





import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowercase_		:				Optional[Any]   	=						pytest.mark.integration

@pytest.mark.parametrize('path'	,			['paws', 'csv'] )
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				inspect_dataset(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			str  						=   path + '.py'
				assert script_name in os.listdir(__lowerCAmelCase )
				assert "__pycache__" not in os.listdir(__lowerCAmelCase )

@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning' )
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning' )
@pytest.mark.parametrize('path'	,			['accuracy'] )
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				inspect_metric(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			Tuple  						=   path + '.py'
				assert script_name in os.listdir(__lowerCAmelCase )
				assert "__pycache__" not in os.listdir(__lowerCAmelCase )

@pytest.mark.parametrize(
    'path, config_name, expected_splits'	,			[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Union[str, Any]  						=   get_dataset_config_info(__lowerCAmelCase	,			config_name=__lowerCAmelCase )
				assert info.config_name == config_name
				assert list(info.splits.keys() ) == expected_splits

@pytest.mark.parametrize(
    'path, config_name, expected_exception'	,			[
        ('paws', None, ValueError),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				with pytest.raises(__lowerCAmelCase ):
								get_dataset_config_info(__lowerCAmelCase	,			config_name=__lowerCAmelCase )

@pytest.mark.parametrize(
    'path, expected'	,			[
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   get_dataset_config_names(__lowerCAmelCase )
				assert expected in config_names

@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'	,			[
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[Any]  						=   get_dataset_infos(__lowerCAmelCase )
				assert list(infos.keys() ) == expected_configs
				_snake_case :			Any  						=   expected_configs[0]
				assert expected_config in infos
				_snake_case :			str  						=   infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys() ) == expected_splits_in_first_config

@pytest.mark.parametrize(
    'path, expected_config, expected_splits'	,			[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Any  						=   get_dataset_infos(__lowerCAmelCase )
				assert expected_config in infos
				_snake_case :			Any  						=   infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys() ) == expected_splits

@pytest.mark.parametrize(
    'path, config_name, expected_exception'	,			[
        ('paws', None, ValueError),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				with pytest.raises(__lowerCAmelCase ):
								get_dataset_split_names(__lowerCAmelCase	,			config_name=__lowerCAmelCase )













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.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







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







						@property
						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   1
										_snake_case :			str  						=   3
										_snake_case :			List[str]  						=   (32, 32)

										_snake_case :			int  						=   floats_tensor((batch_size, num_channels) + sizes ,				rng=random.Random(0					)					).to(lowerCamelCase_					)
										return image







						@property
						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										torch.manual_seed(0					)
										_snake_case :			Union[str, Any]  						=   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 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										torch.manual_seed(0					)
										_snake_case :			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 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										torch.manual_seed(0					)
										_snake_case :			List[str]  						=   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(lowerCamelCase_					)







						@property
						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										def extract(*lowerCamelCase_			:		Optional[int] ,				**lowerCamelCase_			:		str					):


														class 			lowercase				:



																				"""simple docstring"""

																				def __init__(    self			:		Tuple					):





																								'''simple docstring'''







																								_snake_case :			List[str]  						=   torch.ones([0]					)







																				def 		__UpperCAmelCase       (    self			:		int ,				lowerCamelCase_			:		Tuple					):





																								'''simple docstring'''







																								self.pixel_values.to(lowerCamelCase_					)
																								return self



														return Out()

										return extract







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case :			int  						=   self.dummy_cond_unet
										_snake_case :			str  						=   DDIMScheduler(
										    beta_start=0.0_0085 ,				beta_end=0.012 ,				beta_schedule='scaled_linear' ,				clip_sample=lowerCamelCase_ ,				set_alpha_to_one=lowerCamelCase_ ,				)

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

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

										_snake_case :			List[str]  						=   'A painting of a squirrel eating a burger'

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

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

										_snake_case :			Tuple  						=   image[0, -3:, -3:, -1]
										_snake_case :			List[Any]  						=   image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										_snake_case :			Optional[int]  						=   np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]					)

										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 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case :			List[str]  						=   self.dummy_cond_unet
										_snake_case :			List[str]  						=   PNDMScheduler(skip_prk_steps=lowerCamelCase_					)
										_snake_case :			int  						=   self.dummy_vae
										_snake_case :			List[Any]  						=   self.dummy_text_encoder
										_snake_case :			Optional[int]  						=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

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

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

										_snake_case :			Optional[Any]  						=   output.images

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

										_snake_case :			Dict  						=   image[0, -3:, -3:, -1]
										_snake_case :			Any  						=   image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										_snake_case :			str  						=   np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]					)

