


'''simple docstring'''



from __future__ import annotations



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				list[int]							)     ->		list[int]:  # This function is recursive
     __lowerCamelCase   :	Dict								=		len(UpperCAmelCase_							)
     # If the array contains only one element, we return it (it's the stop condition of
     # recursion)
     if array_length <= 1:
          return array
          # Else
     __lowerCamelCase   :	Any								=		array[0]
     __lowerCamelCase   :	Tuple								=		False
     __lowerCamelCase   :	str								=		1
     __lowerCamelCase   :	list[int]								=		[]
     while not is_found and i < array_length:
          if array[i] < pivot:
               __lowerCamelCase   :	str								=		True
               __lowerCamelCase   :	List[str]								=		[element for element in array[i:] if element >= array[i]]
               __lowerCamelCase   :	Optional[int]								=		longest_subsequence(UpperCAmelCase_							)
               if len(UpperCAmelCase_							) > len(UpperCAmelCase_							):
                    __lowerCamelCase   :	Optional[int]								=		temp_array
          else:
               i += 1

     __lowerCamelCase   :	List[str]								=		[element for element in array[1:] if element >= pivot]
     __lowerCamelCase   :	List[str]								=		[pivot, *longest_subsequence(UpperCAmelCase_							)]
     if len(UpperCAmelCase_							) > len(UpperCAmelCase_							):
          return temp_array
     else:
          return longest_subseq


if __name__ == "__main__":
      import doctest

      doctest.testmod()










'''simple docstring'''



import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class    UpperCAmelCase_  (_UpperCAmelCase					,		_UpperCAmelCase					,		_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    lowerCamelCase  :					Union[str, Any]							     =			StableDiffusionInstructPixaPixPipeline
    lowerCamelCase  :					List[Any]							     =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
    lowerCamelCase  :					List[str]							     =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    lowerCamelCase  :					Optional[int]							     =			IMAGE_TO_IMAGE_IMAGE_PARAMS
    lowerCamelCase  :					List[str]							     =			IMAGE_TO_IMAGE_IMAGE_PARAMS







    def       lowercase_				(							self	)				->     int:
         torch.manual_seed(0	)
         __lowerCamelCase   :	Any								=		UNetaDConditionModel(
             block_out_channels=(32, 64)			,     layers_per_block=2			,     sample_size=32			,     in_channels=8			,     out_channels=4			,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,     cross_attention_dim=32			,     )
         __lowerCamelCase   :	Union[str, Any]								=		PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_	)
         torch.manual_seed(0	)
         __lowerCamelCase   :	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			,     )
         torch.manual_seed(0	)
         __lowerCamelCase   :	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			,     )
         __lowerCamelCase   :	int								=		CLIPTextModel(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Optional[int]								=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

         __lowerCamelCase   :	Optional[Any]								=		{
             'unet': unet,
             'scheduler': 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	)				->     Dict:
         __lowerCamelCase   :	int								=		floats_tensor((1, 3, 32, 32)			,     rng=random.Random(SCREAMING_SNAKE_CASE_	)	).to(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[Any]								=		image.cpu().permute(0			,     2			,     3			,     1	)[0]
         __lowerCamelCase   :	int								=		Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_	)	).convert('RGB'	)
         if str(SCREAMING_SNAKE_CASE_	).startswith('mps'	):
              __lowerCamelCase   :	Union[str, Any]								=		torch.manual_seed(SCREAMING_SNAKE_CASE_	)
         else:
              __lowerCamelCase   :	Optional[int]								=		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Tuple								=		{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'guidance_scale': 6.0,
             'image_guidance_scale': 1,
             'output_type': 'numpy',
         }
         return inputs







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	Optional[int]								=		'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase   :	Any								=		self.get_dummy_components()
         __lowerCamelCase   :	Tuple								=		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Optional[int]								=		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		sd_pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	List[str]								=		image[0, -3:, -3:, -1]
         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase   :	Optional[int]								=		np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	List[str]								=		'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase   :	Union[str, Any]								=		self.get_dummy_components()
         __lowerCamelCase   :	List[Any]								=		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Tuple								=		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Union[str, Any]								=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Union[str, Any]								=		'french fries'
         __lowerCamelCase   :	List[Any]								=		sd_pipe(**SCREAMING_SNAKE_CASE_			,     negative_prompt=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Tuple								=		output.images
         __lowerCamelCase   :	str								=		image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase   :	Optional[Any]								=		np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	List[str]								=		'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase   :	Union[str, Any]								=		self.get_dummy_components()
         __lowerCamelCase   :	Any								=		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[str]								=		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		[inputs['prompt']] * 2

         __lowerCamelCase   :	Tuple								=		np.array(inputs['image']	).astype(np.floataa	) / 2_5_5.0
         __lowerCamelCase   :	List[str]								=		torch.from_numpy(SCREAMING_SNAKE_CASE_	).unsqueeze(0	).to(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Dict								=		image / 2 + 0.5
         __lowerCamelCase   :	Optional[Any]								=		image.permute(0			,     3			,     1			,     2	)
         __lowerCamelCase   :	Dict								=		image.repeat(2			,     1			,     1			,     1	)

         __lowerCamelCase   :	int								=		sd_pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	Tuple								=		image[-1, -3:, -3:, -1]

         assert image.shape == (2, 32, 32, 3)
         __lowerCamelCase   :	Union[str, Any]								=		np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	str								=		'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase   :	int								=		self.get_dummy_components()
         __lowerCamelCase   :	Union[str, Any]								=		EulerAncestralDiscreteScheduler(
             beta_start=0.0_0_0_8_5			,     beta_end=0.0_1_2			,     beta_schedule='scaled_linear'	)
         __lowerCamelCase   :	str								=		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Dict								=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		sd_pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	Optional[Any]								=		image[0, -3:, -3:, -1]

