


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    UpperCAmelCase					(       __snake_case    ):




			def __init__(						self							:		Any       ,       __magic_name__							:		VQModel       ,       __magic_name__							:		UNetaDModel       ,       __magic_name__							:		DDIMScheduler						):







						"""simple docstring"""







						super().__init__()
						self.register_modules(vqvae=__magic_name__       ,       unet=__magic_name__       ,       scheduler=__magic_name__						)





			@torch.no_grad()
			def __call__(						self							:		Optional[int]       ,       __magic_name__							:		int = 1       ,       __magic_name__							:		Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,       __magic_name__							:		float = 0.0       ,       __magic_name__							:		int = 5_0       ,       __magic_name__							:		Optional[str] = "pil"       ,       __magic_name__							:		bool = True       ,       **__magic_name__							:		Union[str, Any]       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					randn_tensor(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,       generator=__magic_name__       ,       )
						UpperCamelCase         =					latents.to(self.device						)

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

						self.scheduler.set_timesteps(__magic_name__						)

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

						UpperCamelCase         =					{}
						if accepts_eta:
									UpperCamelCase         =					eta

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

						# decode the image latents with the VAE
						UpperCamelCase         =					self.vqvae.decode(__magic_name__						).sample

						UpperCamelCase         =					(image / 2 + 0.5).clamp(0       ,       1						)
						UpperCamelCase         =					image.cpu().permute(0       ,       2       ,       3       ,       1						).numpy()
						if output_type == "pil":
									UpperCamelCase         =					self.numpy_to_pil(__magic_name__						)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__magic_name__						)








import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available






def 							_lowercase    (		*SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			if not isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	):
						UpperCamelCase         =					list(SCREAMING_SNAKE_CASE_	)
			for i in range(len(SCREAMING_SNAKE_CASE_	)	):
						UpperCamelCase         =					None
			gc.collect()
			if is_xpu_available():
						torch.xpu.empty_cache()
			elif is_npu_available():
						torch.npu.empty_cache()
			else:
						torch.cuda.empty_cache()
			return objects






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Exception	):





			"""simple docstring"""




			UpperCamelCase         =					[
			    """CUDA out of memory.""",  # CUDA OOM
			    """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
			    """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
			]
			if isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	) and len(exception.args	) == 1:
						return any(err in exception.args[0] for err in _statements	)
			return False






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		callable = None       ,  SCREAMING_SNAKE_CASE_   :		int = 128	):





			"""simple docstring"""




			if function is None:
						return functools.partial(SCREAMING_SNAKE_CASE_       ,  starting_batch_size=SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					starting_batch_size

			def decorator(*SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  **SCREAMING_SNAKE_CASE_   :		Tuple	):
						nonlocal batch_size
						gc.collect()
						if is_xpu_available():
									torch.xpu.empty_cache()
						elif is_npu_available():
									torch.npu.empty_cache()
						else:
									torch.cuda.empty_cache()
						UpperCamelCase         =					list(inspect.signature(SCREAMING_SNAKE_CASE_	).parameters.keys()	)
						# Guard against user error
						if len(SCREAMING_SNAKE_CASE_	) < (len(SCREAMING_SNAKE_CASE_	) + 1):
									UpperCamelCase         =					""", """.join([f'{arg}={value}' for arg, value in zip(params[1:]       ,  args[1:]	)]	)
									raise TypeError(
									    f'Batch size was passed into `{function.__name__}` as the first argument when called.'
									    f'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'	)
						while True:
									if batch_size == 0:
												raise RuntimeError("""No executable batch size found, reached zero."""	)
									try:
												return function(SCREAMING_SNAKE_CASE_       ,  *SCREAMING_SNAKE_CASE_       ,  **SCREAMING_SNAKE_CASE_	)
									except Exception as e:
												if should_reduce_batch_size(SCREAMING_SNAKE_CASE_	):
															gc.collect()
															if is_xpu_available():
																		torch.xpu.empty_cache()
															elif is_npu_available():
																		torch.npu.empty_cache()
															else:
																		torch.cuda.empty_cache()
															batch_size //= 2
												else:
															raise

			return decorator




