


"""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      (   A_       ):




		'''simple docstring'''







		def __init__(      self		,			lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__ = None		,			)	->				str:
								super().__init__()
								self.register_modules(transformer=snake_case__		,			vae=snake_case__		,			scheduler=snake_case__							)

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







		def 				__A		(      self		,			lowerCAmelCase__							)	->				List[int]:


								if not isinstance(snake_case__		,			snake_case__							):
														SCREAMING_SNAKE_CASE						=   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		,			lowerCAmelCase__		,			lowerCAmelCase__ = 4.0		,			lowerCAmelCase__ = None		,			lowerCAmelCase__ = 50		,			lowerCAmelCase__ = "pil"		,			lowerCAmelCase__ = True		,			)	->				Union[ImagePipelineOutput, Tuple]:
								SCREAMING_SNAKE_CASE						=   len(snake_case__							)
								SCREAMING_SNAKE_CASE						=   self.transformer.config.sample_size
								SCREAMING_SNAKE_CASE						=   self.transformer.config.in_channels

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

								SCREAMING_SNAKE_CASE						=   torch.tensor(snake_case__		,			device=self.device							).reshape(-1							)
								SCREAMING_SNAKE_CASE						=   torch.tensor([1_000] * batch_size		,			device=self.device							)
								SCREAMING_SNAKE_CASE						=   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:
																				SCREAMING_SNAKE_CASE						=   latent_model_input[: len(snake_case__							) // 2]
																				SCREAMING_SNAKE_CASE						=   torch.cat([half, half]		,			dim=0							)
														SCREAMING_SNAKE_CASE						=   self.scheduler.scale_model_input(snake_case__		,			snake_case__							)

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

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

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

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

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

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

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

								SCREAMING_SNAKE_CASE						=   1 / self.vae.config.scaling_factor * latents
								SCREAMING_SNAKE_CASE						=   self.vae.decode(snake_case__							).sample

								SCREAMING_SNAKE_CASE						=   (samples / 2 + 0.5).clamp(0		,			1							)

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								SCREAMING_SNAKE_CASE						=   samples.cpu().permute(0		,			2		,			3		,			1							).float().numpy()

								if output_type == "pil":
														SCREAMING_SNAKE_CASE						=   self.numpy_to_pil(snake_case__							)

								if not return_dict:
														return (samples,)

								return ImagePipelineOutput(images=snake_case__							)




__lowerCamelCase				=			{
    0: """0""",
    1: """1""",
    2: """2""",
    3: """3""",
    4: """4""",
    5: """5""",
    6: """6""",
    7: """7""",
    8: """8""",
    9: """9""",
    10: """a""",
    11: """b""",
    12: """c""",
    13: """d""",
    14: """e""",
    15: """f""",
}


def      UpperCamelCase   (       __lowerCamelCase	:					float       ):
       assert type(__lowerCamelCase       ) in (int, float) and decimal == int(__lowerCamelCase       )
       snake_case			:  str     							= int(__lowerCamelCase       )
       snake_case			:  str     							= ""
       snake_case			:  str     							= False
       if decimal < 0:
              snake_case			:  Optional[Any]     							= True
              decimal *= -1
       while decimal > 0:
              snake_case							,   snake_case			:  Union[str, Any]     							= divmod(__lowerCamelCase							,   16       )
              snake_case			:  Tuple     							= values[remainder] + hexadecimal
       snake_case			:  Tuple     							= "0x" + hexadecimal
       if negative:
              snake_case			:  Any     							= "-" + hexadecimal
       return hexadecimal


if __name__ == "__main__":
   import doctest

   doctest.testmod()





