).startswith('''mps'''    ):
         __A       :		int							=	torch.manual_seed(snake_case__    )
     else:
         __A       :		Optional[Any]							=	torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
     __A       :		List[Any]							=	{
         "image": image,
         "prompt": "a cat and a frog",
         "generator": generator,
         "num_inference_steps": 2,
         "inpaint_strength": 1.0,
         "guidance_scale": 6.0,
         "decode_latents": True,
         "output_type": "numpy",
     }
     return inputs

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     if not hasattr(self.pipeline_class   ,		'''_optional_components'''    ):
         return

     __A       :		List[str]							=	self.get_dummy_components()
     __A       :		Union[str, Any]							=	self.pipeline_class(**snake_case__    )
     pipe.to(snake_case__    )
     pipe.set_progress_bar_config(disable=snake_case__    )

     # set all optional components to None and update pipeline config accordingly
     for optional_component in pipe._optional_components:
         setattr(snake_case__   ,		snake_case__   ,		snake_case__    )
     pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}    )

     __A       :		List[str]							=	self.get_dummy_inputs(snake_case__    )
     __A       :		Optional[Any]							=	pipe(**snake_case__    )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
         pipe.save_pretrained(snake_case__    )
         __A       :		Optional[Any]							=	self.pipeline_class.from_pretrained(snake_case__    )
         pipe_loaded.to(snake_case__    )
         pipe_loaded.set_progress_bar_config(disable=snake_case__    )

     for optional_component in pipe._optional_components:
         self.assertTrue(
             getattr(snake_case__   ,		snake_case__    ) is None   ,		F"""`{optional_component}` did not stay set to None after loading."""   ,		)

     __A       :		Union[str, Any]							=	self.get_dummy_inputs(snake_case__    )
     __A       :		List[Any]							=	pipe_loaded(**snake_case__    )[0]

     __A       :		int							=	np.abs(output - output_loaded    ).max()
     self.assertLess(snake_case__   ,		1e-4    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[str]							=	"cpu"

     __A       :		Optional[int]							=	self.get_dummy_components()
     __A       :		Optional[Any]							=	self.pipeline_class(**snake_case__    )
     pipe.to(snake_case__    )
     pipe.set_progress_bar_config(disable=snake_case__    )

     __A       :		Tuple							=	self.get_dummy_mask_inputs(snake_case__    )
     __A       :		Any							=	pipe.generate_mask(**snake_case__    )
     __A       :		List[str]							=	mask[0, -3:, -3:]

     self.assertEqual(mask.shape   ,		(1, 16, 16)    )
     __A       :		Optional[Any]							=	np.array([0] * 9    )
     __A       :		Optional[int]							=	np.abs(mask_slice.flatten() - expected_slice    ).max()
     self.assertLessEqual(snake_case__   ,		1e-3    )
     self.assertEqual(mask[0, -3, -4]   ,		0    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Union[str, Any]							=	"cpu"

     __A       :		int							=	self.get_dummy_components()
     __A       :		List[str]							=	self.pipeline_class(**snake_case__    )
     pipe.to(snake_case__    )
     pipe.set_progress_bar_config(disable=snake_case__    )

     __A       :		Union[str, Any]							=	self.get_dummy_inversion_inputs(snake_case__    )
     __A       :		str							=	pipe.invert(**snake_case__    ).images
     __A       :		Union[str, Any]							=	image[0, -1, -3:, -3:]

     self.assertEqual(image.shape   ,		(2, 32, 32, 3)    )
     __A       :		List[Any]							=	np.array(
         [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]   ,		)
     __A       :		Optional[int]							=	np.abs(image_slice.flatten() - expected_slice    ).max()
     self.assertLessEqual(snake_case__   ,		1e-3    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     super().test_inference_batch_single_identical(expected_max_diff=5e-3    )







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Tuple							=	"cpu"

     __A       :		List[Any]							=	self.get_dummy_components()

     __A       :		List[Any]							=	{"beta_start": 0.0_0_0_8_5, "beta_end": 0.0_1_2, "beta_schedule": "scaled_linear"}
     __A       :		Optional[Any]							=	DPMSolverMultistepScheduler(**snake_case__    )
     __A       :		str							=	DPMSolverMultistepInverseScheduler(**snake_case__    )

     __A       :		List[Any]							=	self.pipeline_class(**snake_case__    )
     pipe.to(snake_case__    )
     pipe.set_progress_bar_config(disable=snake_case__    )

     __A       :		List[Any]							=	self.get_dummy_inversion_inputs(snake_case__    )
     __A       :		Optional[int]							=	pipe.invert(**snake_case__    ).images
     __A       :		Dict							=	image[0, -1, -3:, -3:]

     self.assertEqual(image.shape   ,		(2, 32, 32, 3)    )
     __A       :		int							=	np.array(
         [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]   ,		)
     __A       :		Dict							=	np.abs(image_slice.flatten() - expected_slice    ).max()
     self.assertLessEqual(snake_case__   ,		1e-3    )






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





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




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

 @classmethod
 def    UpperCamelCase__(       cls    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''    )

     __A       :		str							=	raw_image.convert('''RGB'''    ).resize((768, 768)    )

     __A       :		Optional[Any]							=	raw_image

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[str]							=	torch.manual_seed(0    )

     __A       :		Dict							=	StableDiffusionDiffEditPipeline.from_pretrained(
         '''stabilityai/stable-diffusion-2-1'''   ,		safety_checker=snake_case__   ,		torch_dtype=torch.floataa    )
     __A       :		Union[str, Any]							=	DDIMScheduler.from_config(pipe.scheduler.config    )
     __A       :		Optional[Any]							=	DDIMInverseScheduler.from_config(pipe.scheduler.config    )
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=snake_case__    )

