


'''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  lowerCAmelCase_						(       snake_case__      ):





		"""simple docstring"""



		def __init__(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							TransformeraDModel				,							SCREAMING_SNAKE_CASE__     :							AutoencoderKL				,							SCREAMING_SNAKE_CASE__     :							KarrasDiffusionSchedulers				,							SCREAMING_SNAKE_CASE__     :							Optional[Dict[int, str]] = None				,							):





							'''simple docstring'''

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

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

		def __a						(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, List[str]]						):





							'''simple docstring'''

							if not isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						):
												__a       =   list(SCREAMING_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     :							Dict				,							SCREAMING_SNAKE_CASE__     :							List[int]				,							SCREAMING_SNAKE_CASE__     :							float = 4.0				,							SCREAMING_SNAKE_CASE__     :							Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							SCREAMING_SNAKE_CASE__     :							int = 5_0				,							SCREAMING_SNAKE_CASE__     :							Optional[str] = "pil"				,							SCREAMING_SNAKE_CASE__     :							bool = True				,							):





							'''simple docstring'''

							__a       =   len(SCREAMING_SNAKE_CASE__						)
							__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=SCREAMING_SNAKE_CASE__				,							device=self.device				,							dtype=self.transformer.dtype				,							)
							__a       =   torch.cat([latents] * 2						) if guidance_scale > 1 else latents

							__a       =   torch.tensor(SCREAMING_SNAKE_CASE__				,							device=self.device						).reshape(-1						)
							__a       =   torch.tensor([1_0_0_0] * 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(SCREAMING_SNAKE_CASE__						)

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

												__a       =   t
												if not torch.is_tensor(SCREAMING_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+)
																	__a       =   latent_model_input.device.type == """mps"""
																	if isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						):
																						__a       =   torch.floataa if is_mps else torch.floataa
																	else:
																						__a       =   torch.intaa if is_mps else torch.intaa
																	__a       =   torch.tensor([timesteps]				,							dtype=SCREAMING_SNAKE_CASE__				,							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(
												    SCREAMING_SNAKE_CASE__				,							timestep=SCREAMING_SNAKE_CASE__				,							class_labels=SCREAMING_SNAKE_CASE__						).sample

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

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

							if not return_dict:
												return (samples,)

							return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__						)










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     return " ".join(input_str.split()[::-1]		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






