



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCAmelCase__	:  Union[str, Any] 		=logging.get_logger(__name__)

class      UpperCAmelCase_						(     UpperCamelCase_ ):

						'''simple docstring'''




						UpperCamelCase__    :				int       						=						'''mt5'''
						UpperCamelCase__    :				Optional[int]       						=						['''past_key_values''']






						def __init__(					self   ,							_A=250_112   ,							_A=512   ,							_A=64   ,							_A=1_024   ,							_A=8   ,							_A=None   ,							_A=6   ,							_A=32   ,							_A=128   ,							_A=0.1   ,							_A=1e-6   ,							_A=1.0   ,							_A="gated-gelu"   ,							_A=True   ,							_A=True   ,							_A="T5Tokenizer"   ,							_A=False   ,							_A=0   ,							_A=1   ,							_A=0   ,							**_A   ,							):







													'''simple docstring'''

													super().__init__(
													    is_encoder_decoder=_A   ,							tokenizer_class=_A   ,							tie_word_embeddings=_A   ,							pad_token_id=_A   ,							eos_token_id=_A   ,							decoder_start_token_id=_A   ,							**_A   ,							)
													__SCREAMING_SNAKE_CASE								=      vocab_size
													__SCREAMING_SNAKE_CASE								=      d_model
													__SCREAMING_SNAKE_CASE								=      d_kv
													__SCREAMING_SNAKE_CASE								=      d_ff
													__SCREAMING_SNAKE_CASE								=      num_layers
													__SCREAMING_SNAKE_CASE								=      (
													    num_decoder_layers if num_decoder_layers is not None else self.num_layers
													)  # default = symmetry
													__SCREAMING_SNAKE_CASE								=      num_heads
													__SCREAMING_SNAKE_CASE								=      relative_attention_num_buckets
													__SCREAMING_SNAKE_CASE								=      relative_attention_max_distance
													__SCREAMING_SNAKE_CASE								=      dropout_rate
													__SCREAMING_SNAKE_CASE								=      layer_norm_epsilon
													__SCREAMING_SNAKE_CASE								=      initializer_factor
													__SCREAMING_SNAKE_CASE								=      feed_forward_proj
													__SCREAMING_SNAKE_CASE								=      use_cache

													__SCREAMING_SNAKE_CASE								=      self.feed_forward_proj.split('-'	)
													__SCREAMING_SNAKE_CASE								=      act_info[-1]
													__SCREAMING_SNAKE_CASE								=      act_info[0] == 'gated'

													if len(_A	) > 1 and act_info[0] != "gated" or len(_A	) > 2:
																				raise ValueError(
																				    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																				    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
																				    '\'gated-gelu\' or \'relu\''	)

													# for backwards compatibility
													if feed_forward_proj == "gated-gelu":
																				__SCREAMING_SNAKE_CASE								=      'gelu_new'






						@property
						def  _A			(					self	):







													'''simple docstring'''

													return self.d_model






						@property
						def  _A			(					self	):







													'''simple docstring'''

													return self.num_heads






						@property
						def  _A			(					self	):







													'''simple docstring'''

													return self.num_layers







class      UpperCAmelCase_						(     UpperCamelCase_ ):

						'''simple docstring'''




						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      {
													    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
													    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
													}
													if self.use_past:
																				__SCREAMING_SNAKE_CASE								=      'past_encoder_sequence + sequence'
																				__SCREAMING_SNAKE_CASE								=      {0: 'batch'}
																				__SCREAMING_SNAKE_CASE								=      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
													else:
																				__SCREAMING_SNAKE_CASE								=      {0: 'batch', 1: 'decoder_sequence'}
																				__SCREAMING_SNAKE_CASE								=      {0: 'batch', 1: 'decoder_sequence'}

													if self.use_past:
																				self.fill_with_past_key_values_(_A   ,							direction='inputs'	)

													return common_inputs






						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def  _A			(					self	):







													'''simple docstring'''

													return 13






						@property
						def  _A			(					self	):







													'''simple docstring'''

													return 5e-4











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






