


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 def __init__(	self				,					__A				,					__A				,					__A				,					__A				,					__A				,					)      ->  List[str]:
     super().__init__()

     self.register_modules(vae=__A				,					text_encoder=__A				,					tokenizer=__A				,					unet=__A				,					scheduler=__A		)


 def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  str:
     with torch.no_grad():
         a					=self.vae.encode(tfms.ToTensor()(__A		).unsqueeze(0		).to(self.device		) * 2 - 1		)
         a					=0.18_215 * latent.latent_dist.sample()
     return latent


 def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Union[str, Any]:
     a					=(1 / 0.18_215) * latent
     with torch.no_grad():
         a					=self.vae.decode(__A		).sample
     a					=(img / 2 + 0.5).clamp(0				,					1		)
     a					=img.detach().cpu().permute(0				,					2				,					3				,					1		).numpy()
     a					=(img * 255).round().astype('''uint8'''		)
     return Image.fromarray(img[0]		)


 def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Dict:
     a					=self.tokenizer(
         __A				,					padding='''max_length'''				,					max_length=self.tokenizer.model_max_length				,					truncation=__A				,					return_tensors='''pt'''				,					)

     a					=self.text_encoder(text_input.input_ids.to(self.device		)		)[0]

     a					=self.tokenizer(
         ''''''				,					padding='''max_length'''				,					max_length=self.tokenizer.model_max_length				,					truncation=__A				,					return_tensors='''pt'''				,					)

     a					=self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

     return torch.cat([uncond_embedding, text_embedding]		)





 def __call__(	self				,					__A				,					__A				,					__A = 0.3				,					__A = 0.6				,					__A = 0.5				,					__A = 42				,					__A = 50				,					__A = 7.5				,					)      ->  Image.Image:
     a					=steps - int(kmin * steps		)
     a					=steps - int(kmax * steps		)

     a					=self.prep_text(__A		)

     self.scheduler.set_timesteps(__A		)

     a	,   a					=img.size
     a					=self.encode(__A		)

     torch.manual_seed(__A		)
     a					=torch.randn(
         (1, self.unet.config.in_channels, height // 8, width // 8)				,					).to(self.device		)

     a					=self.scheduler.add_noise(
         __A				,					__A				,					timesteps=self.scheduler.timesteps[tmax]				,					)

     a					=torch.cat([latents] * 2		)

     a					=self.scheduler.scale_model_input(__A				,					self.scheduler.timesteps[tmax]		)

     with torch.no_grad():
         a					=self.unet(
             __A				,					self.scheduler.timesteps[tmax]				,					encoder_hidden_states=__A				,					).sample

     a	,   a					=pred.chunk(2		)
     a					=pred_uncond + guidance_scale * (pred_text - pred_uncond)

     a					=self.scheduler.step(__A				,					self.scheduler.timesteps[tmax]				,					__A		).prev_sample

     for i, t in enumerate(tqdm(self.scheduler.timesteps		)		):
         if i > tmax:
             if i < tmin:  # layout generation phase
                 a					=self.scheduler.add_noise(
                     __A				,					__A				,					timesteps=__A				,					)

                 a					=(mix_factor * latents) + (
                     1 - mix_factor
                 ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                 a					=torch.cat([input] * 2		)

             else:  # content generation phase
                 a					=torch.cat([latents] * 2		)

             a					=self.scheduler.scale_model_input(__A				,					__A		)

             with torch.no_grad():
                 a					=self.unet(
                     __A				,					__A				,					encoder_hidden_states=__A				,					).sample

             a	,   a					=pred.chunk(2		)
             a					=pred_uncond + guidance_scale * (pred_text - pred_uncond)

             a					=self.scheduler.step(__A				,					__A				,					__A		).prev_sample

     return self.decode(__A		)



"""simple docstring"""







from __future__ import annotations

from random import randrange
def _A		(		lowercase	):







    """simple docstring"""
    if len(lowercase	) < 2:
        return collection
    a					=randrange(len(lowercase	)	)  # Use random element as pivot
    a					=collection[pivot_index]
    a					=[]  # All elements greater than pivot
    a					=[]  # All elements less than or equal to pivot

    for element in collection[:pivot_index]:
        (greater if element > pivot else lesser).append(lowercase	)

    for element in collection[pivot_index + 1 :]:
        (greater if element > pivot else lesser).append(lowercase	)

    return [*quick_sort(lowercase	), pivot, *quick_sort(lowercase	)]


if __name__ == "__main__":
       lowerCamelCase_     :     Union[str, Any]    		=						input("""Enter numbers separated by a comma:\n""").strip()
       lowerCamelCase_     :     Union[str, Any]    		=						[int(item) for item in user_input.split(""",""")]
       print(quick_sort(unsorted))