



"""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 (					lowerCAmelCase):
			"""simple docstring"""



			def __init__(		self     :    str				,   __UpperCamelCase     :    TransformeraDModel				,   __UpperCamelCase     :    AutoencoderKL				,   __UpperCamelCase     :    KarrasDiffusionSchedulers				,   __UpperCamelCase     :    Optional[Dict[int, str]] = None				,   )->Dict:
							super().__init__()
							self.register_modules(transformer=__UpperCamelCase				,   vae=__UpperCamelCase				,   scheduler=__UpperCamelCase   )

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



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    Union[str, List[str]]   )->List[int]:

							if not isinstance(__UpperCamelCase				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    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     :    List[str]				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    float = 4.0				,   __UpperCamelCase     :    Optional[Union[torch.Generator, List[torch.Generator]]] = None				,   __UpperCamelCase     :    int = 5_0				,   __UpperCamelCase     :    Optional[str] = "pil"				,   __UpperCamelCase     :    bool = True				,   )->Union[ImagePipelineOutput, Tuple]:
							_UpperCAmelCase					=    len(__UpperCamelCase   )
							_UpperCAmelCase					=    self.transformer.config.sample_size
							_UpperCAmelCase					=    self.transformer.config.in_channels

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

							_UpperCAmelCase					=    torch.tensor(__UpperCamelCase				,   device=self.device   ).reshape(-1   )
							_UpperCAmelCase					=    torch.tensor([1_0_0_0] * batch_size				,   device=self.device   )
							_UpperCAmelCase					=    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					=    latent_model_input[: len(__UpperCamelCase   ) // 2]
															_UpperCAmelCase					=    torch.cat([half, half]				,   dim=0   )
											_UpperCAmelCase					=    self.scheduler.scale_model_input(__UpperCamelCase				,   __UpperCamelCase   )

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

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

															_UpperCAmelCase					=    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
															_UpperCAmelCase					=    torch.cat([half_eps, half_eps]				,   dim=0   )

															_UpperCAmelCase					=    torch.cat([eps, rest]				,   dim=1   )

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

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

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

							_UpperCAmelCase					=    1 / self.vae.config.scaling_factor * latents
							_UpperCAmelCase					=    self.vae.decode(__UpperCamelCase   ).sample

							_UpperCAmelCase					=    (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					=    samples.cpu().permute(0				,   2				,   3				,   1   ).float().numpy()

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

							if not return_dict:
											return (samples,)

							return ImagePipelineOutput(images=__UpperCamelCase   )








"""simple docstring"""


def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				if a < 0 or b < 0:
								raise ValueError('''the value of both inputs must be positive'''				)

				_UpperCAmelCase					=    str(bin(_SCREAMING_SNAKE_CASE				)				)[2:]  # remove the leading "0b"
				_UpperCAmelCase					=    str(bin(_SCREAMING_SNAKE_CASE				)				)[2:]  # remove the leading "0b"

				_UpperCAmelCase					=    max(len(_SCREAMING_SNAKE_CASE				)					,     len(_SCREAMING_SNAKE_CASE				)				)

				return "0b" + "".join(
				    str(int(char_a == '''1''' and char_b == '''1'''				)				)
				    for char_a, char_b in zip(a_binary.zfill(_SCREAMING_SNAKE_CASE				)					,     b_binary.zfill(_SCREAMING_SNAKE_CASE				)				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



