


"""simple docstring"""







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  __A							(			unittest.TestCase     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     a											=1
     a											=3
     a											=(32, 32)

     a											=floats_tensor((batch_size, num_channels) + sizes					,       rng=random.Random(0		)		).to(__A		)
     return image


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     torch.manual_seed(0		)
     a											=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  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     torch.manual_seed(0		)
     a											=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  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     torch.manual_seed(0		)
     a											=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					,       )
     return CLIPTextModel(__A		)


 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     def extract(*__A					,       **__A		):






         class  __A							:






          """simple docstring"""




          def __init__(						self		)   ->	Dict:
              a											=torch.ones([0]		)





          def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	Optional[Any]:
              self.pixel_values.to(__A		)
              return self

         return Out()

     return extract


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     a											='''cpu'''  # ensure determinism for the device-dependent torch.Generator
     a											=self.dummy_cond_unet
     a											=DDIMScheduler(
         beta_start=0.00_085					,       beta_end=0.012					,       beta_schedule='''scaled_linear'''					,       clip_sample=__A					,       set_alpha_to_one=__A					,       )

     a											=self.dummy_vae
     a											=self.dummy_text_encoder
     a											=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

     # make sure here that pndm scheduler skips prk
     a											=StableDiffusionPipeline(
         unet=__A					,       scheduler=__A					,       vae=__A					,       text_encoder=__A					,       tokenizer=__A					,       safety_checker=__A					,       feature_extractor=self.dummy_extractor					,       )
     a											=sd_pipe.to(__A		)
     sd_pipe.set_progress_bar_config(disable=__A		)

     a											='''A painting of a squirrel eating a burger'''

     a											=torch.Generator(device=__A		).manual_seed(0		)
     a											=sd_pipe([prompt]					,       generator=__A					,       guidance_scale=6.0					,       num_inference_steps=2					,       output_type='''np'''		)
     a											=output.images

     a											=torch.Generator(device=__A		).manual_seed(0		)
     a											=sd_pipe(
         [prompt]					,       generator=__A					,       guidance_scale=6.0					,       num_inference_steps=2					,       output_type='''np'''					,       return_dict=__A					,       )[0]

     a											=image[0, -3:, -3:, -1]
     a											=image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     a											=np.array([0.5_753, 0.6_114, 0.5_001, 0.5_034, 0.5_470, 0.4_729, 0.4_971, 0.4_867, 0.4_867]		)

     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  SCREAMING_SNAKE_CASE				(						self		)   ->	List[Any]:
     a											='''cpu'''  # ensure determinism for the device-dependent torch.Generator
     a											=self.dummy_cond_unet
     a											=PNDMScheduler(skip_prk_steps=__A		)
     a											=self.dummy_vae
     a											=self.dummy_text_encoder
     a											=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

     # make sure here that pndm scheduler skips prk
     a											=StableDiffusionPipeline(
         unet=__A					,       scheduler=__A					,       vae=__A					,       text_encoder=__A					,       tokenizer=__A					,       safety_checker=__A					,       feature_extractor=self.dummy_extractor					,       )
     a											=sd_pipe.to(__A		)
     sd_pipe.set_progress_bar_config(disable=__A		)

     a											='''A painting of a squirrel eating a burger'''
     a											=torch.Generator(device=__A		).manual_seed(0		)
     a											=sd_pipe([prompt]					,       generator=__A					,       guidance_scale=6.0					,       num_inference_steps=2					,       output_type='''np'''		)

     a											=output.images

     a											=torch.Generator(device=__A		).manual_seed(0		)
     a											=sd_pipe(
         [prompt]					,       generator=__A					,       guidance_scale=6.0					,       num_inference_steps=2					,       output_type='''np'''					,       return_dict=__A					,       )[0]

     a											=image[0, -3:, -3:, -1]
     a											=image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     a											=np.array([0.5_122, 0.5_712, 0.4_825, 0.5_053, 0.5_646, 0.4_769, 0.5_179, 0.4_894, 0.4_994]		)

     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  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     a											=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

     a											=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		)
         a											=StableDiffusionPipeline.from_pretrained(__A		)

