


'''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 						__UpperCamelCase	(						lowercase__						,  lowercase__						,  unittest.TestCase							):
							lowercase			:		Tuple   		=  StableDiffusionDiffEditPipeline
							lowercase			:		Any   		=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
							lowercase			:		List[str]   		=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
							lowercase			:		Optional[Any]   		=  frozenset(
							    []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowercase			:		List[str]   		=  frozenset([]							)





							def 		a__   (  self		:str			):
									torch.manual_seed(0			)
									snake_case_		:							str			       =						UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)  ,layers_per_block=2  ,sample_size=3_2  ,in_channels=4  ,out_channels=4  ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,cross_attention_dim=3_2  ,attention_head_dim=(2, 4)  ,use_linear_projection=__lowerCAmelCase  ,)
									snake_case_		:							List[Any]			       =						DDIMScheduler(
									    beta_start=0.0_00_85  ,beta_end=0.0_12  ,beta_schedule="""scaled_linear"""  ,clip_sample=__lowerCAmelCase  ,set_alpha_to_one=__lowerCAmelCase  ,)
									snake_case_		:							Tuple			       =						DDIMInverseScheduler(
									    beta_start=0.0_00_85  ,beta_end=0.0_12  ,beta_schedule="""scaled_linear"""  ,clip_sample=__lowerCAmelCase  ,set_alpha_to_zero=__lowerCAmelCase  ,)
									torch.manual_seed(0			)
									snake_case_		:							Any			       =						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  ,sample_size=1_2_8  ,)
									torch.manual_seed(0			)
									snake_case_		:							List[Any]			       =						CLIPTextConfig(
									    bos_token_id=0  ,eos_token_id=2  ,hidden_size=3_2  ,intermediate_size=3_7  ,layer_norm_eps=1E-0_5  ,num_attention_heads=4  ,num_hidden_layers=5  ,pad_token_id=1  ,vocab_size=1_0_0_0  ,hidden_act="""gelu"""  ,projection_dim=5_1_2  ,)
									snake_case_		:							Dict			       =						CLIPTextModel(__lowerCAmelCase			)
									snake_case_		:							str			       =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

									snake_case_		:							Any			       =						{
									    """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 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:Optional[int]=0			):
									snake_case_		:							Union[str, Any]			       =						floats_tensor((1, 1_6, 1_6)  ,rng=random.Random(__lowerCAmelCase			)			).to(__lowerCAmelCase			)
									snake_case_		:							List[str]			       =						floats_tensor((1, 2, 4, 1_6, 1_6)  ,rng=random.Random(__lowerCAmelCase			)			).to(__lowerCAmelCase			)
									if str(__lowerCAmelCase			).startswith("""mps"""			):
											snake_case_		:							str			       =						torch.manual_seed(__lowerCAmelCase			)
									else:
											snake_case_		:							Optional[int]			       =						torch.Generator(device=__lowerCAmelCase			).manual_seed(__lowerCAmelCase			)
									snake_case_		:							str			       =						{
									    """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 		a__   (  self		:Dict  ,_UpperCamelCase		:int  ,_UpperCamelCase		:int=0			):
									snake_case_		:							Tuple			       =						floats_tensor((1, 3, 3_2, 3_2)  ,rng=random.Random(__lowerCAmelCase			)			).to(__lowerCAmelCase			)
									snake_case_		:							Tuple			       =						image.cpu().permute(0  ,2  ,3  ,1			)[0]
									snake_case_		:							Any			       =						Image.fromarray(np.uinta(__lowerCAmelCase			)			).convert("""RGB"""			)
									if str(__lowerCAmelCase			).startswith("""mps"""			):
											snake_case_		:							str			       =						torch.manual_seed(__lowerCAmelCase			)
									else:
											snake_case_		:							Dict			       =						torch.Generator(device=__lowerCAmelCase			).manual_seed(__lowerCAmelCase			)
									snake_case_		:							str			       =						{
									    """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 		a__   (  self		:int  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:List[str]=0			):
									snake_case_		:							Tuple			       =						floats_tensor((1, 3, 3_2, 3_2)  ,rng=random.Random(__lowerCAmelCase			)			).to(__lowerCAmelCase			)
									snake_case_		:							int			       =						image.cpu().permute(0  ,2  ,3  ,1			)[0]
									snake_case_		:							Any			       =						Image.fromarray(np.uinta(__lowerCAmelCase			)			).convert("""RGB"""			)
									if str(__lowerCAmelCase			).startswith("""mps"""			):
											snake_case_		:							List[Any]			       =						torch.manual_seed(__lowerCAmelCase			)
									else:
											snake_case_		:							str			       =						torch.Generator(device=__lowerCAmelCase			).manual_seed(__lowerCAmelCase			)
									snake_case_		:							str			       =						{
									    """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 		a__   (  self		:Tuple			):







