


from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)






class        UpperCAmelCase__			(   A_						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_ = 0.93   ,  A_ = True   ,  )    ->			List[str]:
         __UpperCamelCase       				=mixing_coeff
         __UpperCamelCase       				=leapfrog_steps

         super().__init__()
         self.register_modules(
             vae=A_   ,  text_encoder=A_   ,  tokenizer=A_   ,  unet=A_   ,  scheduler=A_   ,  )

         __UpperCamelCase       				=2 ** (len(self.vae.config.block_out_channels       ) - 1)
         __UpperCamelCase       				=VaeImageProcessor(vae_scale_factor=self.vae_scale_factor       )






    def _a   (     self   ,  A_   ,  A_ = None   ,  A_ = False       )    ->			Tuple:
         __UpperCamelCase       				=self.tokenizer(
             A_   ,  padding='max_length'   ,  max_length=self.tokenizer.model_max_length   ,  truncation=A_   ,  return_tensors='pt'   ,  )

         __UpperCamelCase       				=self.text_encoder(text_inputs.input_ids.to(self.device       )       ).last_hidden_state

         __UpperCamelCase       				=prompt_embeds.to(dtype=self.text_encoder.dtype   ,  device=self.device       )

         if do_classifier_free_guidance:
              __UpperCamelCase       				='' if negative_prompt is None else negative_prompt

              __UpperCamelCase       				=self.tokenizer(
                  A_   ,  padding='max_length'   ,  max_length=self.tokenizer.model_max_length   ,  truncation=A_   ,  return_tensors='pt'   ,  )

              __UpperCamelCase       				=self.text_encoder(uncond_input.input_ids.to(self.device       )       ).last_hidden_state

              __UpperCamelCase       				=torch.cat([negative_prompt_embeds, prompt_embeds]       )

         return prompt_embeds






    def _a   (     self   ,  A_   ,  A_       )    ->			List[Any]:
         __UpperCamelCase       				=self.mixing_coeff * x + (1 - self.mixing_coeff) * y
         __UpperCamelCase       				=self.mixing_coeff * y + (1 - self.mixing_coeff) * x

         return [x, y]






    def _a   (     self   ,  A_   ,  A_       )    ->			List[str]:
         __UpperCamelCase       				=(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
         __UpperCamelCase       				=(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

         return [x, y]






    def _a   (     self   ,  A_       )    ->			int:
         # as self.alphas_cumprod is always in cpu
         __UpperCamelCase       				=int(A_       )

         __UpperCamelCase       				=self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

         return alpha_prod, 1 - alpha_prod






    def _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			Union[str, Any]:
         __UpperCamelCase       				=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         __UpperCamelCase				, __UpperCamelCase       				=self._get_alpha_and_beta(A_       )
         __UpperCamelCase				, __UpperCamelCase       				=self._get_alpha_and_beta(A_       )

         __UpperCamelCase       				=(alpha_prod_t_prev / alpha_prod_t) ** 0.5
         __UpperCamelCase       				=-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

         __UpperCamelCase       				=(base - b_t * model_output) / a_t

         return model_input, next_model_input.to(base.dtype       )






    def _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			Dict:
         __UpperCamelCase       				=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         __UpperCamelCase				, __UpperCamelCase       				=self._get_alpha_and_beta(A_       )
         __UpperCamelCase				, __UpperCamelCase       				=self._get_alpha_and_beta(A_       )

         __UpperCamelCase       				=(alpha_prod_t_prev / alpha_prod_t) ** 0.5
         __UpperCamelCase       				=-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
         __UpperCamelCase       				=a_t * base + b_t * model_output

         return model_input, next_model_input.to(base.dtype       )






    @torch.no_grad()
    def _a   (     self   ,  A_       )    ->			Optional[int]:
         __UpperCamelCase       				=1 / self.vae.config.scaling_factor * latents
         __UpperCamelCase       				=self.vae.decode(A_       ).sample
         __UpperCamelCase       				=(image / 2 + 0.5).clamp(0   ,  1       )
         return image






    @torch.no_grad()
    def _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_ = None   ,  )    ->			Optional[int]:
         __UpperCamelCase       				=guidance_scale > 1.0

         __UpperCamelCase       				=image.to(device=self.device   ,  dtype=text_embeds.dtype       )
         __UpperCamelCase       				=self.vae.encode(A_       ).latent_dist.sample(A_       )

         __UpperCamelCase       				=self.vae.config.scaling_factor * latent

         __UpperCamelCase       				=[latent.clone(), latent.clone()]

         for i, t in tqdm(enumerate(A_       )   ,  total=len(A_       )       ):
              __UpperCamelCase       				=self.noise_mixing_layer(x=coupled_latents[0]   ,  y=coupled_latents[1]       )