     # sanity check that the pipeline still works
     assert pipe.safety_checker is None
     a											=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  SCREAMING_SNAKE_CASE				(						self		)   ->	List[str]:
     a											=self.dummy_cond_unet
     a											=PNDMScheduler(skip_prk_steps=__A		)
     a											=self.dummy_vae
     a											=self.dummy_text_encoder
     a											=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

     # put models in fp16
     a											=unet.half()
     a											=vae.half()
     a											=bert.half()

     # make sure here that pndm scheduler skips prk
     a											=StableDiffusionPipeline(
         unet=__A					,       scheduler=__A					,       vae=__A					,       text_encoder=__A					,       tokenizer=__A					,       safety_checker=__A					,       feature_extractor=self.dummy_extractor					,       )
     a											=sd_pipe.to(__A		)
     sd_pipe.set_progress_bar_config(disable=__A		)

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

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






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






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     a											='''cuda'''
     a											=StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''		)
     a											=pipe.to(__A		)
     pipe.set_progress_bar_config(disable=__A		)

     a											='''a photo of a cat'''
     a											={
         '''editing_prompt''': ['''sunglasses'''],
         '''reverse_editing_direction''': [False],
         '''edit_warmup_steps''': 10,
         '''edit_guidance_scale''': 6,
         '''edit_threshold''': 0.95,
         '''edit_momentum_scale''': 0.5,
         '''edit_mom_beta''': 0.6,
     }

     a											=3
     a											=7

     # no sega enabled
     a											=torch.Generator(__A		)
     generator.manual_seed(__A		)
     a											=pipe(
         [prompt]					,       generator=__A					,       guidance_scale=__A					,       num_inference_steps=50					,       output_type='''np'''					,       width=512					,       height=512					,       )

     a											=output.images
     a											=image[0, -3:, -3:, -1]
     a											=[
         0.34_673_113,
         0.38_492_733,
         0.37_597_352,
         0.34_086_335,
         0.35_650_748,
         0.35_579_205,
         0.3_384_763,
         0.34_340_236,
         0.3_573_271,
     ]

     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		)
     a											=pipe(
         [prompt]					,       generator=__A					,       guidance_scale=__A					,       num_inference_steps=50					,       output_type='''np'''					,       width=512					,       height=512					,       **__A					,       )

     a											=output.images
     a											=image[0, -3:, -3:, -1]
     a											=[
         0.41_887_826,
         0.37_728_766,
         0.30_138_272,
         0.41_416_335,
         0.41_664_985,
         0.36_283_392,
         0.36_191_246,
         0.43_364_465,
         0.43_001_732,
     ]

     assert image.shape == (1, 512, 512, 3)

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


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     a											='''cuda'''
     a											=StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''		)
     a											=pipe.to(__A		)
     pipe.set_progress_bar_config(disable=__A		)

     a											='''an image of a crowded boulevard, realistic, 4k'''
     a											={
         '''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,
     }

     a											=9
     a											=7

     # no sega enabled
     a											=torch.Generator(__A		)
     generator.manual_seed(__A		)
     a											=pipe(
         [prompt]					,       generator=__A					,       guidance_scale=__A					,       num_inference_steps=50					,       output_type='''np'''					,       width=512					,       height=512					,       )

     a											=output.images
     a											=image[0, -3:, -3:, -1]
     a											=[
         0.43_497_998,
         0.91_814_065,
         0.7_540_739,
         0.55_580_205,
         0.8_467_265,
         0.5_389_691,
         0.62_574_506,
         0.58_897_763,
         0.50_926_757,
     ]

     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		)
     a											=pipe(
         [prompt]					,       generator=__A					,       guidance_scale=__A					,       num_inference_steps=50					,       output_type='''np'''					,       width=512					,       height=512					,       **__A					,       )

     a											=output.images
     a											=image[0, -3:, -3:, -1]
     a											=[
         0.3_089_719,
         0.30_500_144,
         0.29_016_042,
         0.30_630_964,
         0.325_687,
         0.29_419_225,
         0.2_908_091,
         0.28_723_598,
         0.27_696_294,
     ]

     assert image.shape == (1, 512, 512, 3)

