

'''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__      (      _snake_case			):



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__ = None		,				)						->      Any:





           '''simple docstring'''
           super().__init__()
           self.register_modules(transformer=UpperCamelCase__		,				vae=UpperCamelCase__		,				scheduler=UpperCamelCase__       )

           # 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(UpperCamelCase__       )
                  A_						=      dict(sorted(self.labels.items()       )       )



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      List[int]:





           '''simple docstring'''

           if not isinstance(UpperCamelCase__		,				UpperCamelCase__       ):
                  A_						=      list(UpperCamelCase__       )

           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		,				UpperCamelCase__		,				UpperCamelCase__ = 4.0		,				UpperCamelCase__ = None		,				UpperCamelCase__ = 50		,				UpperCamelCase__ = "pil"		,				UpperCamelCase__ = True		,				)						->      Union[ImagePipelineOutput, Tuple]:





           '''simple docstring'''
           A_						=      len(UpperCamelCase__       )
           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=UpperCamelCase__		,				device=self.device		,				dtype=self.transformer.dtype		,				)
           A_						=      torch.cat([latents] * 2       ) if guidance_scale > 1 else latents

           A_						=      torch.tensor(UpperCamelCase__		,				device=self.device       ).reshape(-1       )
           A_						=      torch.tensor([1000] * 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(UpperCamelCase__       )

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

                  A_						=      t
                  if not torch.is_tensor(UpperCamelCase__       ):
                         # 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(UpperCamelCase__		,				UpperCamelCase__       ):
                                A_						=      torch.floataa if is_mps else torch.floataa
                         else:
                                A_						=      torch.intaa if is_mps else torch.intaa
                         A_						=      torch.tensor([timesteps]		,				dtype=UpperCamelCase__		,				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(
                      UpperCamelCase__		,				timestep=UpperCamelCase__		,				class_labels=UpperCamelCase__       ).sample

                  # perform guidance
                  if guidance_scale > 1:
                         A_					,			A_						=      noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                         A_					,			A_						=      torch.split(UpperCamelCase__		,				len(UpperCamelCase__       ) // 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(UpperCamelCase__		,				UpperCamelCase__		,				dim=1       )
                  else:
                         A_						=      noise_pred

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

           if not return_dict:
                  return (samples,)

           return ImagePipelineOutput(images=UpperCamelCase__       )



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__ = 0,       UpperCAmelCase__ = 0				)			->			int:
       A_						=      right or len(UpperCAmelCase__				) - 1
       if left > right:
              return -1
       elif list_data[left] == key:
              return left
       elif list_data[right] == key:
              return right
       else:
              return search(UpperCAmelCase__,       UpperCAmelCase__,       left + 1,       right - 1				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
