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



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         torch.manual_seed(0   )
         a__		=			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=__SCREAMING_SNAKE_CASE     ,				)
         a__		=			DDIMScheduler(
             beta_start=0.0_00_85     ,				beta_end=0.0_12     ,				beta_schedule='scaled_linear'     ,				clip_sample=__SCREAMING_SNAKE_CASE     ,				set_alpha_to_one=__SCREAMING_SNAKE_CASE     ,				)
         a__		=			DDIMInverseScheduler(
             beta_start=0.0_00_85     ,				beta_end=0.0_12     ,				beta_schedule='scaled_linear'     ,				clip_sample=__SCREAMING_SNAKE_CASE     ,				set_alpha_to_zero=__SCREAMING_SNAKE_CASE     ,				)
         torch.manual_seed(0   )
         a__		=			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   )
         a__		=			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     ,				hidden_act='gelu'     ,				projection_dim=5_1_2     ,				)
         a__		=			CLIPTextModel(__SCREAMING_SNAKE_CASE   )
         a__		=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

         a__		=			{
             '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 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=0   )		->     str:







         """simple docstring"""



         a__		=			floats_tensor((1, 1_6, 1_6)     ,				rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			floats_tensor((1, 2, 4, 1_6, 1_6)     ,				rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
         if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
             a__		=			torch.manual_seed(__SCREAMING_SNAKE_CASE   )
         else:
             a__		=			torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
         a__		=			{
             '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 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=0   )		->     str:







         """simple docstring"""



         a__		=			floats_tensor((1, 3, 3_2, 3_2)     ,				rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			image.cpu().permute(0     ,				2     ,				3     ,				1   )[0]
         a__		=			Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   )
         if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
             a__		=			torch.manual_seed(__SCREAMING_SNAKE_CASE   )
         else:
             a__		=			torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
         a__		=			{
             '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 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=0   )		->     Optional[Any]:







         """simple docstring"""



         a__		=			floats_tensor((1, 3, 3_2, 3_2)     ,				rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			image.cpu().permute(0     ,				2     ,				3     ,				1   )[0]
         a__		=			Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   )
         if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
             a__		=			torch.manual_seed(__SCREAMING_SNAKE_CASE   )
         else:
             a__		=			torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
         a__		=			{
             '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 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



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

         a__		=			self.get_dummy_components()
         a__		=			self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
         pipe.to(__SCREAMING_SNAKE_CASE   )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

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

         a__		=			self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )
         a__		=			pipe(**__SCREAMING_SNAKE_CASE   )[0]

         with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(__SCREAMING_SNAKE_CASE   )
             a__		=			self.pipeline_class.from_pretrained(__SCREAMING_SNAKE_CASE   )
             pipe_loaded.to(__SCREAMING_SNAKE_CASE   )
             pipe_loaded.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

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

         a__		=			self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )
         a__		=			pipe_loaded(**__SCREAMING_SNAKE_CASE   )[0]

         a__		=			np.abs(output - output_loaded   ).max()
         self.assertLess(__SCREAMING_SNAKE_CASE     ,				1e-4   )



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         a__		=			'cpu'

         a__		=			self.get_dummy_components()
         a__		=			self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
         pipe.to(__SCREAMING_SNAKE_CASE   )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

         a__		=			self.get_dummy_mask_inputs(__SCREAMING_SNAKE_CASE   )
         a__		=			pipe.generate_mask(**__SCREAMING_SNAKE_CASE   )
         a__		=			mask[0, -3:, -3:]

         self.assertEqual(mask.shape     ,				(1, 1_6, 1_6)   )
         a__		=			np.array([0] * 9   )
         a__		=			np.abs(mask_slice.flatten() - expected_slice   ).max()
         self.assertLessEqual(__SCREAMING_SNAKE_CASE     ,				1e-3   )
         self.assertEqual(mask[0, -3, -4]     ,				0   )



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







         """simple docstring"""



         a__		=			'cpu'

         a__		=			self.get_dummy_components()
         a__		=			self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
         pipe.to(__SCREAMING_SNAKE_CASE   )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

         a__		=			self.get_dummy_inversion_inputs(__SCREAMING_SNAKE_CASE   )
         a__		=			pipe.invert(**__SCREAMING_SNAKE_CASE   ).images
         a__		=			image[0, -1, -3:, -3:]

         self.assertEqual(image.shape     ,				(2, 3_2, 3_2, 3)   )
         a__		=			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]     ,				)
         a__		=			np.abs(image_slice.flatten() - expected_slice   ).max()
         self.assertLessEqual(__SCREAMING_SNAKE_CASE     ,				1e-3   )



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







         """simple docstring"""



