code: 
  '''simple docstring'''
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      EulerAncestralDiscreteScheduler,
      PNDMScheduler,
      StableDiffusionModelEditingPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  
  
  @skip_mps
  class   snake_case__					(			__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE						,				unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	StableDiffusionModelEditingPipeline
  		lowerCamelCase           =	TEXT_TO_IMAGE_PARAMS
  		lowerCamelCase           =	TEXT_TO_IMAGE_BATCH_PARAMS
  		lowerCamelCase           =	TEXT_TO_IMAGE_IMAGE_PARAMS
  		lowerCamelCase           =	TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				torch.manual_seed(0			)
  				snake_case     :					List[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     ,  )
  				snake_case     :					Union[str, Any]     =	DDIMScheduler()
  				torch.manual_seed(0			)
  				snake_case     :					List[Any]     =	AutoencoderKL(
  				    block_out_channels=[32, 64]     ,  in_channels=3     ,  out_channels=3     ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,  latent_channels=4     ,  )
  				torch.manual_seed(0			)
  				snake_case     :					Dict     =	CLIPTextConfig(
  				    bos_token_id=0     ,  eos_token_id=2     ,  hidden_size=32     ,  intermediate_size=37     ,  layer_norm_eps=1e-0_5     ,  num_attention_heads=4     ,  num_hidden_layers=5     ,  pad_token_id=1     ,  vocab_size=1000     ,  )
  				snake_case     :					Tuple     =	CLIPTextModel(UpperCamelCase__			)
  				snake_case     :					List[str]     =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)
  
  				snake_case     :					List[str]     =	{
  				    '''unet''': unet,
  				    '''scheduler''': scheduler,
  				    '''vae''': vae,
  				    '''text_encoder''': text_encoder,
  				    '''tokenizer''': tokenizer,
  				    '''safety_checker''': None,
  				    '''feature_extractor''': None,
  				}
  				return components
  
  
  
  		def 			lowerCAmelCase							(			self							: Union[str, Any]     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: Union[str, Any]=0			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[str]     =	torch.manual_seed(UpperCamelCase__			)
  				snake_case     :					str     =	{
  				    '''prompt''': '''A field of roses''',
  				    '''generator''': generator,
  				    # Setting height and width to None to prevent OOMs on CPU.
  				    '''height''': None,
  				    '''width''': None,
  				    '''num_inference_steps''': 2,
  				    '''guidance_scale''': 6.0,
  				    '''output_type''': '''numpy''',
  				}
  				return inputs
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					str     =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
  				snake_case     :					Optional[int]     =	self.get_dummy_components()
  				snake_case     :					Dict     =	StableDiffusionModelEditingPipeline(**UpperCamelCase__			)
  				snake_case     :					Optional[Any]     =	sd_pipe.to(UpperCamelCase__			)
  				sd_pipe.set_progress_bar_config(disable=UpperCamelCase__			)
  
  				snake_case     :					Dict     =	self.get_dummy_inputs(UpperCamelCase__			)
  				snake_case     :					Dict     =	sd_pipe(**UpperCamelCase__			).images
  				snake_case     :					Optional[Any]     =	image[0, -3:, -3:, -1]
  				assert image.shape == (1, 64, 64, 3)
  
  				snake_case     :					Dict     =	np.array([0.4_755, 0.5_132, 0.4_976, 0.3_904, 0.3_554, 0.4_765, 0.5_139, 0.5_158, 0.4_889]			)
  
