



from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims











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 ):

						'''simple docstring'''




						def  _A			(					self	):







													'''simple docstring'''

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






						@property
						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      1
													__SCREAMING_SNAKE_CASE								=      3
													__SCREAMING_SNAKE_CASE								=      (32, 32)

													__SCREAMING_SNAKE_CASE								=      floats_tensor((batch_size, num_channels) + sizes   ,							rng=random.Random(0	)	).to(_A	)
													return image






						@property
						def  _A			(					self	):







													'''simple docstring'''

													torch.manual_seed(0	)
													__SCREAMING_SNAKE_CASE								=      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  _A			(					self	):







													'''simple docstring'''

													torch.manual_seed(0	)
													__SCREAMING_SNAKE_CASE								=      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  _A			(					self	):







													'''simple docstring'''

													torch.manual_seed(0	)
													__SCREAMING_SNAKE_CASE								=      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=1_000   ,							)
													return CLIPTextModel(_A	)






						@property
						def  _A			(					self	):







													'''simple docstring'''

													def extract(*_A   ,							**_A	):

																				class      UpperCAmelCase_						:

																										'''simple docstring'''




																										def __init__(					self	):







																																	'''simple docstring'''

																																	__SCREAMING_SNAKE_CASE								=      torch.ones([0]	)






																										def  _A			(					self   ,							_A	):







																																	'''simple docstring'''

																																	self.pixel_values.to(_A	)
																																	return self







																				return Out()

													return extract






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      'cpu'  # ensure determinism for the device-dependent torch.Generator
													__SCREAMING_SNAKE_CASE								=      self.dummy_cond_unet
													__SCREAMING_SNAKE_CASE								=      DDIMScheduler(
													    beta_start=0.0_0_0_8_5   ,							beta_end=0.0_1_2   ,							beta_schedule='scaled_linear'   ,							clip_sample=_A   ,							set_alpha_to_one=_A   ,							)

													__SCREAMING_SNAKE_CASE								=      self.dummy_vae
													__SCREAMING_SNAKE_CASE								=      self.dummy_text_encoder
													__SCREAMING_SNAKE_CASE								=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

													# make sure here that pndm scheduler skips prk
													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline(
													    unet=_A   ,							scheduler=_A   ,							vae=_A   ,							text_encoder=_A   ,							tokenizer=_A   ,							safety_checker=_A   ,							feature_extractor=self.dummy_extractor   ,							)
													__SCREAMING_SNAKE_CASE								=      sd_pipe.to(_A	)
													sd_pipe.set_progress_bar_config(disable=_A	)

													__SCREAMING_SNAKE_CASE								=      'A painting of a squirrel eating a burger'

													__SCREAMING_SNAKE_CASE								=      torch.Generator(device=_A	).manual_seed(0	)
													__SCREAMING_SNAKE_CASE								=      sd_pipe([prompt]   ,							generator=_A   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='np'	)
													__SCREAMING_SNAKE_CASE								=      output.images

													__SCREAMING_SNAKE_CASE								=      torch.Generator(device=_A	).manual_seed(0	)
													__SCREAMING_SNAKE_CASE								=      sd_pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='np'   ,							return_dict=_A   ,							)[0]

													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													__SCREAMING_SNAKE_CASE								=      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  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      'cpu'  # ensure determinism for the device-dependent torch.Generator
													__SCREAMING_SNAKE_CASE								=      self.dummy_cond_unet
													__SCREAMING_SNAKE_CASE								=      PNDMScheduler(skip_prk_steps=_A	)
													__SCREAMING_SNAKE_CASE								=      self.dummy_vae
													__SCREAMING_SNAKE_CASE								=      self.dummy_text_encoder
													__SCREAMING_SNAKE_CASE								=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

													# make sure here that pndm scheduler skips prk
													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline(
													    unet=_A   ,							scheduler=_A   ,							vae=_A   ,							text_encoder=_A   ,							tokenizer=_A   ,							safety_checker=_A   ,							feature_extractor=self.dummy_extractor   ,							)
													__SCREAMING_SNAKE_CASE								=      sd_pipe.to(_A	)
													sd_pipe.set_progress_bar_config(disable=_A	)

													__SCREAMING_SNAKE_CASE								=      'A painting of a squirrel eating a burger'
													__SCREAMING_SNAKE_CASE								=      torch.Generator(device=_A	).manual_seed(0	)
													__SCREAMING_SNAKE_CASE								=      sd_pipe([prompt]   ,							generator=_A   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='np'	)

													__SCREAMING_SNAKE_CASE								=      output.images

													__SCREAMING_SNAKE_CASE								=      torch.Generator(device=_A	).manual_seed(0	)
													__SCREAMING_SNAKE_CASE								=      sd_pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='np'   ,							return_dict=_A   ,							)[0]

													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													__SCREAMING_SNAKE_CASE								=      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  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained(
													    'hf-internal-testing/tiny-stable-diffusion-lms-pipe'   ,							safety_checker=_A	)
													assert isinstance(_A   ,							_A	)
													assert isinstance(pipe.scheduler   ,							_A	)
													assert pipe.safety_checker is None

													__SCREAMING_SNAKE_CASE								=      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(_A	)
																				__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained(_A	)

