


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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Union[str, Any]      ,__snake_case       :TransformeraDModel      ,__snake_case       :AutoencoderKL      ,__snake_case       :KarrasDiffusionSchedulers      ,__snake_case       :Optional[Dict[int, str]] = None      ,) ->   Optional[Any]:
												super().__init__()
												self.register_modules(transformer=__snake_case      ,vae=__snake_case      ,scheduler=__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(__snake_case				)
																			a__					=					dict(sorted(self.labels.items()				)				)
					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :Union[str, List[str]]				) ->   List[int]:

												if not isinstance(__snake_case      ,__snake_case				):
																			a__					=					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       :Union[str, Any]      ,__snake_case       :List[int]      ,__snake_case       :float = 4.0      ,__snake_case       :Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,__snake_case       :int = 50      ,__snake_case       :Optional[str] = "pil"      ,__snake_case       :bool = True      ,) ->   Union[ImagePipelineOutput, Tuple]:
												a__					=					len(__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=__snake_case      ,device=self.device      ,dtype=self.transformer.dtype      ,)
												a__					=					torch.cat([latents] * 2				) if guidance_scale > 1 else latents

												a__					=					torch.tensor(__snake_case      ,device=self.device				).reshape(-1				)
												a__					=					torch.tensor([10_00] * 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(__snake_case				)

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

																			a__					=					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+)
																										a__					=					latent_model_input.device.type == 'mps'
																										if isinstance(__snake_case      ,__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=__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(
																			    __snake_case      ,timestep=__snake_case      ,class_labels=__snake_case				).sample

																			# perform guidance
																			if guidance_scale > 1:
																										a__				, a__					=					noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																										a__				, a__					=					torch.split(__snake_case      ,len(__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(__snake_case      ,__snake_case      ,dim=1				)
																			else:
																										a__					=					noise_pred

																			# compute previous image: x_t -> x_t-1
																			a__					=					self.scheduler.step(__snake_case      ,__snake_case      ,__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(__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(__snake_case				)

												if not return_dict:
																			return (samples,)

												return ImagePipelineOutput(images=__snake_case				)










from __future__ import annotations






def 							__lowercase       (			__lowerCAmelCase							:    float ,   __lowerCAmelCase							:    float ,   __lowerCAmelCase							:    float       ):
							if (voltage, current, resistance).count(0       ) != 1:
														raise ValueError('One and only one argument must be 0'       )
							if resistance < 0:
														raise ValueError('Resistance cannot be negative'       )
							if voltage == 0:
														return {"voltage": float(current * resistance       )}
							elif current == 0:
														return {"current": voltage / resistance}
							elif resistance == 0:
														return {"resistance": voltage / current}
							else:
														raise ValueError('Exactly one argument must be 0'       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




