
'''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 					__a			(       self      ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()






   @property
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  str								=		1
    SCREAMING_SNAKE_CASE  :  str								=		3
    SCREAMING_SNAKE_CASE  :  int								=		(32, 32)

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		floats_tensor((batch_size, num_channels) + sizes    ,rng=random.Random(0      )      ).to(__SCREAMING_SNAKE_CASE      )
    return image






   @property
   def 					__a			(       self      ):
    torch.manual_seed(0      )
    SCREAMING_SNAKE_CASE  :  Dict								=		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      ):
    torch.manual_seed(0      )
    SCREAMING_SNAKE_CASE  :  Dict								=		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      ):
    torch.manual_seed(0      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		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=1000    ,)
    return CLIPTextModel(__SCREAMING_SNAKE_CASE      )






   @property
   def 					__a			(       self      ):
    def extract(*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      ):


     class 		_a     :







        """simple docstring"""




        def __init__(       self      ):
         SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch.ones([0]      )






        def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
         self.pixel_values.to(__SCREAMING_SNAKE_CASE      )
         return self



     return Out()

    return extract






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		'cpu'  # ensure determinism for the device-dependent torch.Generator
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.dummy_cond_unet
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		DDIMScheduler(
        beta_start=0.0_0085    ,beta_end=0.012    ,beta_schedule='scaled_linear'    ,clip_sample=__SCREAMING_SNAKE_CASE    ,set_alpha_to_one=__SCREAMING_SNAKE_CASE    ,)

    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.dummy_vae
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.dummy_text_encoder
    SCREAMING_SNAKE_CASE  :  List[str]								=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

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

    SCREAMING_SNAKE_CASE  :  Dict								=		'A painting of a squirrel eating a burger'

    SCREAMING_SNAKE_CASE  :  Dict								=		torch.Generator(device=__SCREAMING_SNAKE_CASE      ).manual_seed(0      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		sd_pipe([prompt]    ,generator=__SCREAMING_SNAKE_CASE    ,guidance_scale=6.0    ,num_inference_steps=2    ,output_type='np'      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		output.images

    SCREAMING_SNAKE_CASE  :  Tuple								=		torch.Generator(device=__SCREAMING_SNAKE_CASE      ).manual_seed(0      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		sd_pipe(
        [prompt]    ,generator=__SCREAMING_SNAKE_CASE    ,guidance_scale=6.0    ,num_inference_steps=2    ,output_type='np'    ,return_dict=__SCREAMING_SNAKE_CASE    ,)[0]

    SCREAMING_SNAKE_CASE  :  Tuple								=		image[0, -3:, -3:, -1]
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]      )

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

    # make sure here that pndm scheduler skips prk
    SCREAMING_SNAKE_CASE  :  List[str]								=		StableDiffusionPipeline(
        unet=__SCREAMING_SNAKE_CASE    ,scheduler=__SCREAMING_SNAKE_CASE    ,vae=__SCREAMING_SNAKE_CASE    ,text_encoder=__SCREAMING_SNAKE_CASE    ,tokenizer=__SCREAMING_SNAKE_CASE    ,safety_checker=__SCREAMING_SNAKE_CASE    ,feature_extractor=self.dummy_extractor    ,)
    SCREAMING_SNAKE_CASE  :  Any								=		sd_pipe.to(__SCREAMING_SNAKE_CASE      )
    sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		'A painting of a squirrel eating a burger'
    SCREAMING_SNAKE_CASE  :  List[Any]								=		torch.Generator(device=__SCREAMING_SNAKE_CASE      ).manual_seed(0      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		sd_pipe([prompt]    ,generator=__SCREAMING_SNAKE_CASE    ,guidance_scale=6.0    ,num_inference_steps=2    ,output_type='np'      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		output.images

    SCREAMING_SNAKE_CASE  :  int								=		torch.Generator(device=__SCREAMING_SNAKE_CASE      ).manual_seed(0      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		sd_pipe(
        [prompt]    ,generator=__SCREAMING_SNAKE_CASE    ,guidance_scale=6.0    ,num_inference_steps=2    ,output_type='np'    ,return_dict=__SCREAMING_SNAKE_CASE    ,)[0]

