




"""simple docstring"""




import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class        UpperCAmelCase_  (	unittest.TestCase						):
		def 					_lowerCamelCase    (		self   )		->   Any:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()
		@property
		def 					_lowerCamelCase    (		self   )		->   int:
					__lowercase	:       Union[str, Any]				=   1
					__lowercase	:       Union[str, Any]				=   3
					__lowercase	:       str				=   (32, 32)

					__lowercase	:       Any				=   floats_tensor((batch_size, num_channels) + sizes							,		rng=random.Random(0   )   ).to(UpperCamelCase_   )
					return image
		@property
		def 					_lowerCamelCase    (		self   )		->   List[Any]:
					torch.manual_seed(0   )
					__lowercase	:       str				=   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							,		)
					return model
		@property
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					torch.manual_seed(0   )
					__lowercase	:       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							,		)
					return model
		@property
		def 					_lowerCamelCase    (		self   )		->   Dict:
					torch.manual_seed(0   )
					__lowercase	:       Optional[int]				=   CLIPTextConfig(
					    bos_token_id=0							,		eos_token_id=2							,		hidden_size=32							,		intermediate_size=37							,		layer_norm_eps=1E-05							,		num_attention_heads=4							,		num_hidden_layers=5							,		pad_token_id=1							,		vocab_size=10_00							,		)
					return CLIPTextModel(UpperCamelCase_   )
		@property
		def 					_lowerCamelCase    (		self   )		->   Dict:
					def extract(*UpperCamelCase_							,		**UpperCamelCase_   ):

								class        UpperCAmelCase_  :
										def __init__(		self   )		->   Optional[Any]:
													__lowercase	:       List[Any]				=   torch.ones([0]   )
										def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   Tuple:
													self.pixel_values.to(UpperCamelCase_   )
													return self


								return Out()

					return extract
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					__lowercase	:       Dict				=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
					__lowercase	:       Optional[Any]				=   self.dummy_cond_unet
					__lowercase	:       Union[str, Any]				=   DDIMScheduler(
					    beta_start=0.0_0_0_8_5							,		beta_end=0.0_1_2							,		beta_schedule='''scaled_linear'''							,		clip_sample=UpperCamelCase_							,		set_alpha_to_one=UpperCamelCase_							,		)

					__lowercase	:       Union[str, Any]				=   self.dummy_vae
					__lowercase	:       int				=   self.dummy_text_encoder
					__lowercase	:       Tuple				=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

					# make sure here that pndm scheduler skips prk
					__lowercase	:       Dict				=   StableDiffusionPipeline(
					    unet=UpperCamelCase_							,		scheduler=UpperCamelCase_							,		vae=UpperCamelCase_							,		text_encoder=UpperCamelCase_							,		tokenizer=UpperCamelCase_							,		safety_checker=UpperCamelCase_							,		feature_extractor=self.dummy_extractor							,		)
					__lowercase	:       List[Any]				=   sd_pipe.to(UpperCamelCase_   )
					sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

					__lowercase	:       Optional[int]				=   '''A painting of a squirrel eating a burger'''

					__lowercase	:       Union[str, Any]				=   torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
					__lowercase	:       int				=   sd_pipe([prompt]							,		generator=UpperCamelCase_							,		guidance_scale=6.0							,		num_inference_steps=2							,		output_type='''np'''   )
					__lowercase	:       Dict				=   output.images

					__lowercase	:       Optional[Any]				=   torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
					__lowercase	:       Tuple				=   sd_pipe(
					    [prompt]							,		generator=UpperCamelCase_							,		guidance_scale=6.0							,		num_inference_steps=2							,		output_type='''np'''							,		return_dict=UpperCamelCase_							,		)[0]

					__lowercase	:       Tuple				=   image[0, -3:, -3:, -1]
					__lowercase	:       List[str]				=   image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)
					__lowercase	:       Optional[Any]				=   np.array([0.5_7_5_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
		def 					_lowerCamelCase    (		self   )		->   Tuple:
					__lowercase	:       Optional[Any]				=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
					__lowercase	:       int				=   self.dummy_cond_unet
					__lowercase	:       List[str]				=   PNDMScheduler(skip_prk_steps=UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   self.dummy_vae
					__lowercase	:       List[str]				=   self.dummy_text_encoder
					__lowercase	:       Optional[Any]				=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