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


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     a											='''cuda'''
     a											=StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''		)
     a											=pipe.to(__A		)
     pipe.set_progress_bar_config(disable=__A		)

     a											='''a castle next to a river'''
     a											={
         '''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,
     }

     a											=48
     a											=7

     # no sega enabled
     a											=torch.Generator(__A		)
     generator.manual_seed(__A		)
     a											=pipe(
         [prompt]					,       generator=__A					,       guidance_scale=__A					,       num_inference_steps=50					,       output_type='''np'''					,       width=512					,       height=512					,       )

     a											=output.images
     a											=image[0, -3:, -3:, -1]
     a											=[
         0.75_163_555,
         0.76_037_145,
         0.61_785,
         0.9_189_673,
         0.8_627_701,
         0.85_189_694,
         0.8_512_813,
         0.87_012_076,
         0.8_312_857,
     ]

     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		)
     a											=pipe(
         [prompt]					,       generator=__A					,       guidance_scale=__A					,       num_inference_steps=50					,       output_type='''np'''					,       width=512					,       height=512					,       **__A					,       )

     a											=output.images
     a											=image[0, -3:, -3:, -1]
     a											=[
         0.73_553_365,
         0.7_537_271,
         0.74_341_905,
         0.66_480_356,
         0.6_472_925,
         0.63_039_416,
         0.64_812_905,
         0.6_749_717,
         0.6_517_102,
     ]

     assert image.shape == (1, 512, 512, 3)

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





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     a											='''cuda'''
     a											=StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''					,       torch_dtype=torch.floataa		)
     a											=pipe.to(__A		)
     pipe.set_progress_bar_config(disable=__A		)

     a											='''a photo of a cat'''
     a											={
         '''editing_prompt''': ['''sunglasses'''],
         '''reverse_editing_direction''': [False],
         '''edit_warmup_steps''': 10,
         '''edit_guidance_scale''': 6,
         '''edit_threshold''': 0.95,
         '''edit_momentum_scale''': 0.5,
         '''edit_mom_beta''': 0.6,
     }

     a											=3
     a											=7

     # no sega enabled
     a											=torch.Generator(__A		)
     generator.manual_seed(__A		)
     a											=pipe(
         [prompt]					,       generator=__A					,       guidance_scale=__A					,       num_inference_steps=50					,       output_type='''np'''					,       width=512					,       height=512					,       )

     a											=output.images
     a											=image[0, -3:, -3:, -1]
     a											=[
         0.34_887_695,
         0.3_876_953,
         0.375,
         0.34_423_828,
         0.3_581_543,
         0.35_717_773,
         0.3_383_789,
         0.34_570_312,
         0.359_375,
     ]

     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		)
     a											=pipe(
         [prompt]					,       generator=__A					,       guidance_scale=__A					,       num_inference_steps=50					,       output_type='''np'''					,       width=512					,       height=512					,       **__A					,       )

     a											=output.images
     a											=image[0, -3:, -3:, -1]
     a											=[
         0.42_285_156,
         0.36_914_062,
         0.29_077_148,
         0.42_041_016,
         0.41_918_945,
         0.35_498_047,
         0.3_618_164,
         0.4_423_828,
         0.43_115_234,
     ]

     assert image.shape == (1, 512, 512, 3)

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



"""simple docstring"""







import itertools
from pathlib import Path
def _A		(		lowercase						):







    """simple docstring"""
    a											=[tuple(lowercase						) for login in logins]

    a											={char for login in split_logins for char in login}

    for permutation in itertools.permutations(lowercase						):
        a											=True
        for login in logins:
            if not (
                permutation.index(login[0]						)
                < permutation.index(login[1]						)
                < permutation.index(login[2]						)
            ):
                a											=False
                break

        if satisfied:
            return int(''''''.join(lowercase						)						)

    raise Exception('''Unable to find the secret passcode'''						)
def _A		(		lowercase = "keylog.txt"						):







    """simple docstring"""
    a											=Path(lowercase						).parent.joinpath(lowercase						).read_text().splitlines()

    return find_secret_passcode(lowercase						)


if __name__ == "__main__":
       print(F'{solution() = }')