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		image[0, -3:, -3:, -1]
    SCREAMING_SNAKE_CASE  :  List[Any]								=		image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    SCREAMING_SNAKE_CASE  :  Tuple								=		np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]      )

    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      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		StableDiffusionPipeline.from_pretrained(
        'hf-internal-testing/tiny-stable-diffusion-lms-pipe'    ,safety_checker=__SCREAMING_SNAKE_CASE      )
    assert isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    assert isinstance(pipe.scheduler    ,__SCREAMING_SNAKE_CASE      )
    assert pipe.safety_checker is None

    SCREAMING_SNAKE_CASE  :  str								=		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(__SCREAMING_SNAKE_CASE      )
     SCREAMING_SNAKE_CASE  :  Tuple								=		StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE      )

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

    # put models in fp16
    SCREAMING_SNAKE_CASE  :  List[Any]								=		unet.half()
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		vae.half()
    SCREAMING_SNAKE_CASE  :  Dict								=		bert.half()

    # make sure here that pndm scheduler skips prk
    SCREAMING_SNAKE_CASE  :  Any								=		StableDiffusionPipeline(
        unet=__SCREAMING_SNAKE_CASE    ,scheduler=__SCREAMING_SNAKE_CASE    ,vae=__SCREAMING_SNAKE_CASE    ,text_encoder=__SCREAMING_SNAKE_CASE    ,tokenizer=__SCREAMING_SNAKE_CASE    ,safety_checker=__SCREAMING_SNAKE_CASE    ,feature_extractor=self.dummy_extractor    ,)
    SCREAMING_SNAKE_CASE  :  Dict								=		sd_pipe.to(__SCREAMING_SNAKE_CASE      )
    sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Any								=		'A painting of a squirrel eating a burger'
    SCREAMING_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 		_a     (							unittest.TestCase  ):







   """simple docstring"""




   def 					__a			(       self      ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		'cuda'
    SCREAMING_SNAKE_CASE  :  Tuple								=		StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'      )
    SCREAMING_SNAKE_CASE  :  str								=		pipe.to(__SCREAMING_SNAKE_CASE      )
    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Dict								=		'a photo of a cat'
    SCREAMING_SNAKE_CASE  :  List[Any]								=		{
        '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,
    }

    SCREAMING_SNAKE_CASE  :  int								=		3
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		7

    # no sega enabled
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		torch.Generator(__SCREAMING_SNAKE_CASE      )
    generator.manual_seed(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		pipe(
        [prompt]    ,generator=__SCREAMING_SNAKE_CASE    ,guidance_scale=__SCREAMING_SNAKE_CASE    ,num_inference_steps=50    ,output_type='np'    ,width=512    ,height=512    ,)

    SCREAMING_SNAKE_CASE  :  List[str]								=		output.images
    SCREAMING_SNAKE_CASE  :  Any								=		image[0, -3:, -3:, -1]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		[
        0.3467_3113,
        0.3849_2733,
        0.3759_7352,
        0.3408_6335,
        0.3565_0748,
        0.3557_9205,
        0.338_4763,
        0.3434_0236,
        0.357_3271,
    ]

    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(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Dict								=		pipe(
        [prompt]    ,generator=__SCREAMING_SNAKE_CASE    ,guidance_scale=__SCREAMING_SNAKE_CASE    ,num_inference_steps=50    ,output_type='np'    ,width=512    ,height=512    ,**__SCREAMING_SNAKE_CASE    ,)

    SCREAMING_SNAKE_CASE  :  Dict								=		output.images
    SCREAMING_SNAKE_CASE  :  Tuple								=		image[0, -3:, -3:, -1]
    SCREAMING_SNAKE_CASE  :  List[str]								=		[
        0.4188_7826,
        0.3772_8766,
        0.3013_8272,
        0.4141_6335,
        0.4166_4985,
        0.3628_3392,
        0.3619_1246,
        0.4336_4465,
        0.4300_1732,
    ]

