






import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 	snake_case__ (							_lowerCAmelCase				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Any:
							super().__init__()
							self.register_modules(vqvae=lowerCAmelCase__      ,       unet=lowerCAmelCase__      ,       scheduler=lowerCAmelCase__			)







	@torch.no_grad()
	def __call__(    self      ,       lowerCAmelCase__ = 1      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = 0.0      ,       lowerCAmelCase__ = 50      ,       lowerCAmelCase__ = "pil"      ,       lowerCAmelCase__ = True      ,       **lowerCAmelCase__      ,       )							->				Union[Tuple, ImagePipelineOutput]:
							__magic_name__      :       Dict       						= randn_tensor(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,       generator=lowerCAmelCase__      ,       )
							__magic_name__      :       Any       						= latents.to(self.device			)

							# scale the initial noise by the standard deviation required by the scheduler
							__magic_name__      :       Tuple       						= latents * self.scheduler.init_noise_sigma

							self.scheduler.set_timesteps(lowerCAmelCase__			)

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							__magic_name__      :       Dict       						= """eta""" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)

							__magic_name__      :       List[Any]       						= {}
							if accepts_eta:
													__magic_name__      :       Tuple       						= eta

							for t in self.progress_bar(self.scheduler.timesteps			):
													__magic_name__      :       Union[str, Any]       						= self.scheduler.scale_model_input(lowerCAmelCase__      ,       lowerCAmelCase__			)
													# predict the noise residual
													__magic_name__      :       Union[str, Any]       						= self.unet(lowerCAmelCase__      ,       lowerCAmelCase__			).sample
													# compute the previous noisy sample x_t -> x_t-1
													__magic_name__      :       List[Any]       						= self.scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample

							# decode the image latents with the VAE
							__magic_name__      :       int       						= self.vqvae.decode(lowerCAmelCase__			).sample

							__magic_name__      :       Union[str, Any]       						= (image / 2 + 0.5).clamp(0      ,       1			)
							__magic_name__      :       Dict       						= image.cpu().permute(0      ,       2      ,       3      ,       1			).numpy()
							if output_type == "pil":
													__magic_name__      :       Optional[Any]       						= self.numpy_to_pil(lowerCAmelCase__			)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=lowerCAmelCase__			)








import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__magic_name__:					int							=     "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
		pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
		pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
		if pkg in deps:
				if pkg == "tokenizers":
						# must be loaded here, or else tqdm check may fail
						from .utils import is_tokenizers_available

						if not is_tokenizers_available():
								continue  # not required, check version only if installed

				require_version_core(deps[pkg])
		else:
				raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")

def    UpperCamelCase						(	_A,   _A=None			):






						"""simple docstring"""







						require_version(deps[pkg],   _A			)
