


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_					(       __UpperCamelCase    ):




      '''simple docstring'''


      def __init__(				self:       Optional[Any]						,      a:       AutoencoderKL						,      a:       CLIPTextModel						,      a:       CLIPTokenizer						,      a:       UNetaDConditionModel						,      a:       Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]						,      ):
       super().__init__()

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




      def      _snake_case	(				self:       Optional[int]						,      a:       int				):
       with torch.no_grad():
        __lowerCamelCase		:							Union[str, Any]							=						self.vae.encode(tfms.ToTensor()(a				).unsqueeze(0				).to(self.device				) * 2 - 1				)
        __lowerCamelCase		:							List[Any]							=						0.1_8_2_1_5 * latent.latent_dist.sample()
       return latent




      def      _snake_case	(				self:       Optional[int]						,      a:       List[str]				):
       __lowerCamelCase		:							List[Any]							=						(1 / 0.1_8_2_1_5) * latent
       with torch.no_grad():
        __lowerCamelCase		:							Tuple							=						self.vae.decode(a				).sample
       __lowerCamelCase		:							Tuple							=						(img / 2 + 0.5).clamp(0						,      1				)
       __lowerCamelCase		:							Dict							=						img.detach().cpu().permute(0						,      2						,      3						,      1				).numpy()
       __lowerCamelCase		:							int							=						(img * 255).round().astype('uint8'				)
       return Image.fromarray(img[0]				)




      def      _snake_case	(				self:       List[str]						,      a:       int				):
       __lowerCamelCase		:							Optional[Any]							=						self.tokenizer(
           a						,      padding='max_length'						,      max_length=self.tokenizer.model_max_length						,      truncation=a						,      return_tensors='pt'						,      )

       __lowerCamelCase		:							int							=						self.text_encoder(text_input.input_ids.to(self.device				)				)[0]

       __lowerCamelCase		:							Dict							=						self.tokenizer(
           ''						,      padding='max_length'						,      max_length=self.tokenizer.model_max_length						,      truncation=a						,      return_tensors='pt'						,      )

       __lowerCamelCase		:							Any							=						self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

       return torch.cat([uncond_embedding, text_embedding]				)




      def __call__(				self:       List[Any]						,      a:       Image.Image						,      a:       str						,      a:       float = 0.3						,      a:       float = 0.6						,      a:       float = 0.5						,      a:       int = 42						,      a:       int = 50						,      a:       float = 7.5						,      ):
       __lowerCamelCase		:							Optional[Any]							=						steps - int(kmin * steps				)
       __lowerCamelCase		:							Tuple							=						steps - int(kmax * steps				)

       __lowerCamelCase		:							List[Any]							=						self.prep_text(a				)

       self.scheduler.set_timesteps(a				)

       __lowerCamelCase					,     __lowerCamelCase		:							int							=						img.size
       __lowerCamelCase		:							List[Any]							=						self.encode(a				)

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

       __lowerCamelCase		:							List[Any]							=						self.scheduler.add_noise(
           a						,      a						,      timesteps=self.scheduler.timesteps[tmax]						,      )

       __lowerCamelCase		:							Any							=						torch.cat([latents] * 2				)

       __lowerCamelCase		:							List[str]							=						self.scheduler.scale_model_input(a						,      self.scheduler.timesteps[tmax]				)

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

       __lowerCamelCase					,     __lowerCamelCase		:							Any							=						pred.chunk(2				)
       __lowerCamelCase		:							List[str]							=						pred_uncond + guidance_scale * (pred_text - pred_uncond)

       __lowerCamelCase		:							str							=						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
          __lowerCamelCase		:							List[Any]							=						self.scheduler.add_noise(
              a						,      a						,      timesteps=a						,      )

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

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

         __lowerCamelCase		:							Any							=						self.scheduler.scale_model_input(a						,      a				)

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

         __lowerCamelCase					,     __lowerCamelCase		:							List[str]							=						pred.chunk(2				)
         __lowerCamelCase		:							List[str]							=						pred_uncond + guidance_scale * (pred_text - pred_uncond)

         __lowerCamelCase		:							Optional[int]							=						self.scheduler.step(a						,      a						,      a				).prev_sample

       return self.decode(a				)





class        A_					:




      '''simple docstring'''


      def __init__(				self:       Tuple						,      a:       Dict=None				):
       __lowerCamelCase		:							List[Any]							=						data
       __lowerCamelCase		:							Optional[Any]							=						None




      def __repr__(				self:       Optional[int]				):
       __lowerCamelCase		:							Any							=						''
       __lowerCamelCase		:							List[Any]							=						self
       while temp:
        string_rep += F'<{temp.data}> ---> '
        __lowerCamelCase		:							Any							=						temp.next
       string_rep += "<END>"
       return string_rep



def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):

 # if elements_list is empty
 if not elements_list:
  raise Exception('The Elements List is empty'						)

 # Set first element as Head
 __lowerCamelCase		:							Optional[int]							=						Node(elements_list[0]						)
 __lowerCamelCase		:							List[str]							=						head
 # Loop through elements from position 1
 for data in elements_list[1:]:
  __lowerCamelCase		:							str							=						Node(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Tuple							=						current.next
 return head


lowercase_           =	[1, 3, 5, 3_2, 4_4, 1_2, 4_3]
print(F"""List: {list_data}""")
print('Creating Linked List from List.')
lowercase_           =	make_linked_list(list_data)
print('Linked List:')



print(linked_list)