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

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






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		'cuda'
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		pipe.to(__SCREAMING_SNAKE_CASE      )
    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		'an image of a crowded boulevard, realistic, 4k'
    SCREAMING_SNAKE_CASE  :  Tuple								=		{
        '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  :  Union[str, Any]								=		9
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		7

    # no sega enabled
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		torch.Generator(__SCREAMING_SNAKE_CASE      )
    generator.manual_seed(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Dict								=		pipe(
        [prompt]    ,generator=__SCREAMING_SNAKE_CASE    ,guidance_scale=__SCREAMING_SNAKE_CASE    ,num_inference_steps=50    ,output_type='np'    ,width=512    ,height=512    ,)

    SCREAMING_SNAKE_CASE  :  Any								=		output.images
    SCREAMING_SNAKE_CASE  :  Any								=		image[0, -3:, -3:, -1]
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		[
        0.4349_7998,
        0.9181_4065,
        0.754_0739,
        0.5558_0205,
        0.846_7265,
        0.538_9691,
        0.6257_4506,
        0.5889_7763,
        0.5092_6757,
    ]

    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(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Any								=		pipe(
        [prompt]    ,generator=__SCREAMING_SNAKE_CASE    ,guidance_scale=__SCREAMING_SNAKE_CASE    ,num_inference_steps=50    ,output_type='np'    ,width=512    ,height=512    ,**__SCREAMING_SNAKE_CASE    ,)

    SCREAMING_SNAKE_CASE  :  List[Any]								=		output.images
    SCREAMING_SNAKE_CASE  :  Dict								=		image[0, -3:, -3:, -1]
    SCREAMING_SNAKE_CASE  :  List[Any]								=		[
        0.308_9719,
        0.3050_0144,
        0.2901_6042,
        0.3063_0964,
        0.32_5687,
        0.2941_9225,
        0.290_8091,
        0.2872_3598,
        0.2769_6294,
    ]

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

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






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  str								=		'cuda'
    SCREAMING_SNAKE_CASE  :  List[str]								=		StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		pipe.to(__SCREAMING_SNAKE_CASE      )
    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Any								=		'a castle next to a river'
    SCREAMING_SNAKE_CASE  :  str								=		{
        '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  :  Optional[Any]								=		48
    SCREAMING_SNAKE_CASE  :  Dict								=		7

    # no sega enabled
    SCREAMING_SNAKE_CASE  :  str								=		torch.Generator(__SCREAMING_SNAKE_CASE      )
    generator.manual_seed(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		pipe(
        [prompt]    ,generator=__SCREAMING_SNAKE_CASE    ,guidance_scale=__SCREAMING_SNAKE_CASE    ,num_inference_steps=50    ,output_type='np'    ,width=512    ,height=512    ,)

    SCREAMING_SNAKE_CASE  :  Any								=		output.images
    SCREAMING_SNAKE_CASE  :  str								=		image[0, -3:, -3:, -1]
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		[
        0.7516_3555,
        0.7603_7145,
        0.6_1785,
        0.918_9673,
        0.862_7701,
        0.8518_9694,
        0.851_2813,
        0.8701_2076,
        0.831_2857,
    ]

    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(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		pipe(
        [prompt]    ,generator=__SCREAMING_SNAKE_CASE    ,guidance_scale=__SCREAMING_SNAKE_CASE    ,num_inference_steps=50    ,output_type='np'    ,width=512    ,height=512    ,**__SCREAMING_SNAKE_CASE    ,)

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		output.images
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		image[0, -3:, -3:, -1]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		[
        0.7355_3365,
        0.753_7271,
        0.7434_1905,
        0.6648_0356,
        0.647_2925,
        0.6303_9416,
        0.6481_2905,
        0.674_9717,
        0.651_7102,
    ]

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

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






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		'cuda'
    SCREAMING_SNAKE_CASE  :  Tuple								=		StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'    ,torch_dtype=torch.floataa      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		pipe.to(__SCREAMING_SNAKE_CASE      )
    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		'a photo of a cat'
    SCREAMING_SNAKE_CASE  :  Tuple								=		{
        '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,
    }

