





import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase_		:				Dict				=						logging.get_logger(__name__)

lowercase_		:				Tuple				=						{
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}


class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	Tuple								=	"conditional_detr"
						_UpperCamelCase  :	List[str]								=	["past_key_values"]
						_UpperCamelCase  :	int								=	{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}







						def __init__(    self			:		Union[str, Any] ,				lowerCamelCase_			:		List[str]=True ,				lowerCamelCase_			:		List[Any]=None ,				lowerCamelCase_			:		Tuple=3 ,				lowerCamelCase_			:		List[str]=3_00 ,				lowerCamelCase_			:		int=6 ,				lowerCamelCase_			:		Optional[Any]=20_48 ,				lowerCamelCase_			:		Any=8 ,				lowerCamelCase_			:		Tuple=6 ,				lowerCamelCase_			:		List[str]=20_48 ,				lowerCamelCase_			:		Dict=8 ,				lowerCamelCase_			:		str=0.0 ,				lowerCamelCase_			:		Optional[int]=0.0 ,				lowerCamelCase_			:		List[str]=True ,				lowerCamelCase_			:		Optional[int]="relu" ,				lowerCamelCase_			:		Dict=2_56 ,				lowerCamelCase_			:		Any=0.1 ,				lowerCamelCase_			:		Union[str, Any]=0.0 ,				lowerCamelCase_			:		str=0.0 ,				lowerCamelCase_			:		List[Any]=0.02 ,				lowerCamelCase_			:		Union[str, Any]=1.0 ,				lowerCamelCase_			:		Union[str, Any]=False ,				lowerCamelCase_			:		str="sine" ,				lowerCamelCase_			:		Dict="resnet50" ,				lowerCamelCase_			:		Dict=True ,				lowerCamelCase_			:		str=False ,				lowerCamelCase_			:		Union[str, Any]=2 ,				lowerCamelCase_			:		Tuple=5 ,				lowerCamelCase_			:		Any=2 ,				lowerCamelCase_			:		Union[str, Any]=1 ,				lowerCamelCase_			:		str=1 ,				lowerCamelCase_			:		Tuple=2 ,				lowerCamelCase_			:		Optional[Any]=5 ,				lowerCamelCase_			:		Optional[int]=2 ,				lowerCamelCase_			:		str=0.25 ,				**lowerCamelCase_			:		Dict ,				):





										'''simple docstring'''







										if backbone_config is not None and use_timm_backbone:
														raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'					)

										if not use_timm_backbone:
														if backbone_config is None:
																		logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'					)
																		_snake_case :			str  						=   CONFIG_MAPPING['resnet'](out_features=['stage4']					)
														elif isinstance(lowerCamelCase_ ,				lowerCamelCase_					):
																		_snake_case :			List[Any]  						=   backbone_config.get('model_type'					)
																		_snake_case :			str  						=   CONFIG_MAPPING[backbone_model_type]
																		_snake_case :			str  						=   config_class.from_dict(lowerCamelCase_					)

										_snake_case :			int  						=   use_timm_backbone
										_snake_case :			Optional[int]  						=   backbone_config
										_snake_case :			Tuple  						=   num_channels
										_snake_case :			List[str]  						=   num_queries
										_snake_case :			List[str]  						=   d_model
										_snake_case :			Dict  						=   encoder_ffn_dim
										_snake_case :			Union[str, Any]  						=   encoder_layers
										_snake_case :			Union[str, Any]  						=   encoder_attention_heads
										_snake_case :			Dict  						=   decoder_ffn_dim
										_snake_case :			Any  						=   decoder_layers
										_snake_case :			List[str]  						=   decoder_attention_heads
										_snake_case :			Any  						=   dropout
										_snake_case :			str  						=   attention_dropout
										_snake_case :			List[Any]  						=   activation_dropout
										_snake_case :			List[str]  						=   activation_function
										_snake_case :			Any  						=   init_std
										_snake_case :			int  						=   init_xavier_std
										_snake_case :			str  						=   encoder_layerdrop
										_snake_case :			Tuple  						=   decoder_layerdrop
										_snake_case :			Optional[Any]  						=   encoder_layers
										_snake_case :			Optional[Any]  						=   auxiliary_loss
										_snake_case :			Any  						=   position_embedding_type
										_snake_case :			Optional[Any]  						=   backbone
										_snake_case :			List[Any]  						=   use_pretrained_backbone
										_snake_case :			List[str]  						=   dilation
										# Hungarian matcher
										_snake_case :			List[Any]  						=   class_cost
										_snake_case :			int  						=   bbox_cost
										_snake_case :			Optional[int]  						=   giou_cost
										# Loss coefficients
										_snake_case :			Any  						=   mask_loss_coefficient
										_snake_case :			str  						=   dice_loss_coefficient
										_snake_case :			Dict  						=   cls_loss_coefficient
										_snake_case :			str  						=   bbox_loss_coefficient
										_snake_case :			Any  						=   giou_loss_coefficient
										_snake_case :			Optional[Any]  						=   focal_alpha
										super().__init__(is_encoder_decoder=lowerCamelCase_ ,				**lowerCamelCase_					)







						@property
						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										return self.encoder_attention_heads







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





										'''simple docstring'''







										return self.d_model







						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										_snake_case :			int  						=   copy.deepcopy(self.__dict__					)
										if self.backbone_config is not None:
														_snake_case :			Optional[int]  						=   self.backbone_config.to_dict()
										_snake_case :			Union[str, Any]  						=   self.__class__.model_type
										return output



class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	Optional[int]								=	version.parse("1.11"     )







						@property
						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										return OrderedDict(
										    [
										        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
										        ('pixel_mask', {0: 'batch'}),
										    ]					)







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





										'''simple docstring'''







										return 1e-5







						@property
						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										return 12













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






