




'''simple docstring'''





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			(     __a							):




				'''simple docstring'''





				def __init__(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							super().__init__()

							# make sure scheduler can always be converted to DDIM
							snake_case:       Optional[int]        =       DDIMScheduler.from_config(scheduler.config				)

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







				@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  ,						):
							'''simple docstring'''







							if isinstance(self.unet.config.sample_size  ,						lowercase_				):
										snake_case:       int        =       (
										    batch_size,
										    self.unet.config.in_channels,
										    self.unet.config.sample_size,
										    self.unet.config.sample_size,
										)
							else:
										snake_case:       Any        =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

							snake_case:       Union[str, Any]        =       randn_tensor(lowercase_  ,						generator=lowercase_  ,						device=self.device  ,						dtype=self.unet.dtype				)

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

							for t in self.progress_bar(self.scheduler.timesteps				):
										# 1. predict noise model_output
										snake_case:       List[Any]        =       self.unet(lowercase_  ,						lowercase_				).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
										snake_case:       str        =       self.scheduler.step(
										    lowercase_  ,						lowercase_  ,						lowercase_  ,						eta=lowercase_  ,						use_clipped_model_output=lowercase_  ,						generator=lowercase_				).prev_sample

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

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=lowercase_				)
'''simple docstring'''


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


__snake_case						  =		logging.get_logger(__name__)





@dataclass
class       _a    (		__a      ):


  """simple docstring"""

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




  def __init__(		self      :       int   ,				**lowercase_      :       int      ):







    '''simple docstring'''


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

    lowercase_     =       kwargs.pop("""torchscript"""   ,				self.torchscript      )
    lowercase_     =       kwargs.pop("""torch_xla_tpu_print_metrics"""   ,				self.torch_xla_tpu_print_metrics      )
    lowercase_     =       kwargs.pop("""fp16_opt_level"""   ,				self.fpaa_opt_level      )
    super().__init__(**lowercase_      )

  A_    							=						field(default=__a ,				metadata={'''help''': '''Trace the models using torchscript'''}      )
  A_    							=						field(default=__a ,				metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}      )
  A_    							=						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 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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




  @property
  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    return is_torch_tpu_available() and self.tpu




  @property
  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


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




  @property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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




  @property
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


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





  @property
  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    return self.n_gpu > 0