										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 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   StableDiffusionPipeline.from_pretrained(
										    'hf-internal-testing/tiny-stable-diffusion-lms-pipe' ,				safety_checker=lowerCamelCase_					)
										assert isinstance(lowerCamelCase_ ,				lowerCamelCase_					)
										assert isinstance(pipe.scheduler ,				lowerCamelCase_					)
										assert pipe.safety_checker is None

										_snake_case :			Dict  						=   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(lowerCamelCase_					)
														_snake_case :			Any  						=   StableDiffusionPipeline.from_pretrained(lowerCamelCase_					)

										# sanity check that the pipeline still works
										assert pipe.safety_checker is None
										_snake_case :			Union[str, Any]  						=   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 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   self.dummy_cond_unet
										_snake_case :			Optional[Any]  						=   PNDMScheduler(skip_prk_steps=lowerCamelCase_					)
										_snake_case :			Any  						=   self.dummy_vae
										_snake_case :			Optional[Any]  						=   self.dummy_text_encoder
										_snake_case :			Optional[int]  						=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

										# put models in fp16
										_snake_case :			str  						=   unet.half()
										_snake_case :			Union[str, Any]  						=   vae.half()
										_snake_case :			Dict  						=   bert.half()

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

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

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



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



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' ,				safety_checker=lowerCamelCase_					)
										_snake_case :			List[str]  						=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config					)
										_snake_case :			Any  						=   sd_pipe.to(lowerCamelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

										_snake_case :			Optional[int]  						=   (
										    'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
										    ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
										    ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
										    ' children from bahnhof zoo, detailed '
										)
										_snake_case :			List[str]  						=   40_03_66_03_46
										_snake_case :			int  						=   7

										# without safety guidance (sld_guidance_scale = 0)
										_snake_case :			Union[str, Any]  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Union[str, Any]  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=0 ,				)

										_snake_case :			str  						=   output.images
										_snake_case :			Dict  						=   image[0, -3:, -3:, -1]
										_snake_case :			Optional[int]  						=   [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

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

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

										# without safety guidance (strong configuration)
										_snake_case :			Tuple  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			int  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=20_00 ,				sld_warmup_steps=7 ,				sld_threshold=0.025 ,				sld_momentum_scale=0.5 ,				sld_mom_beta=0.7 ,				)

										_snake_case :			Tuple  						=   output.images
										_snake_case :			int  						=   image[0, -3:, -3:, -1]
										_snake_case :			List[Any]  						=   [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

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

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







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			str  						=   StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' ,				safety_checker=lowerCamelCase_					)
										_snake_case :			Tuple  						=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config					)
										_snake_case :			Any  						=   sd_pipe.to(lowerCamelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

										_snake_case :			Union[str, Any]  						=   'padme amidala taking a bath artwork, safe for work, no nudity'
										_snake_case :			Optional[Any]  						=   27_34_97_17_55
										_snake_case :			Union[str, Any]  						=   7

										_snake_case :			Dict  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Tuple  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=0 ,				)

										_snake_case :			Any  						=   output.images
										_snake_case :			int  						=   image[0, -3:, -3:, -1]
										_snake_case :			str  						=   [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

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

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

										_snake_case :			Optional[Any]  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Any  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=20_00 ,				sld_warmup_steps=7 ,				sld_threshold=0.025 ,				sld_momentum_scale=0.5 ,				sld_mom_beta=0.7 ,				)

										_snake_case :			str  						=   output.images
										_snake_case :			List[str]  						=   image[0, -3:, -3:, -1]
										_snake_case :			Union[str, Any]  						=   [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

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

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







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'					)
										_snake_case :			Optional[int]  						=   sd_pipe.to(lowerCamelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

										_snake_case :			List[Any]  						=   (
										    'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
										    ' leyendecker'
										)
										_snake_case :			Union[str, Any]  						=   10_44_35_52_34
										_snake_case :			Dict  						=   12

										_snake_case :			Optional[int]  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Any  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=0 ,				)

										_snake_case :			Optional[int]  						=   output.images
										_snake_case :			int  						=   image[0, -3:, -3:, -1]
										_snake_case :			Optional[int]  						=   np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]					)

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

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-7

										_snake_case :			List[Any]  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Optional[int]  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=20_00 ,				sld_warmup_steps=7 ,				sld_threshold=0.025 ,				sld_momentum_scale=0.5 ,				sld_mom_beta=0.7 ,				)

										_snake_case :			str  						=   output.images
										_snake_case :			List[str]  						=   image[0, -3:, -3:, -1]
										_snake_case :			int  						=   np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]					)
										assert image.shape == (1, 5_12, 5_12, 3)

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