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




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







         """simple docstring"""



         a__		=			'cpu'

         a__		=			self.get_dummy_components()

         a__		=			{'beta_start': 0.0_00_85, 'beta_end': 0.0_12, 'beta_schedule': 'scaled_linear'}
         a__		=			DPMSolverMultistepScheduler(**__SCREAMING_SNAKE_CASE   )
         a__		=			DPMSolverMultistepInverseScheduler(**__SCREAMING_SNAKE_CASE   )

         a__		=			self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
         pipe.to(__SCREAMING_SNAKE_CASE   )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

         a__		=			self.get_dummy_inversion_inputs(__SCREAMING_SNAKE_CASE   )
         a__		=			pipe.invert(**__SCREAMING_SNAKE_CASE   ).images
         a__		=			image[0, -1, -3:, -3:]

         self.assertEqual(image.shape     ,				(2, 3_2, 3_2, 3)   )
         a__		=			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]     ,				)
         a__		=			np.abs(image_slice.flatten() - expected_slice   ).max()
         self.assertLessEqual(__SCREAMING_SNAKE_CASE     ,				1e-3   )







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



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







         """simple docstring"""



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



     @classmethod
     def 			lowercase__  (							cls   )		->     Optional[Any]:







         """simple docstring"""



         a__		=			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'   )

         a__		=			raw_image.convert('RGB'   ).resize((7_6_8, 7_6_8)   )

         a__		=			raw_image



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







         """simple docstring"""



         a__		=			torch.manual_seed(0   )

         a__		=			StableDiffusionDiffEditPipeline.from_pretrained(
             'stabilityai/stable-diffusion-2-1'     ,				safety_checker=__SCREAMING_SNAKE_CASE     ,				torch_dtype=torch.floataa   )
         a__		=			DDIMScheduler.from_config(pipe.scheduler.config   )
         a__		=			DDIMInverseScheduler.from_config(pipe.scheduler.config   )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

         a__		=			'a bowl of fruit'
         a__		=			'a bowl of pears'

         a__		=			pipe.generate_mask(
             image=self.raw_image     ,				source_prompt=__SCREAMING_SNAKE_CASE     ,				target_prompt=__SCREAMING_SNAKE_CASE     ,				generator=__SCREAMING_SNAKE_CASE     ,				)

         a__		=			pipe.invert(
             prompt=__SCREAMING_SNAKE_CASE     ,				image=self.raw_image     ,				inpaint_strength=0.7     ,				generator=__SCREAMING_SNAKE_CASE   ).latents

         a__		=			pipe(
             prompt=__SCREAMING_SNAKE_CASE     ,				mask_image=__SCREAMING_SNAKE_CASE     ,				image_latents=__SCREAMING_SNAKE_CASE     ,				generator=__SCREAMING_SNAKE_CASE     ,				negative_prompt=__SCREAMING_SNAKE_CASE     ,				inpaint_strength=0.7     ,				output_type='numpy'     ,				).images[0]

         a__		=			(
             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 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			torch.manual_seed(0   )

         a__		=			StableDiffusionDiffEditPipeline.from_pretrained(
             'stabilityai/stable-diffusion-2-1'     ,				safety_checker=__SCREAMING_SNAKE_CASE     ,				torch_dtype=torch.floataa   )
         a__		=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
         a__		=			DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config   )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

         a__		=			'a bowl of fruit'
         a__		=			'a bowl of pears'

         a__		=			pipe.generate_mask(
             image=self.raw_image     ,				source_prompt=__SCREAMING_SNAKE_CASE     ,				target_prompt=__SCREAMING_SNAKE_CASE     ,				generator=__SCREAMING_SNAKE_CASE     ,				)

         a__		=			pipe.invert(
             prompt=__SCREAMING_SNAKE_CASE     ,				image=self.raw_image     ,				inpaint_strength=0.7     ,				generator=__SCREAMING_SNAKE_CASE     ,				num_inference_steps=2_5     ,				).latents

         a__		=			pipe(
             prompt=__SCREAMING_SNAKE_CASE     ,				mask_image=__SCREAMING_SNAKE_CASE     ,				image_latents=__SCREAMING_SNAKE_CASE     ,				generator=__SCREAMING_SNAKE_CASE     ,				negative_prompt=__SCREAMING_SNAKE_CASE     ,				inpaint_strength=0.7     ,				num_inference_steps=2_5     ,				output_type='numpy'     ,				).images[0]

         a__		=			(
             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 operator



def     __magic_name__		(					UpperCamelCase     :		list							,		UpperCamelCase     :		bool = False							,		UpperCamelCase     :		list | None = None     )     ->						list:
    a__		=			operator.lt if reverse else operator.gt
    a__		=			solution or []

    if not arr:
        return solution

    a__		=			[arr.pop(0     )]
    for i, item in enumerate(UpperCamelCase     ):
        if _operator(UpperCamelCase							,		sublist[-1]     ):
            sublist.append(UpperCamelCase     )
            arr.pop(UpperCamelCase     )

    #  merging sublist into solution list
    if not solution:
        solution.extend(UpperCamelCase     )
    else:
        while sublist:
            a__		=			sublist.pop(0     )
            for i, xx in enumerate(UpperCamelCase     ):
                if not _operator(UpperCamelCase							,		UpperCamelCase     ):
                    solution.insert(UpperCamelCase							,		UpperCamelCase     )
                    break
            else:
                solution.append(UpperCamelCase     )

    strand_sort(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )
    return solution


if __name__ == "__main__":
    assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
    assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]


