


'''simple docstring'''







from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  def __init__(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							TransformeraDModel				,							SCREAMING_SNAKE_CASE__     :							AutoencoderKL				,							SCREAMING_SNAKE_CASE__     :							KarrasDiffusionSchedulers				,							SCREAMING_SNAKE_CASE__     :							Optional[Dict[int, str]] = None				,							):





       '''simple docstring'''

       super().__init__()
       self.register_modules(transformer=SCREAMING_SNAKE_CASE__				,							vae=SCREAMING_SNAKE_CASE__				,							scheduler=SCREAMING_SNAKE_CASE__						)

       # create a imagenet -> id dictionary for easier use
       __a       =   {}
       if idalabel is not None:
            for key, value in idalabel.items():
                 for label in value.split(""","""						):
                      __a       =   int(SCREAMING_SNAKE_CASE__						)
            __a       =   dict(sorted(self.labels.items()						)						)

  def __a						(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, List[str]]						):





       '''simple docstring'''

       if not isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						):
            __a       =   list(SCREAMING_SNAKE_CASE__						)

       for l in label:
            if l not in self.labels:
                 raise ValueError(
                     f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''						)

       return [self.labels[l] for l in label]


  @torch.no_grad()
  def __call__(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							List[int]				,							SCREAMING_SNAKE_CASE__     :							float = 4.0				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							SCREAMING_SNAKE_CASE__     :							int = 5_0				,							SCREAMING_SNAKE_CASE__     :							Optional[str] = "pil"				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							):





       '''simple docstring'''

       __a       =   len(SCREAMING_SNAKE_CASE__						)
       __a       =   self.transformer.config.sample_size
       __a       =   self.transformer.config.in_channels

       __a       =   randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)				,							generator=SCREAMING_SNAKE_CASE__				,							device=self.device				,							dtype=self.transformer.dtype				,							)
       __a       =   torch.cat([latents] * 2						) if guidance_scale > 1 else latents

       __a       =   torch.tensor(SCREAMING_SNAKE_CASE__				,							device=self.device						).reshape(-1						)
       __a       =   torch.tensor([1_0_0_0] * batch_size				,							device=self.device						)
       __a       =   torch.cat([class_labels, class_null]				,							0						) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__						)

       for t in self.progress_bar(self.scheduler.timesteps						):
            if guidance_scale > 1:
                 __a       =   latent_model_input[: len(SCREAMING_SNAKE_CASE__						) // 2]
                 __a       =   torch.cat([half, half]				,							dim=0						)
            __a       =   self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

            __a       =   t
            if not torch.is_tensor(SCREAMING_SNAKE_CASE__						):
                 # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                 # This would be a good case for the `match` statement (Python 3.10+)
                 __a       =   latent_model_input.device.type == """mps"""
                 if isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						):
                      __a       =   torch.floataa if is_mps else torch.floataa
                 else:
                      __a       =   torch.intaa if is_mps else torch.intaa
                 __a       =   torch.tensor([timesteps]				,							dtype=SCREAMING_SNAKE_CASE__				,							device=latent_model_input.device						)
            elif len(timesteps.shape						) == 0:
                 __a       =   timesteps[None].to(latent_model_input.device						)
            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
            __a       =   timesteps.expand(latent_model_input.shape[0]						)
            # predict noise model_output
            __a       =   self.transformer(
                SCREAMING_SNAKE_CASE__				,							timestep=SCREAMING_SNAKE_CASE__				,							class_labels=SCREAMING_SNAKE_CASE__						).sample

            # perform guidance
            if guidance_scale > 1:
                 __a ,						__a       =   noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                 __a ,						__a       =   torch.split(SCREAMING_SNAKE_CASE__				,							len(SCREAMING_SNAKE_CASE__						) // 2				,							dim=0						)

                 __a       =   uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                 __a       =   torch.cat([half_eps, half_eps]				,							dim=0						)

                 __a       =   torch.cat([eps, rest]				,							dim=1						)

            # learned sigma
            if self.transformer.config.out_channels // 2 == latent_channels:
                 __a ,						__a       =   torch.split(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							dim=1						)
            else:
                 __a       =   noise_pred

            # compute previous image: x_t -> x_t-1
            __a       =   self.scheduler.step(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						).prev_sample

       if guidance_scale > 1:
            __a ,						__a       =   latent_model_input.chunk(2				,							dim=0						)
       else:
            __a       =   latent_model_input

       __a       =   1 / self.vae.config.scaling_factor * latents
       __a       =   self.vae.decode(SCREAMING_SNAKE_CASE__						).sample

       __a       =   (samples / 2 + 0.5).clamp(0				,							1						)

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       __a       =   samples.cpu().permute(0				,							2				,							3				,							1						).float().numpy()

       if output_type == "pil":
            __a       =   self.numpy_to_pil(SCREAMING_SNAKE_CASE__						)

       if not return_dict:
            return (samples,)

       return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__						)










'''simple docstring'''







SCREAMING_SNAKE_CASE_      =			0  # The first color of the flag.
SCREAMING_SNAKE_CASE_      =			1  # The second color of the flag.
SCREAMING_SNAKE_CASE_      =			2  # The third color of the flag.
SCREAMING_SNAKE_CASE_      =			(red, white, blue)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					list:
     """simple docstring"""




     if not sequence:
          return []
     if len(__SCREAMING_SNAKE_CASE		) == 1:
          return list(__SCREAMING_SNAKE_CASE		)
     __a       =   0
     __a       =   len(__SCREAMING_SNAKE_CASE		) - 1
     __a       =   0
     while mid <= high:
          if sequence[mid] == colors[0]:
               __a ,						__a       =   sequence[mid], sequence[low]
               low += 1
               mid += 1
          elif sequence[mid] == colors[1]:
               mid += 1
          elif sequence[mid] == colors[2]:
               __a ,						__a       =   sequence[high], sequence[mid]
               high -= 1
          else:
               __a       =   F'''The elements inside the sequence must contains only {colors} values'''
               raise ValueError(__SCREAMING_SNAKE_CASE		)
     return sequence


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 SCREAMING_SNAKE_CASE_      =			input('Enter numbers separated by commas:\n').strip()
 SCREAMING_SNAKE_CASE_      =			[int(item.strip()) for item in user_input.split(',')]
 print(f"""{dutch_national_flag_sort(unsorted)}""")






