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







				def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






					torch.manual_seed(0						)
					_lowercase :    List[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_			,					)
					_lowercase :    Dict							=							DDIMScheduler(
					    beta_start=0.0_0085			,					beta_end=0.012			,					beta_schedule='scaled_linear'			,					clip_sample=lowerCamelCase_			,					set_alpha_to_one=lowerCamelCase_			,					)
					_lowercase :    Union[str, Any]							=							DDIMInverseScheduler(
					    beta_start=0.0_0085			,					beta_end=0.012			,					beta_schedule='scaled_linear'			,					clip_sample=lowerCamelCase_			,					set_alpha_to_zero=lowerCamelCase_			,					)
					torch.manual_seed(0						)
					_lowercase :    List[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						)
					_lowercase :    Optional[Any]							=							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			,					)
					_lowercase :    Tuple							=							CLIPTextModel(lowerCamelCase_						)
					_lowercase :    List[str]							=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

					_lowercase :    str							=							{
					    '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    :       Union[str, Any]			,					lowerCamelCase_    :       Any			,					lowerCamelCase_    :       Optional[Any]=0						):

					"""simple docstring"""






					_lowercase :    Any							=							floats_tensor((1, 1_6, 1_6)			,					rng=random.Random(lowerCamelCase_						)						).to(lowerCamelCase_						)
					_lowercase :    Union[str, Any]							=							floats_tensor((1, 2, 4, 1_6, 1_6)			,					rng=random.Random(lowerCamelCase_						)						).to(lowerCamelCase_						)
					if str(lowerCamelCase_						).startswith('mps'						):
						_lowercase :    Optional[Any]							=							torch.manual_seed(lowerCamelCase_						)
					else:
						_lowercase :    List[Any]							=							torch.Generator(device=lowerCamelCase_						).manual_seed(lowerCamelCase_						)
					_lowercase :    Union[str, 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    :       Optional[int]			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       Dict=0						):

					"""simple docstring"""






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

					"""simple docstring"""






					_lowercase :    int							=							floats_tensor((1, 3, 3_2, 3_2)			,					rng=random.Random(lowerCamelCase_						)						).to(lowerCamelCase_						)
					_lowercase :    Optional[int]							=							image.cpu().permute(0			,					2			,					3			,					1						)[0]
					_lowercase :    Optional[Any]							=							Image.fromarray(np.uinta(lowerCamelCase_						)						).convert('RGB'						)
					if str(lowerCamelCase_						).startswith('mps'						):
						_lowercase :    Optional[int]							=							torch.manual_seed(lowerCamelCase_						)
					else:
						_lowercase :    str							=							torch.Generator(device=lowerCamelCase_						).manual_seed(lowerCamelCase_						)
					_lowercase :    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    :       List[Any]						):

					"""simple docstring"""






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

					_lowercase :    List[Any]							=							self.get_dummy_components()
					_lowercase :    List[str]							=							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}						)

					_lowercase :    int							=							self.get_dummy_inputs(lowerCamelCase_						)
					_lowercase :    Optional[Any]							=							pipe(**lowerCamelCase_						)[0]

					with tempfile.TemporaryDirectory() as tmpdir:
						pipe.save_pretrained(lowerCamelCase_						)
						_lowercase :    Optional[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.'''			,					)

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

					_lowercase :    List[Any]							=							np.abs(output - output_loaded						).max()
					self.assertLess(lowerCamelCase_			,					1E-4						)







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					_lowercase :    Dict							=							'cpu'

					_lowercase :    int							=							self.get_dummy_components()
					_lowercase :    Any							=							self.pipeline_class(**lowerCamelCase_						)
					pipe.to(lowerCamelCase_						)
					pipe.set_progress_bar_config(disable=lowerCamelCase_						)

					_lowercase :    Dict							=							self.get_dummy_mask_inputs(lowerCamelCase_						)
					_lowercase :    Dict							=							pipe.generate_mask(**lowerCamelCase_						)
					_lowercase :    List[str]							=							mask[0, -3:, -3:]

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







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    Tuple							=							'cpu'

					_lowercase :    str							=							self.get_dummy_components()
					_lowercase :    int							=							self.pipeline_class(**lowerCamelCase_						)
					pipe.to(lowerCamelCase_						)
					pipe.set_progress_bar_config(disable=lowerCamelCase_						)