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		3
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		7

    # no sega enabled
    SCREAMING_SNAKE_CASE  :  int								=		torch.Generator(__SCREAMING_SNAKE_CASE      )
    generator.manual_seed(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Any								=		pipe(
        [prompt]    ,generator=__SCREAMING_SNAKE_CASE    ,guidance_scale=__SCREAMING_SNAKE_CASE    ,num_inference_steps=50    ,output_type='np'    ,width=512    ,height=512    ,)

    SCREAMING_SNAKE_CASE  :  Dict								=		output.images
    SCREAMING_SNAKE_CASE  :  Tuple								=		image[0, -3:, -3:, -1]
    SCREAMING_SNAKE_CASE  :  Any								=		[
        0.3488_7695,
        0.387_6953,
        0.375,
        0.3442_3828,
        0.358_1543,
        0.3571_7773,
        0.338_3789,
        0.3457_0312,
        0.35_9375,
    ]

    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(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  str								=		pipe(
        [prompt]    ,generator=__SCREAMING_SNAKE_CASE    ,guidance_scale=__SCREAMING_SNAKE_CASE    ,num_inference_steps=50    ,output_type='np'    ,width=512    ,height=512    ,**__SCREAMING_SNAKE_CASE    ,)

    SCREAMING_SNAKE_CASE  :  str								=		output.images
    SCREAMING_SNAKE_CASE  :  Dict								=		image[0, -3:, -3:, -1]
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		[
        0.4228_5156,
        0.3691_4062,
        0.2907_7148,
        0.4204_1016,
        0.4191_8945,
        0.3549_8047,
        0.361_8164,
        0.442_3828,
        0.4311_5234,
    ]

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

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




'''simple docstring'''

import random
import sys

__UpperCAmelCase												=   'ABCDEFGHIJKLMNOPQRSTUVWXYZ'



def        SCREAMING_SNAKE_CASE_  (	)       ->	None:
 SCREAMING_SNAKE_CASE  :  Dict								=		input('Enter message: '							)
 SCREAMING_SNAKE_CASE  :  Dict								=		'LFWOAYUISVKMNXPBDCRJTQEGHZ'
 SCREAMING_SNAKE_CASE  :  Tuple								=		input('Encrypt/Decrypt [e/d]: '							)

 check_valid_key(snake_case_							)

 if resp.lower().startswith('e'							):
  SCREAMING_SNAKE_CASE  :  str								=		'encrypt'
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		encrypt_message(snake_case_   ,     snake_case_							)
 elif resp.lower().startswith('d'							):
  SCREAMING_SNAKE_CASE  :  List[Any]								=		'decrypt'
  SCREAMING_SNAKE_CASE  :  List[str]								=		decrypt_message(snake_case_   ,     snake_case_							)

 print(f"""\n{mode.title()}ion: \n{translated}"""							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str							)       ->	None:
 SCREAMING_SNAKE_CASE  :  int								=		list(snake_case_							)
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		list(snake_case_							)
 key_list.sort()
 letters_list.sort()

 if key_list != letters_list:
  sys.exit('Error in the key or symbol set.'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 return translate_message(snake_case_   ,     snake_case_   ,     'encrypt'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 return translate_message(snake_case_   ,     snake_case_   ,     'decrypt'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 SCREAMING_SNAKE_CASE  :  Any								=		''
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		LETTERS
 SCREAMING_SNAKE_CASE  :  List[Any]								=		key

 if mode == "decrypt":
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[Any]								=		chars_b, chars_a

 for symbol in message:
  if symbol.upper() in chars_a:
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		chars_a.find(symbol.upper()							)
   if symbol.isupper():
    translated += chars_b[sym_index].upper()
   else:
    translated += chars_b[sym_index].lower()
  else:
   translated += symbol

 return translated



def        SCREAMING_SNAKE_CASE_  (	)       ->	str:
 SCREAMING_SNAKE_CASE  :  Dict								=		list(snake_case_							)
 random.shuffle(snake_case_							)
 return "".join(snake_case_							)


if __name__ == "__main__":
   main()