					# make sure here that pndm scheduler skips prk
					__lowercase	:       Union[str, Any]				=   StableDiffusionPipeline(
					    unet=UpperCamelCase_							,		scheduler=UpperCamelCase_							,		vae=UpperCamelCase_							,		text_encoder=UpperCamelCase_							,		tokenizer=UpperCamelCase_							,		safety_checker=UpperCamelCase_							,		feature_extractor=self.dummy_extractor							,		)
					__lowercase	:       Optional[int]				=   sd_pipe.to(UpperCamelCase_   )
					sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

					__lowercase	:       Optional[int]				=   '''A painting of a squirrel eating a burger'''
					__lowercase	:       Union[str, Any]				=   torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
					__lowercase	:       str				=   sd_pipe([prompt]							,		generator=UpperCamelCase_							,		guidance_scale=6.0							,		num_inference_steps=2							,		output_type='''np'''   )

					__lowercase	:       Optional[Any]				=   output.images

					__lowercase	:       List[str]				=   torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
					__lowercase	:       Dict				=   sd_pipe(
					    [prompt]							,		generator=UpperCamelCase_							,		guidance_scale=6.0							,		num_inference_steps=2							,		output_type='''np'''							,		return_dict=UpperCamelCase_							,		)[0]

					__lowercase	:       Optional[Any]				=   image[0, -3:, -3:, -1]
					__lowercase	:       Optional[Any]				=   image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)
					__lowercase	:       Union[str, Any]				=   np.array([0.5_1_2_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_4]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Optional[Any]				=   StableDiffusionPipeline.from_pretrained(
					    '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''							,		safety_checker=UpperCamelCase_   )
					assert isinstance(UpperCamelCase_							,		UpperCamelCase_   )
					assert isinstance(pipe.scheduler							,		UpperCamelCase_   )
					assert pipe.safety_checker is None

					__lowercase	:       str				=   pipe('''example prompt'''							,		num_inference_steps=2   ).images[0]
					assert image is not None

					# check that there's no error when saving a pipeline with one of the models being None
					with tempfile.TemporaryDirectory() as tmpdirname:
								pipe.save_pretrained(UpperCamelCase_   )
								__lowercase	:       List[Any]				=   StableDiffusionPipeline.from_pretrained(UpperCamelCase_   )

					# sanity check that the pipeline still works
					assert pipe.safety_checker is None
					__lowercase	:       List[str]				=   pipe('''example prompt'''							,		num_inference_steps=2   ).images[0]
					assert image is not None
		@unittest.skipIf(torch_device != '''cuda'''							,		'''This test requires a GPU'''   )
		def 					_lowerCamelCase    (		self   )		->   Any:
					__lowercase	:       List[str]				=   self.dummy_cond_unet
					__lowercase	:       List[str]				=   PNDMScheduler(skip_prk_steps=UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   self.dummy_vae
					__lowercase	:       Dict				=   self.dummy_text_encoder
					__lowercase	:       Optional[int]				=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

					# put models in fp16
					__lowercase	:       Tuple				=   unet.half()
					__lowercase	:       List[Any]				=   vae.half()
					__lowercase	:       str				=   bert.half()

					# make sure here that pndm scheduler skips prk
					__lowercase	:       Optional[int]				=   StableDiffusionPipeline(
					    unet=UpperCamelCase_							,		scheduler=UpperCamelCase_							,		vae=UpperCamelCase_							,		text_encoder=UpperCamelCase_							,		tokenizer=UpperCamelCase_							,		safety_checker=UpperCamelCase_							,		feature_extractor=self.dummy_extractor							,		)
					__lowercase	:       List[str]				=   sd_pipe.to(UpperCamelCase_   )
					sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

					__lowercase	:       Optional[int]				=   '''A painting of a squirrel eating a burger'''
					__lowercase	:       Union[str, Any]				=   sd_pipe([prompt]							,		num_inference_steps=2							,		output_type='''np'''   ).images

					assert image.shape == (1, 64, 64, 3)