  				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
  
  
  
  		def 			lowerCAmelCase							(			self							: Any			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					str     =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
  				snake_case     :					Optional[Any]     =	self.get_dummy_components()
  				snake_case     :					Optional[int]     =	StableDiffusionModelEditingPipeline(**UpperCamelCase__			)
  				snake_case     :					str     =	sd_pipe.to(UpperCamelCase__			)
  				sd_pipe.set_progress_bar_config(disable=UpperCamelCase__			)
  
  				snake_case     :					List[Any]     =	self.get_dummy_inputs(UpperCamelCase__			)
  				snake_case     :					int     =	'''french fries'''
  				snake_case     :					Optional[Any]     =	sd_pipe(**UpperCamelCase__     ,  negative_prompt=UpperCamelCase__			)
  				snake_case     :					List[str]     =	output.images
  				snake_case     :					Any     =	image[0, -3:, -3:, -1]
  
  				assert image.shape == (1, 64, 64, 3)
  
  				snake_case     :					Union[str, Any]     =	np.array([0.4_992, 0.5_101, 0.5_004, 0.3_949, 0.3_604, 0.4_735, 0.5_216, 0.5_204, 0.4_913]			)
  
  				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
  				snake_case     :					Union[str, Any]     =	self.get_dummy_components()
  				snake_case     :					Any     =	EulerAncestralDiscreteScheduler(
  				    beta_start=0.00_085     ,  beta_end=0.012     ,  beta_schedule='''scaled_linear'''			)
  				snake_case     :					Tuple     =	StableDiffusionModelEditingPipeline(**UpperCamelCase__			)
  				snake_case     :					Optional[int]     =	sd_pipe.to(UpperCamelCase__			)
  				sd_pipe.set_progress_bar_config(disable=UpperCamelCase__			)
  
  				snake_case     :					Any     =	self.get_dummy_inputs(UpperCamelCase__			)
  				snake_case     :					Optional[int]     =	sd_pipe(**UpperCamelCase__			).images
  				snake_case     :					Any     =	image[0, -3:, -3:, -1]
  
  				assert image.shape == (1, 64, 64, 3)
  
  				snake_case     :					List[Any]     =	np.array([0.4_747, 0.5_372, 0.4_779, 0.4_982, 0.5_543, 0.4_816, 0.5_238, 0.4_904, 0.5_027]			)
  
  				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
  
  
  
  		def 			lowerCAmelCase							(			self							: str			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[Any]     =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
  				snake_case     :					Optional[Any]     =	self.get_dummy_components()
  				snake_case     :					int     =	PNDMScheduler()
  				snake_case     :					Optional[Any]     =	StableDiffusionModelEditingPipeline(**UpperCamelCase__			)
  				snake_case     :					Dict     =	sd_pipe.to(UpperCamelCase__			)
  				sd_pipe.set_progress_bar_config(disable=UpperCamelCase__			)
  
  				snake_case     :					Tuple     =	self.get_dummy_inputs(UpperCamelCase__			)
  				# the pipeline does not expect pndm so test if it raises error.
  				with self.assertRaises(UpperCamelCase__			):
  						snake_case     :					Dict     =	sd_pipe(**UpperCamelCase__			).images
  
  
  
  		def 			lowerCAmelCase							(			self							: List[Any]			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				super().test_inference_batch_single_identical(expected_max_diff=5e-3			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Union[str, Any]			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				super().test_attention_slicing_forward_pass(expected_max_diff=5e-3			)
  
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class   snake_case__					(			unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def 			lowerCAmelCase							(			self							: List[Any]			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				super().tearDown()
  				gc.collect()
  				torch.cuda.empty_cache()
  
  
  
  		def 			lowerCAmelCase							(			self							: List[str]     ,  UpperCamelCase__							: Dict=0			)	-> Tuple:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Tuple     =	torch.manual_seed(UpperCamelCase__			)
  				snake_case     :					Any     =	{
  				    '''prompt''': '''A field of roses''',
  				    '''generator''': generator,
  				    '''num_inference_steps''': 3,
  				    '''guidance_scale''': 7.5,
  				    '''output_type''': '''numpy''',
  				}
  				return inputs
  
