'''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       (__magic_name__   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     ):
              super().__init__()
              self.register_modules(transformer=UpperCamelCase_				,     vae=UpperCamelCase_				,     scheduler=UpperCamelCase_      )

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






       def 			_snake_case						(      self				,     UpperCamelCase_      ):

              if not isinstance(UpperCamelCase_				,     UpperCamelCase_      ):
                     __UpperCAmelCase  :				Tuple       				=		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				,     ):
              __UpperCAmelCase  :				Any       				=		len(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		self.transformer.config.sample_size
              __UpperCAmelCase  :				Optional[int]       				=		self.transformer.config.in_channels

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

              __UpperCAmelCase  :				Union[str, Any]       				=		torch.tensor(UpperCamelCase_				,     device=self.device      ).reshape(-1      )
              __UpperCAmelCase  :				Union[str, Any]       				=		torch.tensor([10_00] * batch_size				,     device=self.device      )
              __UpperCAmelCase  :				Union[str, Any]       				=		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:
                            __UpperCAmelCase  :				Optional[Any]       				=		latent_model_input[: len(UpperCamelCase_      ) // 2]
                            __UpperCAmelCase  :				str       				=		torch.cat([half, half]				,     dim=0      )
                     __UpperCAmelCase  :				str       				=		self.scheduler.scale_model_input(UpperCamelCase_				,     UpperCamelCase_      )

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

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

                            __UpperCAmelCase  :				int       				=		uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                            __UpperCAmelCase  :				Optional[int]       				=		torch.cat([half_eps, half_eps]				,     dim=0      )

                            __UpperCAmelCase  :				Dict       				=		torch.cat([eps, rest]				,     dim=1      )

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

                     # compute previous image: x_t -> x_t-1
                     __UpperCAmelCase  :				str       				=		self.scheduler.step(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ).prev_sample

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

              __UpperCAmelCase  :				Any       				=		1 / self.vae.config.scaling_factor * latents
              __UpperCAmelCase  :				List[Any]       				=		self.vae.decode(UpperCamelCase_      ).sample

              __UpperCAmelCase  :				List[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
              __UpperCAmelCase  :				Optional[Any]       				=		samples.cpu().permute(0				,     2				,     3				,     1      ).float().numpy()

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

              if not return_dict:
                     return (samples,)

              return ImagePipelineOutput(images=UpperCamelCase_      )







'''simple docstring'''




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def _lowercase	(      )					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		HfArgumentParser(lowerCamelCase__						)
       __UpperCAmelCase  :				Optional[Any]       				=		parser.parse_args_into_dataclasses()[0]
       __UpperCAmelCase  :				Any       				=		TensorFlowBenchmark(args=lowerCamelCase__						)
       try:
              __UpperCAmelCase  :				List[Any]       				=		parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              __UpperCAmelCase  :				str       				=		"Arg --no_{0} is no longer used, please use --no-{0} instead."
              __UpperCAmelCase  :				Tuple       				=		" ".join(str(lowerCamelCase__						).split(" "						)[:-1]						)
              __UpperCAmelCase  :				Any       				=		""
              __UpperCAmelCase  :				List[Any]       				=		eval(str(lowerCamelCase__						).split(" "						)[-1]						)
              __UpperCAmelCase  :				Optional[int]       				=		[]
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]						)
                     else:
                            wrong_args.append(lowerCamelCase__						)
              if len(lowerCamelCase__						) > 0:
                     __UpperCAmelCase  :				Union[str, Any]       				=		full_error_msg + begin_error_msg + str(lowerCamelCase__						)
              raise ValueError(lowerCamelCase__						)
       benchmark.run()


if __name__ == "__main__":
   main()






