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     _SCREAMING_SNAKE_CASE       ( SCREAMING_SNAKE_CASE__							):



   '''simple docstring'''



   def __init__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = 0.93   ,							_SCREAMING_SNAKE_CASE = True   ,							):


      '''simple docstring'''

      __lowerCamelCase								=				mixing_coeff
      __lowerCamelCase								=				leapfrog_steps

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

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







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = None   ,							_SCREAMING_SNAKE_CASE = False  ):


      '''simple docstring'''

      __lowerCamelCase								=				self.tokenizer(
          _SCREAMING_SNAKE_CASE   ,							padding='max_length'   ,							max_length=self.tokenizer.model_max_length   ,							truncation=_SCREAMING_SNAKE_CASE   ,							return_tensors='pt'   ,							)

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

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

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

         __lowerCamelCase								=				self.tokenizer(
             _SCREAMING_SNAKE_CASE   ,							padding='max_length'   ,							max_length=self.tokenizer.model_max_length   ,							truncation=_SCREAMING_SNAKE_CASE   ,							return_tensors='pt'   ,							)

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

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

      return prompt_embeds







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				self.mixing_coeff * x + (1 - self.mixing_coeff) * y
      __lowerCamelCase								=				self.mixing_coeff * y + (1 - self.mixing_coeff) * x

      return [x, y]







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
      __lowerCamelCase								=				(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

      return [x, y]







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				int(_SCREAMING_SNAKE_CASE  )

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

      return alpha_prod, 1 - alpha_prod







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							):


      '''simple docstring'''

      __lowerCamelCase								=				timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

      __lowerCamelCase		,__lowerCamelCase								=				self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase		,__lowerCamelCase								=				self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE  )

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

      __lowerCamelCase								=				(base - b_t * model_output) / a_t

      return model_input, next_model_input.to(base.dtype  )







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							):


      '''simple docstring'''

      __lowerCamelCase								=				timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

      __lowerCamelCase		,__lowerCamelCase								=				self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase		,__lowerCamelCase								=				self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE  )

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

      return model_input, next_model_input.to(base.dtype  )







   @torch.no_grad()
   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				1 / self.vae.config.scaling_factor * latents
      __lowerCamelCase								=				self.vae.decode(_SCREAMING_SNAKE_CASE  ).sample
      __lowerCamelCase								=				(image / 2 + 0.5).clamp(0   ,							1  )
      return image







   @torch.no_grad()
   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = None   ,							):


      '''simple docstring'''

      __lowerCamelCase								=				guidance_scale > 1.0

      __lowerCamelCase								=				image.to(device=self.device   ,							dtype=text_embeds.dtype  )
      __lowerCamelCase								=				self.vae.encode(_SCREAMING_SNAKE_CASE  ).latent_dist.sample(_SCREAMING_SNAKE_CASE  )

      __lowerCamelCase								=				self.vae.config.scaling_factor * latent

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

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

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

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

            __lowerCamelCase								=				coupled_latents[j]
            __lowerCamelCase								=				coupled_latents[k]

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

            __lowerCamelCase								=				self.unet(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							encoder_hidden_states=_SCREAMING_SNAKE_CASE  ).sample

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

            __lowerCamelCase		,__lowerCamelCase								=				self.noise_step(
                base=_SCREAMING_SNAKE_CASE   ,							model_input=_SCREAMING_SNAKE_CASE   ,							model_output=_SCREAMING_SNAKE_CASE   ,							timestep=_SCREAMING_SNAKE_CASE   ,							)

            __lowerCamelCase								=				model_input

      return coupled_latents







   @torch.no_grad()
   def __call__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = 3.0   ,							_SCREAMING_SNAKE_CASE = 50   ,							_SCREAMING_SNAKE_CASE = 0.8   ,							_SCREAMING_SNAKE_CASE = None   ,							_SCREAMING_SNAKE_CASE = None   ,							_SCREAMING_SNAKE_CASE = "pil"   ,							):


      '''simple docstring'''

      __lowerCamelCase								=				guidance_scale > 1.0

      __lowerCamelCase								=				self.image_processor.preprocess(_SCREAMING_SNAKE_CASE  )

      __lowerCamelCase								=				self._encode_prompt(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				self._encode_prompt(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )

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

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

      __lowerCamelCase								=				self.prepare_latents(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )

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

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

            __lowerCamelCase								=				coupled_latents[j]
            __lowerCamelCase								=				coupled_latents[k]

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

            __lowerCamelCase								=				self.unet(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							encoder_hidden_states=_SCREAMING_SNAKE_CASE  ).sample

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

            __lowerCamelCase		,__lowerCamelCase								=				self.denoise_step(
                base=_SCREAMING_SNAKE_CASE   ,							model_input=_SCREAMING_SNAKE_CASE   ,							model_output=_SCREAMING_SNAKE_CASE   ,							timestep=_SCREAMING_SNAKE_CASE   ,							)

            __lowerCamelCase								=				model_input

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

      # either one is fine
      __lowerCamelCase								=				coupled_latents[0]

      if output_type not in ["latent", "pt", "np", "pil"]:
         __lowerCamelCase								=				(
             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'   ,							_SCREAMING_SNAKE_CASE   ,							standard_warn=_SCREAMING_SNAKE_CASE  )
         __lowerCamelCase								=				'np'

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

      return image







"""simple docstring"""







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

_SCREAMING_SNAKE_CASE  :		List[str]        =      [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      [0 for x in range(len(graph))]
_SCREAMING_SNAKE_CASE  :		List[Any]        =      []



def 			A__						(  lowercase_    :  Optional[int]			,   lowercase_    :  List[str]	)     ->       List[str]:
   __lowerCamelCase								=				1
   while stack:
      __lowerCamelCase								=				stack.pop()
      print(lowercase_			,   clothes[current_clothing]	)
      order += 1



def 			A__						(  lowercase_    :  List[Any]			,   lowercase_    :  List[str]			,   lowercase_    :  Union[str, Any]	)     ->       List[Any]:
   __lowerCamelCase								=				1
   for v in graph[u]:
      if not visited[v]:
         depth_first_search(lowercase_			,   lowercase_			,   lowercase_	)

   stack.append(lowercase_	)



def 			A__						(  lowercase_    :  Dict			,   lowercase_    :  Optional[Any]	)     ->       Union[str, Any]:
   for v in range(len(lowercase_	)	):
      if not visited[v]:
         depth_first_search(lowercase_			,   lowercase_			,   lowercase_	)


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






