



"""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        A__(  __UpperCamelCase			):







      def __init__(					self		:     List[str]				,				__SCREAMING_SNAKE_CASE		:     TransformeraDModel				,				__SCREAMING_SNAKE_CASE		:     AutoencoderKL				,				__SCREAMING_SNAKE_CASE		:     KarrasDiffusionSchedulers				,				__SCREAMING_SNAKE_CASE		:     Optional[Dict[int, str]] = None				,				)							->		List[str]:






            """simple docstring"""

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

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







      def  _a   (					self		:     int				,				__SCREAMING_SNAKE_CASE		:     Union[str, List[str]]			)							->		Dict:






            """simple docstring"""

            if not isinstance(snake_case__				,				snake_case__			):
                  __SCREAMING_SNAKE_CASE       		=   list(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		:     List[str]				,				__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 = 50				,				__SCREAMING_SNAKE_CASE		:     Optional[str] = "pil"				,				__SCREAMING_SNAKE_CASE		:     bool = True				,				)							->		List[Any]:






            """simple docstring"""

            __SCREAMING_SNAKE_CASE       		=   len(snake_case__			)
            __SCREAMING_SNAKE_CASE       		=   self.transformer.config.sample_size
            __SCREAMING_SNAKE_CASE       		=   self.transformer.config.in_channels

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

            __SCREAMING_SNAKE_CASE       		=   torch.tensor(snake_case__				,				device=self.device			).reshape(-1			)
            __SCREAMING_SNAKE_CASE       		=   torch.tensor([10_00] * batch_size				,				device=self.device			)
            __SCREAMING_SNAKE_CASE       		=   torch.cat([class_labels, class_null]				,				0			) if guidance_scale > 1 else class_labels

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

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

                  __SCREAMING_SNAKE_CASE       		=   t
                  if not torch.is_tensor(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+)
                        __SCREAMING_SNAKE_CASE       		=   latent_model_input.device.type == '''mps'''
                        if isinstance(snake_case__				,				snake_case__			):
                              __SCREAMING_SNAKE_CASE       		=   torch.floataa if is_mps else torch.floataa
                        else:
                              __SCREAMING_SNAKE_CASE       		=   torch.intaa if is_mps else torch.intaa
                        __SCREAMING_SNAKE_CASE       		=   torch.tensor([timesteps]				,				dtype=snake_case__				,				device=latent_model_input.device			)
                  elif len(timesteps.shape			) == 0:
                        __SCREAMING_SNAKE_CASE       		=   timesteps[None].to(latent_model_input.device			)
                  # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                  __SCREAMING_SNAKE_CASE       		=   timesteps.expand(latent_model_input.shape[0]			)
                  # predict noise model_output
                  __SCREAMING_SNAKE_CASE       		=   self.transformer(
                      snake_case__				,				timestep=snake_case__				,				class_labels=snake_case__			).sample

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

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

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

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

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

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

            __SCREAMING_SNAKE_CASE       		=   1 / self.vae.config.scaling_factor * latents
            __SCREAMING_SNAKE_CASE       		=   self.vae.decode(snake_case__			).sample

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

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

            if output_type == "pil":
                  __SCREAMING_SNAKE_CASE       		=   self.numpy_to_pil(snake_case__			)

            if not return_dict:
                  return (samples,)

            return ImagePipelineOutput(images=snake_case__			)

"""simple docstring"""




def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		return " ".join(
		    """""".join(word[::-1]  ) if len(lowerCamelCase__  ) > 4 else word for word in sentence.split()  )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(reverse_long_words("Hey wollef sroirraw"))




