




'''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 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Union[str, Any] = None  ,				):

      """simple docstring"""
      super().__init__()
      self.register_modules(transformer=__a  ,				vae=__a  ,				scheduler=__a   )

      # 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(__a   )
         _SCREAMING_SNAKE_CASE	=				dict(sorted(self.labels.items()   )   )



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     List[str]   ):

      """simple docstring"""

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

      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 :     Optional[Any]  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     List[str] = 4.0  ,				__lowerCamelCase :     Dict = None  ,				__lowerCamelCase :     Optional[int] = 5_0  ,				__lowerCamelCase :     Tuple = "pil"  ,				__lowerCamelCase :     List[str] = True  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				len(__a   )
      _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=__a  ,				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(__a  ,				device=self.device   ).reshape(-1   )
      _SCREAMING_SNAKE_CASE	=				torch.tensor([1_0_0_0] * 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(__a   )

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

         _SCREAMING_SNAKE_CASE	=				t
         if not torch.is_tensor(__a   ):
            # 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(__a  ,				__a   ):
               _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=__a  ,				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(
             __a  ,				timestep=__a  ,				class_labels=__a   ).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(__a  ,				len(__a   ) // 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(__a  ,				__a  ,				dim=1   )
         else:
            _SCREAMING_SNAKE_CASE	=				noise_pred

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

      if not return_dict:
         return (samples,)

      return ImagePipelineOutput(images=__a   )







'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			str  )			->			str:
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				(
       first_str_length if first_str_length > second_str_length else second_str_length
   )
   _SCREAMING_SNAKE_CASE	=				[]
   for char_count in range(__A  ):
      if char_count < first_str_length:
         output_list.append(first_str[char_count]  )
      if char_count < second_str_length:
         output_list.append(second_str[char_count]  )
   return "".join(__A  )


if __name__ == "__main__":
 print(alternative_string_arrange('AB', 'XYZ'), end=' ')