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

									snake_case_		:							int			       =						self.get_dummy_components()
									snake_case_		:							int			       =						self.pipeline_class(**__lowerCAmelCase			)
									pipe.to(__lowerCAmelCase			)
									pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

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

									snake_case_		:							int			       =						self.get_dummy_inputs(__lowerCAmelCase			)
									snake_case_		:							Union[str, Any]			       =						pipe(**__lowerCAmelCase			)[0]

									with tempfile.TemporaryDirectory() as tmpdir:
											pipe.save_pretrained(__lowerCAmelCase			)
											snake_case_		:							Any			       =						self.pipeline_class.from_pretrained(__lowerCAmelCase			)
											pipe_loaded.to(__lowerCAmelCase			)
											pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase			)

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

									snake_case_		:							List[str]			       =						self.get_dummy_inputs(__lowerCAmelCase			)
									snake_case_		:							List[Any]			       =						pipe_loaded(**__lowerCAmelCase			)[0]

									snake_case_		:							Tuple			       =						np.abs(output - output_loaded			).max()
									self.assertLess(__lowerCAmelCase  ,1E-4			)





							def 		a__   (  self		:Tuple			):
									snake_case_		:							str			       =						"""cpu"""

									snake_case_		:							Optional[Any]			       =						self.get_dummy_components()
									snake_case_		:							Dict			       =						self.pipeline_class(**__lowerCAmelCase			)
									pipe.to(__lowerCAmelCase			)
									pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

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

									self.assertEqual(mask.shape  ,(1, 1_6, 1_6)			)
									snake_case_		:							Tuple			       =						np.array([0] * 9			)
									snake_case_		:							Optional[Any]			       =						np.abs(mask_slice.flatten() - expected_slice			).max()
									self.assertLessEqual(__lowerCAmelCase  ,1E-3			)
									self.assertEqual(mask[0, -3, -4]  ,0			)





							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							Optional[int]			       =						"""cpu"""

									snake_case_		:							str			       =						self.get_dummy_components()
									snake_case_		:							Optional[int]			       =						self.pipeline_class(**__lowerCAmelCase			)
									pipe.to(__lowerCAmelCase			)
									pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

									snake_case_		:							str			       =						self.get_dummy_inversion_inputs(__lowerCAmelCase			)
									snake_case_		:							Tuple			       =						pipe.invert(**__lowerCAmelCase			).images
									snake_case_		:							Optional[Any]			       =						image[0, -1, -3:, -3:]

									self.assertEqual(image.shape  ,(2, 3_2, 3_2, 3)			)
									snake_case_		:							Tuple			       =						np.array(
									    [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]  ,)
									snake_case_		:							str			       =						np.abs(image_slice.flatten() - expected_slice			).max()
									self.assertLessEqual(__lowerCAmelCase  ,1E-3			)





							def 		a__   (  self		:int			):
									super().test_inference_batch_single_identical(expected_max_diff=5E-3			)





							def 		a__   (  self		:Any			):
									snake_case_		:							Union[str, Any]			       =						"""cpu"""

									snake_case_		:							Optional[int]			       =						self.get_dummy_components()

									snake_case_		:							List[Any]			       =						{"""beta_start""": 0.0_00_85, """beta_end""": 0.0_12, """beta_schedule""": """scaled_linear"""}
									snake_case_		:							int			       =						DPMSolverMultistepScheduler(**__lowerCAmelCase			)
									snake_case_		:							List[Any]			       =						DPMSolverMultistepInverseScheduler(**__lowerCAmelCase			)

									snake_case_		:							Dict			       =						self.pipeline_class(**__lowerCAmelCase			)
									pipe.to(__lowerCAmelCase			)
									pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

									snake_case_		:							Tuple			       =						self.get_dummy_inversion_inputs(__lowerCAmelCase			)
									snake_case_		:							List[str]			       =						pipe.invert(**__lowerCAmelCase			).images
									snake_case_		:							int			       =						image[0, -1, -3:, -3:]

									self.assertEqual(image.shape  ,(2, 3_2, 3_2, 3)			)
									snake_case_		:							Optional[int]			       =						np.array(
									    [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]  ,)
									snake_case_		:							int			       =						np.abs(image_slice.flatten() - expected_slice			).max()
									self.assertLessEqual(__lowerCAmelCase  ,1E-3			)





