






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_    (						__lowercase			):






 '''simple docstring'''




 def __init__(	self							,  _A							,  _A							,  _A							,  _A = None							,  ) ->		List[Any]:

     """simple docstring"""



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

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


 def  snake_case__				(	self							,  _A) ->		List[int]:

     """simple docstring"""



     if not isinstance(_A							,  _A):
         _UpperCAmelCase				:      Tuple			  =				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							,  _A							,  _A = 4.0							,  _A = None							,  _A = 50							,  _A = "pil"							,  _A = True							,  ) ->		Union[ImagePipelineOutput, Tuple]:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				len(_A)
     _UpperCAmelCase				:      Optional[Any]			  =				self.transformer.config.sample_size
     _UpperCAmelCase				:      Any			  =				self.transformer.config.in_channels

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

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

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

         # perform guidance
         if guidance_scale > 1:
             _UpperCAmelCase  , _UpperCAmelCase				:      int			  =				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
             _UpperCAmelCase  , _UpperCAmelCase				:      List[str]			  =				torch.split(_A							,  len(_A) // 2							,  dim=0)

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

             _UpperCAmelCase				:      Optional[Any]			  =				torch.cat([eps, rest]							,  dim=1)

         # learned sigma
         if self.transformer.config.out_channels // 2 == latent_channels:
             _UpperCAmelCase  , _UpperCAmelCase				:      List[Any]			  =				torch.split(_A							,  _A							,  dim=1)
         else:
             _UpperCAmelCase				:      str			  =				noise_pred

         # compute previous image: x_t -> x_t-1
         _UpperCAmelCase				:      Dict			  =				self.scheduler.step(_A							,  _A							,  _A).prev_sample

     if guidance_scale > 1:
         _UpperCAmelCase  , _UpperCAmelCase				:      Optional[Any]			  =				latent_model_input.chunk(2							,  dim=0)
     else:
         _UpperCAmelCase				:      str			  =				latent_model_input

     _UpperCAmelCase				:      Any			  =				1 / self.vae.config.scaling_factor * latents
     _UpperCAmelCase				:      Any			  =				self.vae.decode(_A).sample

     _UpperCAmelCase				:      Tuple			  =				(samples / 2 + 0.5).clamp(0							,  1)

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

     if output_type == "pil":
         _UpperCAmelCase				:      Union[str, Any]			  =				self.numpy_to_pil(_A)

     if not return_dict:
         return (samples,)

     return ImagePipelineOutput(images=_A)












def      _lowerCamelCase	(						__A    :							int )      ->					bool:
    if not isinstance(__A		,		__A ):
        _UpperCAmelCase				:      Tuple			  =				f'''Input value of [number={number}] must be an integer'''
        raise TypeError(__A )
    if number < 0:
        return False
    _UpperCAmelCase				:      List[str]			  =				number * number
    while number > 0:
        if number % 10 != number_square % 10:
            return False
        number //= 10
        number_square //= 10
    return True


if __name__ == "__main__":
  import doctest

  doctest.testmod()