              # j - model_input index, k - base index
              for j in range(2       ):
                   __UpperCamelCase       				=j ^ 1

                   if self.leapfrog_steps:
                        if i % 2 == 0:
                             __UpperCamelCase				, __UpperCamelCase       				=j, k

                   __UpperCamelCase       				=coupled_latents[j]
                   __UpperCamelCase       				=coupled_latents[k]

                   __UpperCamelCase       				=torch.cat([model_input] * 2       ) if do_classifier_free_guidance else model_input

                   __UpperCamelCase       				=self.unet(A_   ,  A_   ,  encoder_hidden_states=A_       ).sample

                   if do_classifier_free_guidance:
                        __UpperCamelCase				, __UpperCamelCase       				=noise_pred.chunk(2       )
                        __UpperCamelCase       				=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   __UpperCamelCase				, __UpperCamelCase       				=self.noise_step(
                       base=A_   ,  model_input=A_   ,  model_output=A_   ,  timestep=A_   ,  )

                   __UpperCamelCase       				=model_input

         return coupled_latents






    @torch.no_grad()
    def __call__(     self   ,  A_   ,  A_   ,  A_   ,  A_ = 3.0   ,  A_ = 50   ,  A_ = 0.8   ,  A_ = None   ,  A_ = None   ,  A_ = "pil"   ,  )    ->			Union[str, Any]:
         __UpperCamelCase       				=guidance_scale > 1.0

         __UpperCamelCase       				=self.image_processor.preprocess(A_       )

         __UpperCamelCase       				=self._encode_prompt(A_   ,  A_   ,  A_       )
         __UpperCamelCase       				=self._encode_prompt(A_   ,  A_   ,  A_       )

         self.scheduler.set_timesteps(A_   ,  self.device       )

         __UpperCamelCase       				=num_inference_steps - int(num_inference_steps * strength       )
         __UpperCamelCase       				=self.scheduler.timesteps[t_limit:]
         __UpperCamelCase       				=fwd_timesteps.flip(0       )

         __UpperCamelCase       				=self.prepare_latents(A_   ,  A_   ,  A_   ,  A_   ,  A_       )

         for i, t in tqdm(enumerate(A_       )   ,  total=len(A_       )       ):
              # j - model_input index, k - base index
              for k in range(2       ):
                   __UpperCamelCase       				=k ^ 1

                   if self.leapfrog_steps:
                        if i % 2 == 1:
                             __UpperCamelCase				, __UpperCamelCase       				=j, k

                   __UpperCamelCase       				=coupled_latents[j]
                   __UpperCamelCase       				=coupled_latents[k]

                   __UpperCamelCase       				=torch.cat([model_input] * 2       ) if do_classifier_free_guidance else model_input

                   __UpperCamelCase       				=self.unet(A_   ,  A_   ,  encoder_hidden_states=A_       ).sample

                   if do_classifier_free_guidance:
                        __UpperCamelCase				, __UpperCamelCase       				=noise_pred.chunk(2       )
                        __UpperCamelCase       				=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   __UpperCamelCase				, __UpperCamelCase       				=self.denoise_step(
                       base=A_   ,  model_input=A_   ,  model_output=A_   ,  timestep=A_   ,  )

                   __UpperCamelCase       				=model_input

              __UpperCamelCase       				=self.denoise_mixing_layer(x=coupled_latents[0]   ,  y=coupled_latents[1]       )

         # either one is fine
         __UpperCamelCase       				=coupled_latents[0]

         if output_type not in ["latent", "pt", "np", "pil"]:
              __UpperCamelCase       				=(
                  f'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
                  '`pil`, `np`, `pt`, `latent`'
              )
              deprecate('Unsupported output_type'   ,  '1.0.0'   ,  A_   ,  standard_warn=A_       )
              __UpperCamelCase       				='np'

         if output_type == "latent":
              __UpperCamelCase       				=final_latent
         else:
              __UpperCamelCase       				=self.decode_latents(A_       )
              __UpperCamelCase       				=self.image_processor.postprocess(A_   ,  output_type=A_       )

         return image









_A									=		{
    0: 'underwear',
    1: 'pants',
    2: 'belt',
    3: 'suit',
    4: 'shoe',
    5: 'socks',
    6: 'shirt',
    7: 'tie',
    8: 'watch',
}

_A									=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_A									=		[0 for x in range(len(graph))]
_A									=		[]

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     while stack:
          __UpperCamelCase       				=stack.pop()
          print(SCREAMING_SNAKE_CASE__     ,	clothes[current_clothing]							)
          order += 1

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     stack.append(SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   List[str]							):
     for v in range(len(SCREAMING_SNAKE_CASE__							)							):
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)