     __A       :		Optional[Any]							=	"a bowl of fruit"
     __A       :		Optional[int]							=	"a bowl of pears"

     __A       :		Union[str, Any]							=	pipe.generate_mask(
         image=self.raw_image   ,		source_prompt=snake_case__   ,		target_prompt=snake_case__   ,		generator=snake_case__   ,		)

     __A       :		str							=	pipe.invert(
         prompt=snake_case__   ,		image=self.raw_image   ,		inpaint_strength=0.7   ,		generator=snake_case__    ).latents

     __A       :		Union[str, Any]							=	pipe(
         prompt=snake_case__   ,		mask_image=snake_case__   ,		image_latents=snake_case__   ,		generator=snake_case__   ,		negative_prompt=snake_case__   ,		inpaint_strength=0.7   ,		output_type='''numpy'''   ,		).images[0]

     __A       :		Tuple							=	(
         np.array(
             load_image(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/diffedit/pears.png'''    ).resize((768, 768)    )    )
         / 255
     )
     assert np.abs((expected_image - image).max()    ) < 5e-1







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		int							=	torch.manual_seed(0    )

     __A       :		Union[str, Any]							=	StableDiffusionDiffEditPipeline.from_pretrained(
         '''stabilityai/stable-diffusion-2-1'''   ,		safety_checker=snake_case__   ,		torch_dtype=torch.floataa    )
     __A       :		List[str]							=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
     __A       :		List[Any]							=	DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=snake_case__    )

     __A       :		Optional[int]							=	"a bowl of fruit"
     __A       :		Union[str, Any]							=	"a bowl of pears"

     __A       :		List[Any]							=	pipe.generate_mask(
         image=self.raw_image   ,		source_prompt=snake_case__   ,		target_prompt=snake_case__   ,		generator=snake_case__   ,		)

     __A       :		Optional[Any]							=	pipe.invert(
         prompt=snake_case__   ,		image=self.raw_image   ,		inpaint_strength=0.7   ,		generator=snake_case__   ,		num_inference_steps=25   ,		).latents

     __A       :		Optional[int]							=	pipe(
         prompt=snake_case__   ,		mask_image=snake_case__   ,		image_latents=snake_case__   ,		generator=snake_case__   ,		negative_prompt=snake_case__   ,		inpaint_strength=0.7   ,		num_inference_steps=25   ,		output_type='''numpy'''   ,		).images[0]

     __A       :		List[str]							=	(
         np.array(
             load_image(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/diffedit/pears.png'''    ).resize((768, 768)    )    )
         / 255
     )
     assert np.abs((expected_image - image).max()    ) < 5e-1












"""simple docstring"""



import numpy as np


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : np.ndarray			,					snake_case       : np.ndarray			,					snake_case       : float = 1E-1_2			,					snake_case       : int = 100			,					)->		tuple[float, np.ndarray]:






     '''simple docstring'''



     assert np.shape(snake_case				)[0] == np.shape(snake_case				)[1]
     # Ensure proper dimensionality.
     assert np.shape(snake_case				)[0] == np.shape(snake_case				)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(snake_case				) == np.iscomplexobj(snake_case				)
     UpperCAmelCase__						:    str							=							np.iscomplexobj(snake_case				)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(snake_case			,					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.

     UpperCAmelCase__						:    Union[str, Any]							=							False
     UpperCAmelCase__						:    List[Any]							=							0
     UpperCAmelCase__						:    Any							=							0
     UpperCAmelCase__						:    List[str]							=							1E1_2

     while not convergence:
          # Multiple matrix by the vector.
          UpperCAmelCase__						:    Optional[Any]							=							np.dot(snake_case			,					snake_case				)
          # Normalize the resulting output vector.
          UpperCAmelCase__						:    Tuple							=							w / np.linalg.norm(snake_case				)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          UpperCAmelCase__						:    Any							=							vector.conj().T if is_complex else vector.T
          UpperCAmelCase__						:    List[str]							=							np.dot(snake_case			,					np.dot(snake_case			,					snake_case				)				)

          # Check convergence.
          UpperCAmelCase__						:    Optional[Any]							=							np.abs(lambda_ - lambda_previous				) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               UpperCAmelCase__						:    Tuple							=							True

          UpperCAmelCase__						:    Tuple							=							lambda_

     if is_complex:
          UpperCAmelCase__						:    str							=							np.real(lambda_				)

     return lambda_, vector


def 	SCREAMING_SNAKE_CASE__       (  )->		None:






     '''simple docstring'''



     UpperCAmelCase__						:    List[Any]							=							np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]				)
     UpperCAmelCase__						:    Dict							=							np.array([41, 4, 20]				)
     UpperCAmelCase__						:    Union[str, Any]							=							real_input_matrix.astype(np.complexaaa				)
     UpperCAmelCase__						:    Optional[int]							=							np.triu(1J * complex_input_matrix			,					1				)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     UpperCAmelCase__						:    int							=							np.array([41, 4, 20]				).astype(np.complexaaa				)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               UpperCAmelCase__						:    List[str]							=							real_input_matrix
               UpperCAmelCase__						:    Any							=							real_vector
          elif problem_type == "complex":
               UpperCAmelCase__						:    str							=							complex_input_matrix
               UpperCAmelCase__						:    List[Any]							=							complex_vector

          # Our implementation.
          UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							power_iteration(snake_case			,					snake_case				)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							np.linalg.eigh(snake_case				)
          # Last eigenvalue is the maximum one.
          UpperCAmelCase__						:    str							=							eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          UpperCAmelCase__						:    Optional[int]							=							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(snake_case				) - np.abs(snake_case				)				) <= 1E-6


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 test_power_iteration()