@nightly
@require_torch_gpu
class        UpperCAmelCase_  (	unittest.TestCase						):
		def 					_lowerCamelCase    (		self   )		->   Any:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					__lowercase	:       List[str]				=   '''cuda'''
					__lowercase	:       Dict				=   StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   )
					__lowercase	:       List[str]				=   pipe.to(UpperCamelCase_   )
					pipe.set_progress_bar_config(disable=UpperCamelCase_   )

					__lowercase	:       int				=   '''a photo of a cat'''
					__lowercase	:       Union[str, Any]				=   {
					    '''editing_prompt''': ['''sunglasses'''],
					    '''reverse_editing_direction''': [False],
					    '''edit_warmup_steps''': 10,
					    '''edit_guidance_scale''': 6,
					    '''edit_threshold''': 0.9_5,
					    '''edit_momentum_scale''': 0.5,
					    '''edit_mom_beta''': 0.6,
					}

					__lowercase	:       List[str]				=   3
					__lowercase	:       List[str]				=   7

					# no sega enabled
					__lowercase	:       List[str]				=   torch.Generator(UpperCamelCase_   )
					generator.manual_seed(UpperCamelCase_   )
					__lowercase	:       List[Any]				=   pipe(
					    [prompt]							,		generator=UpperCamelCase_							,		guidance_scale=UpperCamelCase_							,		num_inference_steps=50							,		output_type='''np'''							,		width=5_12							,		height=5_12							,		)

					__lowercase	:       List[str]				=   output.images
					__lowercase	:       Optional[Any]				=   image[0, -3:, -3:, -1]
					__lowercase	:       Optional[Any]				=   [
					    0.3_4_6_7_3_1_1_3,
					    0.3_8_4_9_2_7_3_3,
					    0.3_7_5_9_7_3_5_2,
					    0.3_4_0_8_6_3_3_5,
					    0.3_5_6_5_0_7_4_8,
					    0.3_5_5_7_9_2_0_5,
					    0.3_3_8_4_7_6_3,
					    0.3_4_3_4_0_2_3_6,
					    0.3_5_7_3_2_7_1,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2

					# with sega enabled
					# generator = torch.manual_seed(seed)
					generator.manual_seed(UpperCamelCase_   )
					__lowercase	:       Optional[Any]				=   pipe(
					    [prompt]							,		generator=UpperCamelCase_							,		guidance_scale=UpperCamelCase_							,		num_inference_steps=50							,		output_type='''np'''							,		width=5_12							,		height=5_12							,		**UpperCamelCase_							,		)

					__lowercase	:       Union[str, Any]				=   output.images
					__lowercase	:       Any				=   image[0, -3:, -3:, -1]
					__lowercase	:       Any				=   [
					    0.4_1_8_8_7_8_2_6,
					    0.3_7_7_2_8_7_6_6,
					    0.3_0_1_3_8_2_7_2,
					    0.4_1_4_1_6_3_3_5,
					    0.4_1_6_6_4_9_8_5,
					    0.3_6_2_8_3_3_9_2,
					    0.3_6_1_9_1_2_4_6,
					    0.4_3_3_6_4_4_6_5,
					    0.4_3_0_0_1_7_3_2,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
		def 					_lowerCamelCase    (		self   )		->   Tuple:
					__lowercase	:       str				=   '''cuda'''
					__lowercase	:       Tuple				=   StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   )
					__lowercase	:       Tuple				=   pipe.to(UpperCamelCase_   )
					pipe.set_progress_bar_config(disable=UpperCamelCase_   )

					__lowercase	:       Optional[Any]				=   '''an image of a crowded boulevard, realistic, 4k'''
					__lowercase	:       Optional[int]				=   {
					    '''editing_prompt''': '''crowd, crowded, people''',
					    '''reverse_editing_direction''': True,
					    '''edit_warmup_steps''': 10,
					    '''edit_guidance_scale''': 8.3,
					    '''edit_threshold''': 0.9,
					    '''edit_momentum_scale''': 0.5,
					    '''edit_mom_beta''': 0.6,
					}