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

					self.assertEqual(image.shape			,					(2, 3_2, 3_2, 3)						)
					_lowercase :    Optional[Any]							=							np.array(
					    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]			,					)
					_lowercase :    Tuple							=							np.abs(image_slice.flatten() - expected_slice						).max()
					self.assertLessEqual(lowerCamelCase_			,					1E-3						)







				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							'cpu'

					_lowercase :    Tuple							=							self.get_dummy_components()

					_lowercase :    str							=							{'beta_start': 0.0_0085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
					_lowercase :    Union[str, Any]							=							DPMSolverMultistepScheduler(**lowerCamelCase_						)
					_lowercase :    Union[str, Any]							=							DPMSolverMultistepInverseScheduler(**lowerCamelCase_						)

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

					_lowercase :    Optional[Any]							=							self.get_dummy_inversion_inputs(lowerCamelCase_						)
					_lowercase :    Union[str, Any]							=							pipe.invert(**lowerCamelCase_						).images
					_lowercase :    Optional[int]							=							image[0, -1, -3:, -3:]

					self.assertEqual(image.shape			,					(2, 3_2, 3_2, 3)						)
					_lowercase :    Dict							=							np.array(
					    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]			,					)
					_lowercase :    List[Any]							=							np.abs(image_slice.flatten() - expected_slice						).max()
					self.assertLessEqual(lowerCamelCase_			,					1E-3						)



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







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






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







				@classmethod
				def 							__UpperCAmelCase					( cls    :       List[str]						):

					"""simple docstring"""






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

					_lowercase :    Optional[int]							=							raw_image.convert('RGB'						).resize((7_6_8, 7_6_8)						)

					_lowercase :    Optional[int]							=							raw_image







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					_lowercase :    str							=							torch.manual_seed(0						)

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

					_lowercase :    Optional[Any]							=							'a bowl of fruit'
					_lowercase :    List[Any]							=							'a bowl of pears'

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

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

					_lowercase :    Tuple							=							pipe(
					    prompt=lowerCamelCase_			,					mask_image=lowerCamelCase_			,					image_latents=lowerCamelCase_			,					generator=lowerCamelCase_			,					negative_prompt=lowerCamelCase_			,					inpaint_strength=0.7			,					output_type='numpy'			,					).images[0]

					_lowercase :    Optional[int]							=							(
					    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 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






					_lowercase :    int							=							torch.manual_seed(0						)

					_lowercase :    Any							=							StableDiffusionDiffEditPipeline.from_pretrained(
					    'stabilityai/stable-diffusion-2-1'			,					safety_checker=lowerCamelCase_			,					torch_dtype=torch.floataa						)
					_lowercase :    Tuple							=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
					_lowercase :    Dict							=							DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config						)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=lowerCamelCase_						)

					_lowercase :    Union[str, Any]							=							'a bowl of fruit'
					_lowercase :    Optional[Any]							=							'a bowl of pears'

					_lowercase :    Tuple							=							pipe.generate_mask(
					    image=self.raw_image			,					source_prompt=lowerCamelCase_			,					target_prompt=lowerCamelCase_			,					generator=lowerCamelCase_			,					)

					_lowercase :    Union[str, Any]							=							pipe.invert(
					    prompt=lowerCamelCase_			,					image=self.raw_image			,					inpaint_strength=0.7			,					generator=lowerCamelCase_			,					num_inference_steps=2_5			,					).latents

					_lowercase :    int							=							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]

					_lowercase :    str							=							(
					    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"""







def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	stooge(__UpperCAmelCase     ,0     ,len(__UpperCAmelCase			) - 1			)
	return arr


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if i >= h:
		return

	# If first element is smaller than the last then swap them
	if arr[i] > arr[h]:
		_lowercase				,      _lowercase :    List[str]							=							arr[h], arr[i]

	# If there are more than 2 elements in the array
	if h - i + 1 > 2:
		_lowercase :    Optional[int]							=							(int)((h - i + 1) / 3			)

		# Recursively sort first 2/3 elements
		stooge(__UpperCAmelCase     ,__UpperCAmelCase     ,(h - t)			)

		# Recursively sort last 2/3 elements
		stooge(__UpperCAmelCase     ,i + t     ,(__UpperCAmelCase)			)

		# Recursively sort first 2/3 elements
		stooge(__UpperCAmelCase     ,__UpperCAmelCase     ,(h - t)			)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						input('Enter numbers separated by a comma:\n').strip()
						SCREAMING_SNAKE_CASE									=						[int(item) for item in user_input.split(',')]
						print(stooge_sort(unsorted))



