
"""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     _UpperCAmelCase(    UpperCamelCase_	):



		def __init__(					self				,		__a				,		__a				,		__a				,		__a = None				,		)       ->       Optional[int]:






							'''simple docstring'''
							super().__init__()
							self.register_modules(transformer=lowercase_				,		vae=lowercase_				,		scheduler=lowercase_)

							# 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(lowercase_)
												_UpperCamelCase							 =  dict(sorted(self.labels.items()))



		def 						UpperCAmelCase							(					self				,		__a)       ->       List[int]:






							'''simple docstring'''




							if not isinstance(lowercase_				,		lowercase_):
												_UpperCamelCase							 =  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				,		__a				,		__a = 4.0				,		__a = None				,		__a = 50				,		__a = "pil"				,		__a = True				,		)       ->       Union[ImagePipelineOutput, Tuple]:






							'''simple docstring'''
							_UpperCamelCase							 =  len(lowercase_)
							_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=lowercase_				,		device=self.device				,		dtype=self.transformer.dtype				,		)
							_UpperCamelCase							 =  torch.cat([latents] * 2) if guidance_scale > 1 else latents

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

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

												_UpperCamelCase							 =  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+)
																	_UpperCamelCase							 =  latent_model_input.device.type == 'mps'
																	if isinstance(lowercase_				,		lowercase_):
																						_UpperCamelCase							 =  torch.floataa if is_mps else torch.floataa
																	else:
																						_UpperCamelCase							 =  torch.intaa if is_mps else torch.intaa
																	_UpperCamelCase							 =  torch.tensor([timesteps]				,		dtype=lowercase_				,		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(
												    lowercase_				,		timestep=lowercase_				,		class_labels=lowercase_).sample

												# perform guidance
												if guidance_scale > 1:
																	_UpperCamelCase							 =  noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																	_UpperCamelCase							 =  torch.split(lowercase_				,		len(lowercase_) // 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							 =  torch.split(lowercase_				,		lowercase_				,		dim=1)
												else:
																	_UpperCamelCase							 =  noise_pred

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

							if guidance_scale > 1:
												_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(lowercase_).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(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 A  (   ctypes.Structure     ):
       # _fields is a specific attr expected by ctypes
       UpperCamelCase__							:  List[Any]      =[('size', ctypes.c_int), ('visible', ctypes.c_byte)]




def 	a_      (    ):
       '''simple docstring'''





       if os.name == "nt":
              _lowerCamelCase       :		Optional[Any]		=CursorInfo()
              _lowerCamelCase       :		Dict		=ctypes.windll.kernelaa.GetStdHandle(-11     )
              ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
              _lowerCamelCase       :		Any		=False
              ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
       elif os.name == "posix":
              sys.stdout.write('\033[?25l'     )
              sys.stdout.flush()




def 	a_      (    ):
       '''simple docstring'''





       if os.name == "nt":
              _lowerCamelCase       :		Any		=CursorInfo()
              _lowerCamelCase       :		Optional[Any]		=ctypes.windll.kernelaa.GetStdHandle(-11     )
              ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
              _lowerCamelCase       :		Union[str, Any]		=True
              ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
       elif os.name == "posix":
              sys.stdout.write('\033[?25h'     )
              sys.stdout.flush()




@contextmanager
def 	a_      (    ):
       '''simple docstring'''





       try:
              hide_cursor()
              yield
       finally:
              show_cursor()