					__lowercase	:       List[str]				=   9
					__lowercase	:       Any				=   7

					# no sega enabled
					__lowercase	:       Optional[Any]				=   torch.Generator(UpperCamelCase_   )
					generator.manual_seed(UpperCamelCase_   )
					__lowercase	:       List[str]				=   pipe(
					    [prompt]							,		generator=UpperCamelCase_							,		guidance_scale=UpperCamelCase_							,		num_inference_steps=50							,		output_type='''np'''							,		width=5_12							,		height=5_12							,		)

					__lowercase	:       Optional[Any]				=   output.images
					__lowercase	:       Union[str, Any]				=   image[0, -3:, -3:, -1]
					__lowercase	:       Union[str, Any]				=   [
					    0.4_3_4_9_7_9_9_8,
					    0.9_1_8_1_4_0_6_5,
					    0.7_5_4_0_7_3_9,
					    0.5_5_5_8_0_2_0_5,
					    0.8_4_6_7_2_6_5,
					    0.5_3_8_9_6_9_1,
					    0.6_2_5_7_4_5_0_6,
					    0.5_8_8_9_7_7_6_3,
					    0.5_0_9_2_6_7_5_7,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2

					# with sega enabled
					# generator = torch.manual_seed(seed)
					generator.manual_seed(UpperCamelCase_   )
					__lowercase	:       int				=   pipe(
					    [prompt]							,		generator=UpperCamelCase_							,		guidance_scale=UpperCamelCase_							,		num_inference_steps=50							,		output_type='''np'''							,		width=5_12							,		height=5_12							,		**UpperCamelCase_							,		)

					__lowercase	:       str				=   output.images
					__lowercase	:       List[str]				=   image[0, -3:, -3:, -1]
					__lowercase	:       Optional[int]				=   [
					    0.3_0_8_9_7_1_9,
					    0.3_0_5_0_0_1_4_4,
					    0.2_9_0_1_6_0_4_2,
					    0.3_0_6_3_0_9_6_4,
					    0.3_2_5_6_8_7,
					    0.2_9_4_1_9_2_2_5,
					    0.2_9_0_8_0_9_1,
					    0.2_8_7_2_3_5_9_8,
					    0.2_7_6_9_6_2_9_4,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
		def 					_lowerCamelCase    (		self   )		->   Tuple:
					__lowercase	:       List[str]				=   '''cuda'''
					__lowercase	:       str				=   StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   )
					__lowercase	:       Optional[int]				=   pipe.to(UpperCamelCase_   )
					pipe.set_progress_bar_config(disable=UpperCamelCase_   )

					__lowercase	:       List[str]				=   '''a castle next to a river'''
					__lowercase	:       Tuple				=   {
					    '''editing_prompt''': ['''boat on a river, boat''', '''monet, impression, sunrise'''],
					    '''reverse_editing_direction''': False,
					    '''edit_warmup_steps''': [15, 18],
					    '''edit_guidance_scale''': 6,
					    '''edit_threshold''': [0.9, 0.8],
					    '''edit_momentum_scale''': 0.5,
					    '''edit_mom_beta''': 0.6,
					}

					__lowercase	:       List[str]				=   48
					__lowercase	:       Optional[Any]				=   7

					# no sega enabled
					__lowercase	:       Tuple				=   torch.Generator(UpperCamelCase_   )
					generator.manual_seed(UpperCamelCase_   )
					__lowercase	:       List[Any]				=   pipe(
					    [prompt]							,		generator=UpperCamelCase_							,		guidance_scale=UpperCamelCase_							,		num_inference_steps=50							,		output_type='''np'''							,		width=5_12							,		height=5_12							,		)

					__lowercase	:       str				=   output.images
					__lowercase	:       List[str]				=   image[0, -3:, -3:, -1]
					__lowercase	:       List[str]				=   [
					    0.7_5_1_6_3_5_5_5,
					    0.7_6_0_3_7_1_4_5,
					    0.6_1_7_8_5,
					    0.9_1_8_9_6_7_3,
					    0.8_6_2_7_7_0_1,
					    0.8_5_1_8_9_6_9_4,
					    0.8_5_1_2_8_1_3,
					    0.8_7_0_1_2_0_7_6,
					    0.8_3_1_2_8_5_7,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2

