





"""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 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					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
									__a			:    Optional[int]      							=	{}
									if idalabel is not None:
													for key, value in idalabel.items():
																	for label in value.split(''','''  ):
																					__a			:    Optional[int]      							=	int(_UpperCAmelCase  )
													__a			:    Optional[Any]      							=	dict(sorted(self.labels.items()  )  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):

									if not isinstance(_UpperCAmelCase						,      _UpperCAmelCase  ):
													__a			:    Optional[Any]      							=	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						,      ):
									__a			:    List[Any]      							=	len(_UpperCAmelCase  )
									__a			:    str      							=	self.transformer.config.sample_size
									__a			:    int      							=	self.transformer.config.in_channels

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

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

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

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

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

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

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

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

									if guidance_scale > 1:
													__a							,    __a			:    str      							=	latent_model_input.chunk(2						,      dim=0  )
									else:
													__a			:    int      							=	latent_model_input

									__a			:    List[str]      							=	1 / self.vae.config.scaling_factor * latents
									__a			:    Dict      							=	self.vae.decode(_UpperCAmelCase  ).sample

									__a			:    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
									__a			:    int      							=	samples.cpu().permute(0						,      2						,      3						,      1  ).float().numpy()

									if output_type == "pil":
													__a			:    int      							=	self.numpy_to_pil(_UpperCAmelCase  )

									if not return_dict:
													return (samples,)

									return ImagePipelineOutput(images=_UpperCAmelCase  )




'''simple docstring'''
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						return int((input_a, input_a).count(0				) != 0				)






def A__       (	):
						assert nand_gate(0    ,    0				) == 1
						assert nand_gate(0    ,    1				) == 1
						assert nand_gate(1    ,    0				) == 1
						assert nand_gate(1    ,    1				) == 0


if __name__ == "__main__":
					print(nand_gate(0, 0))
					print(nand_gate(0, 1))
					print(nand_gate(1, 0))
					print(nand_gate(1, 1))


