code: 
  
  
  
  """simple docstring"""
  from typing import List, Optional, Tuple, Union
  
  import torch
  
  from ...models import UNetaDModel
  from ...schedulers import PNDMScheduler
  from ...utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  class 							_UpperCAmelCase							(				lowerCamelCase_   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =42
       a__             =42
  
  
  
  
  
       def __init__(      self ,			A ,			A	)							->      Optional[Any]:
           super().__init__()
  
           _UpperCAmelCase							:						Optional[Any]       							=					PNDMScheduler.from_config(scheduler.config	)
  
           self.register_modules(unet=A ,			scheduler=A	)
  
  
  
  
  
       @torch.no_grad()
       def __call__(      self ,			A = 1 ,			A = 5_0 ,			A = None ,			A = "pil" ,			A = True ,			**A ,			)							->      Tuple:
           _UpperCAmelCase							:						Union[str, Any]       							=					randn_tensor(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,			generator=A ,			device=self.device ,			)
  
           self.scheduler.set_timesteps(A	)
           for t in self.progress_bar(self.scheduler.timesteps	):
               _UpperCAmelCase							:						Union[str, Any]       							=					self.unet(A ,			A	).sample
  
               _UpperCAmelCase							:						Union[str, Any]       							=					self.scheduler.step(A ,			A ,			A	).prev_sample
  
           _UpperCAmelCase							:						Any       							=					(image / 2 + 0.5).clamp(0 ,			1	)
           _UpperCAmelCase							:						Any       							=					image.cpu().permute(0 ,			2 ,			3 ,			1	).numpy()
           if output_type == "pil":
               _UpperCAmelCase							:						int       							=					self.numpy_to_pil(A	)
  
           if not return_dict:
               return (image,)
  
           return ImagePipelineOutput(images=A	)
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import os
  
  from apache_beam.io.filesystems import FileSystems
  from apache_beam.pipeline import Pipeline
  
  from .logging import get_logger
  
  
  _lowerCAmelCase					:Optional[int]  =      2 << 20  # 2mb
  _lowerCAmelCase					:Union[str, Any]  =      get_logger(__name__)
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Any:
           _UpperCAmelCase							:						str       							=					self._options.get_all_options().get('''runner'''	)
           return runner in [None, "DirectRunner", "PortableRunner"]
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	str  , UpperCamelCase__	:	Dict=False							):
      _UpperCAmelCase							:						Union[str, Any]       							=					FileSystems
      if fs.exists(UpperCamelCase__							):
          if force_upload:
              logger.info(F'Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'							)
          else:
              logger.info(F'Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'							)
              return
      with fs.create(UpperCamelCase__							) as remote_file:
          with open(UpperCamelCase__  , '''rb'''							) as local_file:
              _UpperCAmelCase							:						Optional[Any]       							=					local_file.read(UpperCamelCase__							)
              while chunk:
                  remote_file.write(UpperCamelCase__							)
                  _UpperCAmelCase							:						List[Any]       							=					local_file.read(UpperCamelCase__							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	int  , UpperCamelCase__	:	Dict  , UpperCamelCase__	:	str=False							):
      _UpperCAmelCase							:						int       							=					FileSystems
      if os.path.exists(UpperCamelCase__							):
          if force_download:
              logger.info(F'Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'							)
          else:
              logger.info(F'Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'							)
              return
      with fs.open(UpperCamelCase__							) as remote_file:
          with open(UpperCamelCase__  , '''wb'''							) as local_file:
              _UpperCAmelCase							:						List[str]       							=					remote_file.read(UpperCamelCase__							)
              while chunk:
                  local_file.write(UpperCamelCase__							)
                  _UpperCAmelCase							:						List[Any]       							=					remote_file.read(UpperCamelCase__							)
  
  
  