



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


__A       =						logging.get_logger(__name__)






@dataclass
class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: Tuple		       =							[
	    "no_inference",
	    "no_cuda",
	    "no_tpu",
	    "no_speed",
	    "no_memory",
	    "no_env_print",
	    "no_multi_process",
	]




	def __init__(     self     :						int						,  **__UpperCAmelCase     :						str					)      ->			Dict:
							for deprecated_arg in self.deprecated_args:
													if deprecated_arg in kwargs:
																			UpperCAmelCase_=       deprecated_arg[3:]
																			setattr(self						,  __UpperCAmelCase						,  not kwargs.pop(__UpperCAmelCase					)					)
																			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__(**__UpperCAmelCase					)

	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Trace the models using torchscript"})
	a__				: bool		       =							field(default=snake_case__			,   metadata={"help": "Print Xla/PyTorch tpu metrics"})
	a__				: str		       =							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   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Tuple["torch.device", int]:
							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   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Optional[Any]:
							return is_torch_tpu_available() and self.tpu




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			int:
							requires_backends(self						,  ["""torch"""]					)
							# TODO(PVP): currently only single GPU is supported
							return torch.cuda.current_device()




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			"torch.device":
							requires_backends(self						,  ["""torch"""]					)
							return self._setup_devices[0]




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[str]:
							requires_backends(self						,  ["""torch"""]					)
							return self._setup_devices[1]




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Union[str, Any]:
							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 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Dict						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						List[Any]					)      ->			List[str]:
							super().__init__()

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

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




	@torch.no_grad()
	def __call__(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None						,  __UpperCAmelCase     :						float = 0.0						,  __UpperCAmelCase     :						int = 50						,  __UpperCAmelCase     :						Optional[bool] = None						,  __UpperCAmelCase     :						Optional[str] = "pil"						,  __UpperCAmelCase     :						bool = True						,  )      ->			Union[ImagePipelineOutput, Tuple]:

							# Sample gaussian noise to begin loop
							if isinstance(self.unet.config.sample_size						,  __UpperCAmelCase					):
													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(__UpperCAmelCase						,  __UpperCAmelCase					) and len(__UpperCAmelCase					) != batch_size:
													raise ValueError(
													    F"""You have passed a list of generators of length {len(__UpperCAmelCase					)}, but requested an effective batch"""
													    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""					)

							UpperCAmelCase_=       randn_tensor(__UpperCAmelCase						,  generator=__UpperCAmelCase						,  device=self.device						,  dtype=self.unet.dtype					)

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

							for t in self.progress_bar(self.scheduler.timesteps					):
													# 1. predict noise model_output
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					).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(
													    __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  eta=__UpperCAmelCase						,  use_clipped_model_output=__UpperCAmelCase						,  generator=__UpperCAmelCase					).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(__UpperCAmelCase					)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=__UpperCAmelCase					)