  
  
  		def 			lowerCAmelCase							(			self							: Tuple			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[str]     =	'''CompVis/stable-diffusion-v1-4'''
  				snake_case     :					Union[str, Any]     =	StableDiffusionModelEditingPipeline.from_pretrained(UpperCamelCase__     ,  safety_checker=UpperCamelCase__			)
  				pipe.to(UpperCamelCase__			)
  				pipe.set_progress_bar_config(disable=UpperCamelCase__			)
  				pipe.enable_attention_slicing()
  
  				snake_case     :					Optional[int]     =	self.get_inputs()
  				snake_case     :					Tuple     =	pipe(**UpperCamelCase__			).images
  				snake_case     :					Optional[int]     =	image[0, -3:, -3:, -1].flatten()
  
  				assert image.shape == (1, 512, 512, 3)
  
  				snake_case     :					List[str]     =	np.array(
  				    [0.6_749_496, 0.6_386_453, 0.51_443_267, 0.66_094_905, 0.61_921_215, 0.5_491_332, 0.5_744_417, 0.58_075_106, 0.5_174_658]			)
  
  				assert np.abs(expected_slice - image_slice			).max() < 1e-2
  
  				# make sure image changes after editing
  				pipe.edit_model('''A pack of roses'''     ,  '''A pack of blue roses'''			)
  
  				snake_case     :					List[Any]     =	pipe(**UpperCamelCase__			).images
  				snake_case     :					List[Any]     =	image[0, -3:, -3:, -1].flatten()
  
  				assert image.shape == (1, 512, 512, 3)
  
  				assert np.abs(expected_slice - image_slice			).max() > 1e-1
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> Any:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				torch.cuda.empty_cache()
  				torch.cuda.reset_max_memory_allocated()
  				torch.cuda.reset_peak_memory_stats()
  
  				snake_case     :					Optional[Any]     =	'''CompVis/stable-diffusion-v1-4'''
  				snake_case     :					Dict     =	DDIMScheduler.from_pretrained(UpperCamelCase__     ,  subfolder='''scheduler'''			)
  				snake_case     :					List[str]     =	StableDiffusionModelEditingPipeline.from_pretrained(
  				    UpperCamelCase__     ,  scheduler=UpperCamelCase__     ,  safety_checker=UpperCamelCase__			)
  				snake_case     :					int     =	pipe.to(UpperCamelCase__			)
  				pipe.set_progress_bar_config(disable=UpperCamelCase__			)
  				pipe.enable_attention_slicing(1			)
  				pipe.enable_sequential_cpu_offload()
  
  				snake_case     :					Optional[Any]     =	self.get_inputs()
  				snake_case     :					Optional[int]     =	pipe(**UpperCamelCase__			)
  
  				snake_case     :					Optional[Any]     =	torch.cuda.max_memory_allocated()
  				# make sure that less than 4.4 GB is allocated
  				assert mem_bytes < 4.4 * 10**9
  
  
  
style_context: 
  '''simple docstring'''
  import itertools
  from pathlib import Path
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[Any]     =	[tuple(SCREAMING_SNAKE_CASE__ ) for login in logins]
  
  		snake_case     :					Dict     =	{char for login in split_logins for char in login}
  
  		for permutation in itertools.permutations(SCREAMING_SNAKE_CASE__ ):
  				snake_case     :					Optional[int]     =	True
  				for login in logins:
  						if not (
  						    permutation.index(login[0] )
  						    < permutation.index(login[1] )
  						    < permutation.index(login[2] )
  						):
  								snake_case     :					int     =	False
  								break
  
  				if satisfied:
  						return int(''''''.join(SCREAMING_SNAKE_CASE__ ) )
  
  		raise Exception('''Unable to find the secret passcode''' )
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ = "keylog.txt" )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[Any]     =	Path(SCREAMING_SNAKE_CASE__ ).parent.joinpath(SCREAMING_SNAKE_CASE__ ).read_text().splitlines()
  
  		return find_secret_passcode(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
  						print(f"{solution() = }")
  
  
  