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





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





							@classmethod
							def 		a__   (  cls		:Tuple			):
									snake_case_		:							Union[str, Any]			       =						load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""			)

									snake_case_		:							Dict			       =						raw_image.convert("""RGB"""			).resize((7_6_8, 7_6_8)			)

									snake_case_		:							Dict			       =						raw_image





							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							Optional[int]			       =						torch.manual_seed(0			)

									snake_case_		:							Any			       =						StableDiffusionDiffEditPipeline.from_pretrained(
									    """stabilityai/stable-diffusion-2-1"""  ,safety_checker=__lowerCAmelCase  ,torch_dtype=torch.floataa			)
									snake_case_		:							List[Any]			       =						DDIMScheduler.from_config(pipe.scheduler.config			)
									snake_case_		:							Any			       =						DDIMInverseScheduler.from_config(pipe.scheduler.config			)
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

									snake_case_		:							str			       =						"""a bowl of fruit"""
									snake_case_		:							Dict			       =						"""a bowl of pears"""

									snake_case_		:							Optional[int]			       =						pipe.generate_mask(
									    image=self.raw_image  ,source_prompt=__lowerCAmelCase  ,target_prompt=__lowerCAmelCase  ,generator=__lowerCAmelCase  ,)

									snake_case_		:							Dict			       =						pipe.invert(
									    prompt=__lowerCAmelCase  ,image=self.raw_image  ,inpaint_strength=0.7  ,generator=__lowerCAmelCase			).latents

									snake_case_		:							Union[str, Any]			       =						pipe(
									    prompt=__lowerCAmelCase  ,mask_image=__lowerCAmelCase  ,image_latents=__lowerCAmelCase  ,generator=__lowerCAmelCase  ,negative_prompt=__lowerCAmelCase  ,inpaint_strength=0.7  ,output_type="""numpy"""  ,).images[0]

									snake_case_		:							List[Any]			       =						(
									    np.array(
									        load_image(
									            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									            """/diffedit/pears.png"""			).resize((7_6_8, 7_6_8)			)			)
									    / 2_5_5
									)
									assert np.abs((expected_image - image).max()			) < 5E-1





							def 		a__   (  self		:Any			):
									snake_case_		:							Optional[int]			       =						torch.manual_seed(0			)

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

									snake_case_		:							Dict			       =						"""a bowl of fruit"""
									snake_case_		:							Any			       =						"""a bowl of pears"""

									snake_case_		:							List[str]			       =						pipe.generate_mask(
									    image=self.raw_image  ,source_prompt=__lowerCAmelCase  ,target_prompt=__lowerCAmelCase  ,generator=__lowerCAmelCase  ,)

									snake_case_		:							Tuple			       =						pipe.invert(
									    prompt=__lowerCAmelCase  ,image=self.raw_image  ,inpaint_strength=0.7  ,generator=__lowerCAmelCase  ,num_inference_steps=2_5  ,).latents

									snake_case_		:							List[Any]			       =						pipe(
									    prompt=__lowerCAmelCase  ,mask_image=__lowerCAmelCase  ,image_latents=__lowerCAmelCase  ,generator=__lowerCAmelCase  ,negative_prompt=__lowerCAmelCase  ,inpaint_strength=0.7  ,num_inference_steps=2_5  ,output_type="""numpy"""  ,).images[0]

									snake_case_		:							Any			       =						(
									    np.array(
									        load_image(
									            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									            """/diffedit/pears.png"""			).resize((7_6_8, 7_6_8)			)			)
									    / 2_5_5
									)
									assert np.abs((expected_image - image).max()			) < 5E-1



'''simple docstring'''






import random
from typing import Any





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		for _ in range(len(lowerCamelCase_ ) ):
				snake_case_		:							Union[str, Any]			       =						random.randint(0				,      len(lowerCamelCase_ ) - 1 )
				snake_case_		:							Any			       =						random.randint(0				,      len(lowerCamelCase_ ) - 1 )
				snake_case_       ,      snake_case_		:							List[str]			       =						data[b], data[a]
		return data


if __name__ == "__main__":
							__A	:	Optional[int]     =     [0, 1, 2, 3, 4, 5, 6, 7]
							__A	:	Optional[int]     =     ['python', 'says', 'hello', '!']
							print('Fisher-Yates Shuffle:')
							print('List', integers, strings)
							print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))