         __lowerCamelCase   :	Tuple								=		[round(SCREAMING_SNAKE_CASE_			,     4	) for x in image_slice.flatten().tolist()]
         print(','.join([str(SCREAMING_SNAKE_CASE_	) for x in slice]	)	)

         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase   :	List[str]								=		np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     List[Any]:
         super().test_inference_batch_single_identical(expected_max_diff=3E-3	)







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Union[str, Any]								=		self.get_dummy_components()
         __lowerCamelCase   :	Tuple								=		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE_			,     do_normalize=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	int								=		pipe(**self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_			,     input_image_type='pt'	)	)[0]

         __lowerCamelCase   :	Optional[Any]								=		components['vae']
         __lowerCamelCase   :	Dict								=		self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_			,     input_image_type='pt'	)

         for image_param in self.image_latents_params:
              if image_param in inputs.keys():
                   __lowerCamelCase   :	str								=		vae.encode(inputs[image_param]	).latent_dist.mode()

         __lowerCamelCase   :	str								=		pipe(**SCREAMING_SNAKE_CASE_	)[0]

         __lowerCamelCase   :	Optional[Any]								=		np.abs(out - out_latents_inputs	).max()
         self.assertLess(SCREAMING_SNAKE_CASE_			,     1E-4			,     'passing latents as image input generate different result from passing image'	)







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





    """simple docstring"""






    def       lowercase_				(							self	)				->     int:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0	)				->     str:
         __lowerCamelCase   :	str								=		torch.manual_seed(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'	)
         __lowerCamelCase   :	Any								=		{
             'prompt': 'turn him into a cyborg',
             'image': image,
             'generator': generator,
             'num_inference_steps': 3,
             'guidance_scale': 7.5,
             'image_guidance_scale': 1.0,
             'output_type': 'numpy',
         }
         return inputs







    def       lowercase_				(							self	)				->     str:
         __lowerCamelCase   :	int								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_	)
         pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing()

         __lowerCamelCase   :	Optional[Any]								=		self.get_inputs()
         __lowerCamelCase   :	List[str]								=		pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	Optional[int]								=		image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Any								=		np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]	)

         assert np.abs(expected_slice - image_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Any:
         __lowerCamelCase   :	Dict								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
         pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing()

         __lowerCamelCase   :	Optional[Any]								=		self.get_inputs()
         __lowerCamelCase   :	Optional[int]								=		pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	str								=		image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Optional[Any]								=		np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]	)

         assert np.abs(expected_slice - image_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Dict								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Dict								=		DDIMScheduler.from_config(pipe.scheduler.config	)
         pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing()

         __lowerCamelCase   :	Union[str, Any]								=		self.get_inputs()
         __lowerCamelCase   :	str								=		pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	Dict								=		image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Union[str, Any]								=		np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]	)

         assert np.abs(expected_slice - image_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Union[str, Any]								=		0

         def callback_fn(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	) -> None:
              __lowerCamelCase   :	List[Any]								=		True
              nonlocal number_of_steps
              number_of_steps += 1
              if step == 1:
                   __lowerCamelCase   :	Any								=		latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   __lowerCamelCase   :	Union[str, Any]								=		latents[0, -3:, -3:, -1]
                   __lowerCamelCase   :	str								=		np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]	)

                   assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5E-2
              elif step == 2:
                   __lowerCamelCase   :	Optional[int]								=		latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   __lowerCamelCase   :	List[Any]								=		latents[0, -3:, -3:, -1]
                   __lowerCamelCase   :	Any								=		np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]	)

                   assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5E-2

         __lowerCamelCase   :	int								=		False

         __lowerCamelCase   :	Dict								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_			,     torch_dtype=torch.floataa	)
         __lowerCamelCase   :	Optional[Any]								=		pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing()

         __lowerCamelCase   :	Optional[int]								=		self.get_inputs()
         pipe(**SCREAMING_SNAKE_CASE_			,     callback=SCREAMING_SNAKE_CASE_			,     callback_steps=1	)
         assert callback_fn.has_been_called
         assert number_of_steps == 3







    def       lowercase_				(							self	)				->     Union[str, Any]:
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __lowerCamelCase   :	Dict								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_			,     torch_dtype=torch.floataa	)
         __lowerCamelCase   :	List[Any]								=		pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing(1	)
         pipe.enable_sequential_cpu_offload()

         __lowerCamelCase   :	List[str]								=		self.get_inputs()
         __lowerCamelCase   :	Tuple								=		pipe(**SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Optional[Any]								=		torch.cuda.max_memory_allocated()
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 10**9







    def       lowercase_				(							self	)				->     List[Any]:
         __lowerCamelCase   :	Optional[int]								=		self.get_inputs()
         # resize to resolution that is divisible by 8 but not 16 or 32
         __lowerCamelCase   :	Union[str, Any]								=		inputs['image'].resize((5_04, 5_04)	)

         __lowerCamelCase   :	int								=		'timbrooks/instruct-pix2pix'
         __lowerCamelCase   :	Any								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             SCREAMING_SNAKE_CASE_			,     safety_checker=SCREAMING_SNAKE_CASE_			,     )
         pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing()

         __lowerCamelCase   :	Dict								=		pipe(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		output.images[0]

         __lowerCamelCase   :	Optional[int]								=		image[2_55:2_58, 3_83:3_86, -1]

         assert image.shape == (5_04, 5_04, 3)
         __lowerCamelCase   :	List[str]								=		np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 5E-3