					# with sega enabled
					# generator = torch.manual_seed(seed)
					generator.manual_seed(UpperCamelCase_   )
					__lowercase	:       List[str]				=   pipe(
					    [prompt]							,		generator=UpperCamelCase_							,		guidance_scale=UpperCamelCase_							,		num_inference_steps=50							,		output_type='''np'''							,		width=5_12							,		height=5_12							,		**UpperCamelCase_							,		)

					__lowercase	:       int				=   output.images
					__lowercase	:       Any				=   image[0, -3:, -3:, -1]
					__lowercase	:       str				=   [
					    0.7_3_5_5_3_3_6_5,
					    0.7_5_3_7_2_7_1,
					    0.7_4_3_4_1_9_0_5,
					    0.6_6_4_8_0_3_5_6,
					    0.6_4_7_2_9_2_5,
					    0.6_3_0_3_9_4_1_6,
					    0.6_4_8_1_2_9_0_5,
					    0.6_7_4_9_7_1_7,
					    0.6_5_1_7_1_0_2,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Union[str, Any]				=   '''cuda'''
					__lowercase	:       Optional[int]				=   StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							,		torch_dtype=torch.floataa   )
					__lowercase	:       Tuple				=   pipe.to(UpperCamelCase_   )
					pipe.set_progress_bar_config(disable=UpperCamelCase_   )

					__lowercase	:       Dict				=   '''a photo of a cat'''
					__lowercase	:       Dict				=   {
					    '''editing_prompt''': ['''sunglasses'''],
					    '''reverse_editing_direction''': [False],
					    '''edit_warmup_steps''': 10,
					    '''edit_guidance_scale''': 6,
					    '''edit_threshold''': 0.9_5,
					    '''edit_momentum_scale''': 0.5,
					    '''edit_mom_beta''': 0.6,
					}

					__lowercase	:       Optional[int]				=   3
					__lowercase	:       Optional[Any]				=   7

					# no sega enabled
					__lowercase	:       Union[str, Any]				=   torch.Generator(UpperCamelCase_   )
					generator.manual_seed(UpperCamelCase_   )
					__lowercase	:       str				=   pipe(
					    [prompt]							,		generator=UpperCamelCase_							,		guidance_scale=UpperCamelCase_							,		num_inference_steps=50							,		output_type='''np'''							,		width=5_12							,		height=5_12							,		)

					__lowercase	:       List[str]				=   output.images
					__lowercase	:       Optional[int]				=   image[0, -3:, -3:, -1]
					__lowercase	:       Tuple				=   [
					    0.3_4_8_8_7_6_9_5,
					    0.3_8_7_6_9_5_3,
					    0.3_7_5,
					    0.3_4_4_2_3_8_2_8,
					    0.3_5_8_1_5_4_3,
					    0.3_5_7_1_7_7_7_3,
					    0.3_3_8_3_7_8_9,
					    0.3_4_5_7_0_3_1_2,
					    0.3_5_9_3_7_5,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2

					# with sega enabled
					# generator = torch.manual_seed(seed)
					generator.manual_seed(UpperCamelCase_   )
					__lowercase	:       str				=   pipe(
					    [prompt]							,		generator=UpperCamelCase_							,		guidance_scale=UpperCamelCase_							,		num_inference_steps=50							,		output_type='''np'''							,		width=5_12							,		height=5_12							,		**UpperCamelCase_							,		)

					__lowercase	:       Tuple				=   output.images
					__lowercase	:       List[str]				=   image[0, -3:, -3:, -1]
					__lowercase	:       List[Any]				=   [
					    0.4_2_2_8_5_1_5_6,
					    0.3_6_9_1_4_0_6_2,
					    0.2_9_0_7_7_1_4_8,
					    0.4_2_0_4_1_0_1_6,
					    0.4_1_9_1_8_9_4_5,
					    0.3_5_4_9_8_0_4_7,
					    0.3_6_1_8_1_6_4,
					    0.4_4_2_3_8_2_8,
					    0.4_3_1_1_5_2_3_4,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2









"""simple docstring"""




def       __UpperCAmelCase      (				__UpperCamelCase					):
			if len(__UpperCamelCase					) <= 1:
						return data
			else:
						return [
						    *quick_sort([e for e in data[1:] if e <= data[0]]					),
						    data[0],
						    *quick_sort([e for e in data[1:] if e > data[0]]					),
						]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



