



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__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase = None ,       ) ->				Union[str, Any]:
									"""simple docstring"""




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

									# create a imagenet -> id dictionary for easier use
									a__       :						int							=  {}
									if idalabel is not None:
												for key, value in idalabel.items():
															for label in value.split(""","""  ):
																		a__       :						Union[str, Any]							=  int(__lowercase  )
												a__       :						Dict							=  dict(sorted(self.labels.items()  )  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[int]:
									"""simple docstring"""




									if not isinstance(__lowercase ,       __lowercase  ):
												a__       :						Any							=  list(__lowercase  )

									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 ,       __lowercase ,       __lowercase = 4.0 ,       __lowercase = None ,       __lowercase = 5_0 ,       __lowercase = "pil" ,       __lowercase = True ,       ) ->				Union[ImagePipelineOutput, Tuple]:
									"""simple docstring"""




									a__       :						Tuple							=  len(__lowercase  )
									a__       :						List[str]							=  self.transformer.config.sample_size
									a__       :						Tuple							=  self.transformer.config.in_channels

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

									a__       :						List[Any]							=  torch.tensor(__lowercase ,       device=self.device  ).reshape(-1  )
									a__       :						Union[str, Any]							=  torch.tensor([1_0_0_0] * batch_size ,       device=self.device  )
									a__       :						Optional[Any]							=  torch.cat([class_labels, class_null] ,       0  ) if guidance_scale > 1 else class_labels

									# set step values
									self.scheduler.set_timesteps(__lowercase  )

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

												a__       :						Tuple							=  t
												if not torch.is_tensor(__lowercase  ):
															# 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__       :						int							=  latent_model_input.device.type == """mps"""
															if isinstance(__lowercase ,       __lowercase  ):
																		a__       :						Union[str, Any]							=  torch.floataa if is_mps else torch.floataa
															else:
																		a__       :						List[Any]							=  torch.intaa if is_mps else torch.intaa
															a__       :						Optional[int]							=  torch.tensor([timesteps] ,       dtype=__lowercase ,       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__       :						Optional[int]							=  self.transformer(
												    __lowercase ,       timestep=__lowercase ,       class_labels=__lowercase  ).sample

												# perform guidance
												if guidance_scale > 1:
															a__	,						a__       :						Optional[Any]							=  noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
															a__	,						a__       :						int							=  torch.split(__lowercase ,       len(__lowercase  ) // 2 ,       dim=0  )

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

															a__       :						List[str]							=  torch.cat([eps, rest] ,       dim=1  )

												# learned sigma
												if self.transformer.config.out_channels // 2 == latent_channels:
															a__	,						a__       :						List[Any]							=  torch.split(__lowercase ,       __lowercase ,       dim=1  )
												else:
															a__       :						List[Any]							=  noise_pred

												# compute previous image: x_t -> x_t-1
												a__       :						List[Any]							=  self.scheduler.step(__lowercase ,       __lowercase ,       __lowercase  ).prev_sample

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

									a__       :						str							=  1 / self.vae.config.scaling_factor * latents
									a__       :						Tuple							=  self.vae.decode(__lowercase  ).sample

									a__       :						Any							=  (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__       :						Optional[Any]							=  samples.cpu().permute(0 ,       2 ,       3 ,       1  ).float().numpy()

									if output_type == "pil":
												a__       :						str							=  self.numpy_to_pil(__lowercase  )

									if not return_dict:
												return (samples,)

									return ImagePipelineOutput(images=__lowercase  )









import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
		import ctypes
		import msvcrt  # noqa

		class        snake_case__    (ctypes.Structure		):







								"""simple docstring"""




								__lowerCAmelCase					:Dict      =					[("size", ctypes.c_int), ("visible", ctypes.c_byte)]

def      lowerCAmelCase_					(  )							->	List[Any]:







			"""simple docstring"""
			if os.name == "nt":
						a__       :						int							=  CursorInfo()
						a__       :						Union[str, Any]							=  ctypes.windll.kernelaa.GetStdHandle(-11)
						ctypes.windll.kernelaa.GetConsoleCursorInfo(_lowercase						,       ctypes.byref(_lowercase))
						a__       :						List[str]							=  False
						ctypes.windll.kernelaa.SetConsoleCursorInfo(_lowercase						,       ctypes.byref(_lowercase))
			elif os.name == "posix":
						sys.stdout.write("""\033[?25l""")
						sys.stdout.flush()

def      lowerCAmelCase_					(  )							->	Optional[Any]:







			"""simple docstring"""
			if os.name == "nt":
						a__       :						List[Any]							=  CursorInfo()
						a__       :						Optional[int]							=  ctypes.windll.kernelaa.GetStdHandle(-11)
						ctypes.windll.kernelaa.GetConsoleCursorInfo(_lowercase						,       ctypes.byref(_lowercase))
						a__       :						Dict							=  True
						ctypes.windll.kernelaa.SetConsoleCursorInfo(_lowercase						,       ctypes.byref(_lowercase))
			elif os.name == "posix":
						sys.stdout.write("""\033[?25h""")
						sys.stdout.flush()





@contextmanager
def      lowerCAmelCase_					(  )							->	Any:







			"""simple docstring"""
			try:
						hide_cursor()
						yield
			finally:
						show_cursor()




