
'''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




'''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() = }")


