
from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
				import torch

if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm


lowercase__							:							List[str]									=			logging.get_logger(__name__)






@dataclass
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  [
							    'no_inference',
							    'no_cuda',
							    'no_tpu',
							    'no_speed',
							    'no_memory',
							    'no_env_print',
							    'no_multi_process',
							]



							def __init__(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													for deprecated_arg in self.deprecated_args:
																			if deprecated_arg in kwargs:
																									__UpperCamelCase       =			deprecated_arg[3:]
																									setattr(self     ,	SCREAMING_SNAKE_CASE_     ,	not kwargs.pop(SCREAMING_SNAKE_CASE_			)			)
																									logger.warning(
																									    F"{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"
																									    F" {positive_arg}={kwargs[positive_arg]}"			)

													__UpperCamelCase       =			kwargs.pop('''torchscript'''     ,	self.torchscript			)
													__UpperCamelCase       =			kwargs.pop('''torch_xla_tpu_print_metrics'''     ,	self.torch_xla_tpu_print_metrics			)
													__UpperCamelCase       =			kwargs.pop('''fp16_opt_level'''     ,	self.fpaa_opt_level			)
													super().__init__(**SCREAMING_SNAKE_CASE_			)

							_snake_case     		=  field(default=SCREAMING_SNAKE_CASE_   ,		metadata={'help': 'Trace the models using torchscript'}    )
							_snake_case     		=  field(default=SCREAMING_SNAKE_CASE_   ,		metadata={'help': 'Print Xla/PyTorch tpu metrics'}    )
							_snake_case     		=  field(
							    default='O1'   ,		metadata={
							        'help': (
							            'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '
							            'See details at https://nvidia.github.io/apex/amp.html'
							        )
							    }   ,		)



							@cached_property
							def     A__		(  self			)->   Tuple["torch.device", int]:





													'''simple docstring'''






													requires_backends(self     ,	['''torch''']			)
													logger.info('''PyTorch: setting up devices'''			)
													if not self.cuda:
																			__UpperCamelCase       =			torch.device('''cpu'''			)
																			__UpperCamelCase       =			0
													elif is_torch_tpu_available():
																			__UpperCamelCase       =			xm.xla_device()
																			__UpperCamelCase       =			0
													else:
																			__UpperCamelCase       =			torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''			)
																			__UpperCamelCase       =			torch.cuda.device_count()
													return device, n_gpu



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return is_torch_tpu_available() and self.tpu



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													requires_backends(self     ,	['''torch''']			)
													# TODO(PVP): currently only single GPU is supported
													return torch.cuda.current_device()



							@property
							def     A__		(  self			)->   "torch.device":





													'''simple docstring'''






													requires_backends(self     ,	['''torch''']			)
													return self._setup_devices[0]



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													requires_backends(self     ,	['''torch''']			)
													return self._setup_devices[1]



							@property
							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													return self.n_gpu > 0






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													super().__init__()

													# make sure scheduler can always be converted to DDIM
													__UpperCamelCase       =			DDIMScheduler.from_config(scheduler.config			)

													self.register_modules(unet=SCREAMING_SNAKE_CASE_     ,	scheduler=SCREAMING_SNAKE_CASE_			)



							@torch.no_grad()
							def __call__(  self     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = 0.0     ,	SCREAMING_SNAKE_CASE_ = 50     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "pil"     ,	SCREAMING_SNAKE_CASE_ = True     ,	)->   Union[ImagePipelineOutput, Tuple]:





													'''simple docstring'''






													if isinstance(self.unet.config.sample_size     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			(
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			__UpperCamelCase       =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) and len(SCREAMING_SNAKE_CASE_			) != batch_size:
																			raise ValueError(
																			    F"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_			)}, but requested an effective batch"
																			    F" size of {batch_size}. Make sure the batch size matches the length of the generators."			)

													__UpperCamelCase       =			randn_tensor(SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	device=self.device     ,	dtype=self.unet.dtype			)

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

													for t in self.progress_bar(self.scheduler.timesteps			):
																			# 1. predict noise model_output
																			__UpperCamelCase       =			self.unet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			).sample

																			# 2. predict previous mean of image x_t-1 and add variance depending on eta
																			# eta corresponds to η in paper and should be between [0, 1]
																			# do x_t -> x_t-1
																			__UpperCamelCase       =			self.scheduler.step(
																			    SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	eta=SCREAMING_SNAKE_CASE_     ,	use_clipped_model_output=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_			).prev_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(SCREAMING_SNAKE_CASE_			)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_			)