													# sanity check that the pipeline still works
													assert pipe.safety_checker is None
													__SCREAMING_SNAKE_CASE								=      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  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      self.dummy_cond_unet
													__SCREAMING_SNAKE_CASE								=      PNDMScheduler(skip_prk_steps=_A	)
													__SCREAMING_SNAKE_CASE								=      self.dummy_vae
													__SCREAMING_SNAKE_CASE								=      self.dummy_text_encoder
													__SCREAMING_SNAKE_CASE								=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

													# put models in fp16
													__SCREAMING_SNAKE_CASE								=      unet.half()
													__SCREAMING_SNAKE_CASE								=      vae.half()
													__SCREAMING_SNAKE_CASE								=      bert.half()

													# make sure here that pndm scheduler skips prk
													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline(
													    unet=_A   ,							scheduler=_A   ,							vae=_A   ,							text_encoder=_A   ,							tokenizer=_A   ,							safety_checker=_A   ,							feature_extractor=self.dummy_extractor   ,							)
													__SCREAMING_SNAKE_CASE								=      sd_pipe.to(_A	)
													sd_pipe.set_progress_bar_config(disable=_A	)

													__SCREAMING_SNAKE_CASE								=      'A painting of a squirrel eating a burger'
													__SCREAMING_SNAKE_CASE								=      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 ):

						'''simple docstring'''




						def  _A			(					self	):







													'''simple docstring'''

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






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      'cuda'
													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
													__SCREAMING_SNAKE_CASE								=      pipe.to(_A	)
													pipe.set_progress_bar_config(disable=_A	)

													__SCREAMING_SNAKE_CASE								=      'a photo of a cat'
													__SCREAMING_SNAKE_CASE								=      {
													    '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,
													}

													__SCREAMING_SNAKE_CASE								=      3
													__SCREAMING_SNAKE_CASE								=      7

													# no sega enabled
													__SCREAMING_SNAKE_CASE								=      torch.Generator(_A	)
													generator.manual_seed(_A	)
													__SCREAMING_SNAKE_CASE								=      pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							)

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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, 512, 512, 3)

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

													# with sega enabled
													# generator = torch.manual_seed(seed)
													generator.manual_seed(_A	)
													__SCREAMING_SNAKE_CASE								=      pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							**_A   ,							)

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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, 512, 512, 3)

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






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      'cuda'
													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
													__SCREAMING_SNAKE_CASE								=      pipe.to(_A	)
													pipe.set_progress_bar_config(disable=_A	)

													__SCREAMING_SNAKE_CASE								=      'an image of a crowded boulevard, realistic, 4k'
													__SCREAMING_SNAKE_CASE								=      {
													    '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,
													}

													__SCREAMING_SNAKE_CASE								=      9
													__SCREAMING_SNAKE_CASE								=      7

													# no sega enabled
													__SCREAMING_SNAKE_CASE								=      torch.Generator(_A	)
													generator.manual_seed(_A	)
													__SCREAMING_SNAKE_CASE								=      pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							)

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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, 512, 512, 3)

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

													# with sega enabled
													# generator = torch.manual_seed(seed)
													generator.manual_seed(_A	)
													__SCREAMING_SNAKE_CASE								=      pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							**_A   ,							)

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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, 512, 512, 3)

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






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      'cuda'
													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
													__SCREAMING_SNAKE_CASE								=      pipe.to(_A	)
													pipe.set_progress_bar_config(disable=_A	)

													__SCREAMING_SNAKE_CASE								=      'a castle next to a river'
													__SCREAMING_SNAKE_CASE								=      {
													    '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,
													}

													__SCREAMING_SNAKE_CASE								=      48
													__SCREAMING_SNAKE_CASE								=      7

													# no sega enabled
													__SCREAMING_SNAKE_CASE								=      torch.Generator(_A	)
													generator.manual_seed(_A	)
													__SCREAMING_SNAKE_CASE								=      pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							)

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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, 512, 512, 3)

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

													# with sega enabled
													# generator = torch.manual_seed(seed)
													generator.manual_seed(_A	)
													__SCREAMING_SNAKE_CASE								=      pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							**_A   ,							)

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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, 512, 512, 3)

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






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      'cuda'
													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'   ,							torch_dtype=torch.floataa	)
													__SCREAMING_SNAKE_CASE								=      pipe.to(_A	)
													pipe.set_progress_bar_config(disable=_A	)

													__SCREAMING_SNAKE_CASE								=      'a photo of a cat'
													__SCREAMING_SNAKE_CASE								=      {
													    '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,
													}

													__SCREAMING_SNAKE_CASE								=      3
													__SCREAMING_SNAKE_CASE								=      7

													# no sega enabled
													__SCREAMING_SNAKE_CASE								=      torch.Generator(_A	)
													generator.manual_seed(_A	)
													__SCREAMING_SNAKE_CASE								=      pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							)

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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, 512, 512, 3)

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

													# with sega enabled
													# generator = torch.manual_seed(seed)
													generator.manual_seed(_A	)
													__SCREAMING_SNAKE_CASE								=      pipe(
													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							**_A   ,							)

													__SCREAMING_SNAKE_CASE								=      output.images
													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE								=      [
													    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, 512, 512, 3)

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






