

"""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 	__UpperCamelCase    (    UpperCAmelCase_						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A = None    ,):
      '''simple docstring'''

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

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

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if not isinstance(_snake_case    ,_snake_case      ):
          _lowerCAmelCase       :   List[Any]  =    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    ,_A    ,_A = 4.0    ,_A = None    ,_A = 50    ,_A = "pil"    ,_A = True    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    len(_snake_case      )
      _lowerCAmelCase       :   Tuple  =    self.transformer.config.sample_size
      _lowerCAmelCase       :   Optional[int]  =    self.transformer.config.in_channels

      _lowerCAmelCase       :   Any  =    randn_tensor(
          shape=(batch_size, latent_channels, latent_size, latent_size)    ,generator=_snake_case    ,device=self.device    ,dtype=self.transformer.dtype    ,)
      _lowerCAmelCase       :   Union[str, Any]  =    torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

      _lowerCAmelCase       :   Union[str, Any]  =    torch.tensor(_snake_case    ,device=self.device      ).reshape(-1      )
      _lowerCAmelCase       :   List[Any]  =    torch.tensor([1000] * batch_size    ,device=self.device      )
      _lowerCAmelCase       :   List[str]  =    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:
              _lowerCAmelCase       :   Dict  =    latent_model_input[: len(_snake_case      ) // 2]
              _lowerCAmelCase       :   Dict  =    torch.cat([half, half]    ,dim=0      )
          _lowerCAmelCase       :   Union[str, Any]  =    self.scheduler.scale_model_input(_snake_case    ,_snake_case      )

          _lowerCAmelCase       :   int  =    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+)
              _lowerCAmelCase       :   int  =    latent_model_input.device.type == 'mps'
              if isinstance(_snake_case    ,_snake_case      ):
                  _lowerCAmelCase       :   Dict  =    torch.floataa if is_mps else torch.floataa
              else:
                  _lowerCAmelCase       :   int  =    torch.intaa if is_mps else torch.intaa
              _lowerCAmelCase       :   Dict  =    torch.tensor([timesteps]    ,dtype=_snake_case    ,device=latent_model_input.device      )
          elif len(timesteps.shape      ) == 0:
              _lowerCAmelCase       :   Optional[int]  =    timesteps[None].to(latent_model_input.device      )
          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          _lowerCAmelCase       :   Optional[int]  =    timesteps.expand(latent_model_input.shape[0]      )
          # predict noise model_output
          _lowerCAmelCase       :   Optional[int]  =    self.transformer(
              _snake_case    ,timestep=_snake_case    ,class_labels=_snake_case      ).sample

          # perform guidance
          if guidance_scale > 1:
              _lowerCAmelCase,					_lowerCAmelCase       :   List[Any]  =    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
              _lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    torch.split(_snake_case    ,len(_snake_case      ) // 2    ,dim=0      )

              _lowerCAmelCase       :   Dict  =    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
              _lowerCAmelCase       :   List[str]  =    torch.cat([half_eps, half_eps]    ,dim=0      )

              _lowerCAmelCase       :   Tuple  =    torch.cat([eps, rest]    ,dim=1      )

          # learned sigma
          if self.transformer.config.out_channels // 2 == latent_channels:
              _lowerCAmelCase,					_lowerCAmelCase       :   Union[str, Any]  =    torch.split(_snake_case    ,_snake_case    ,dim=1      )
          else:
              _lowerCAmelCase       :   int  =    noise_pred

          # compute previous image: x_t -> x_t-1
          _lowerCAmelCase       :   Optional[int]  =    self.scheduler.step(_snake_case    ,_snake_case    ,_snake_case      ).prev_sample

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

      _lowerCAmelCase       :   Any  =    1 / self.vae.config.scaling_factor * latents
      _lowerCAmelCase       :   List[str]  =    self.vae.decode(_snake_case      ).sample

      _lowerCAmelCase       :   Any  =    (samples / 2 + 0.5).clamp(0    ,1      )

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

      if output_type == "pil":
          _lowerCAmelCase       :   Tuple  =    self.numpy_to_pil(_snake_case      )

      if not return_dict:
          return (samples,)

      return ImagePipelineOutput(images=_snake_case      )








"""simple docstring"""






import baseaa

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    return baseaa.aaaencode(string.encode('utf-8'   )   )

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    return baseaa.aaadecode(_lowerCamelCase   ).decode('utf-8'   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()





