code: 
  
  
  
  
  
  import unittest
  
  import numpy as np
  import PIL
  import torch
  
  from diffusers.image_processor import VaeImageProcessor
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					unittest.TestCase):
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Optional[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =1
          a__			:      Union[str, Any]            =3
          a__			:      Dict            =8
          a__			:      Optional[Any]            =8
  
          a__			:      Any            =torch.rand((batch_size, num_channels, height, width)							)
  
          return sample
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if isinstance(image[0]	,   PIL.Image.Image							):
            return np.stack([np.array(lowerCAmelCase__							) for i in image]	,   axis=0							)
          elif isinstance(lowerCAmelCase__	,   torch.Tensor							):
            return image.cpu().numpy().transpose(0	,   2	,   3	,   1							)
          return image
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)
  
          a__			:      Optional[int]            =self.dummy_sample
          a__			:      str            =self.to_np(lowerCAmelCase__							)
  
          for output_type in ["pt", "np", "pil"]:
            a__			:      Optional[int]            =image_processor.postprocess(
                image_processor.preprocess(lowerCAmelCase__							)	,   output_type=lowerCAmelCase__	,   )
            a__			:      Dict            =self.to_np(lowerCAmelCase__							)
            a__			:      int            =(input_np * 2_5_5).round() if output_type == "pil" else input_np
            assert (
                np.abs(in_np - out_np							).max() < 1E-6
            ), F'''decoded output does not match input for output_type {output_type}'''
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)
          a__			:      Optional[int]            =self.dummy_sample.cpu().numpy().transpose(0	,   2	,   3	,   1							)
  
          for output_type in ["pt", "np", "pil"]:
            a__			:      List[Any]            =image_processor.postprocess(image_processor.preprocess(lowerCAmelCase__							)	,   output_type=lowerCAmelCase__							)
  
            a__			:      Union[str, Any]            =self.to_np(lowerCAmelCase__							)
            a__			:      Optional[int]            =(input_np * 2_5_5).round() if output_type == "pil" else input_np
            assert (
                np.abs(in_np - out_np							).max() < 1E-6
            ), F'''decoded output does not match input for output_type {output_type}'''
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)
  
          a__			:      Union[str, Any]            =self.dummy_sample.cpu().numpy().transpose(0	,   2	,   3	,   1							)
          a__			:      List[Any]            =image_processor.numpy_to_pil(lowerCAmelCase__							)
  
          for output_type in ["pt", "np", "pil"]:
            a__			:      List[str]            =image_processor.postprocess(image_processor.preprocess(lowerCAmelCase__							)	,   output_type=lowerCAmelCase__							)
            for i, o in zip(lowerCAmelCase__	,   lowerCAmelCase__							):
              a__			:      List[str]            =np.array(lowerCAmelCase__							)
              a__			:      Optional[int]            =self.to_np(lowerCAmelCase__							) if output_type == "pil" else (self.to_np(lowerCAmelCase__							) * 2_5_5).round()
              assert (
                  np.abs(in_np - out_np							).max() < 1E-6
              ), F'''decoded output does not match input for output_type {output_type}'''
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)
  
          a__			:      Optional[int]            =self.dummy_sample
          a__			:      List[str]            =input_pt_ad.squeeze(0							)
  
          a__			:      Any            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
          a__			:      Optional[int]            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
  
          a__			:      Optional[Any]            =self.to_np(self.dummy_sample							)
          a__			:      Tuple            =input_np_ad.squeeze(0							)
  
          a__			:      Any            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
          a__			:      Optional[int]            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
  
          assert np.abs(out_pt_ad - out_pt_ad							).max() < 1E-6
          assert np.abs(out_np_ad - out_np_ad							).max() < 1E-6
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =VaeImageProcessor(do_resize=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__							)
  
          a__			:      Union[str, Any]            =self.dummy_sample
          a__			:      Optional[int]            =list(lowerCAmelCase__							)
  
          a__			:      List[Any]            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
  
          a__			:      Tuple            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
  
          a__			:      Tuple            =self.to_np(self.dummy_sample							)
          list(lowerCAmelCase__							)
  
          a__			:      Optional[Any]            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
  
          a__			:      Any            =image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__							)	,   output_type="np"	,   )
  
          assert np.abs(out_pt_ad - out_pt_list							).max() < 1E-6
          assert np.abs(out_np_ad - out_np_list							).max() < 1E-6
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  def   _A (	):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[int]            =ArgumentParser(
        description=(
            "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
        )		)
  
    # Optional arguments for the launch helper
    parser.add_argument("--num_cores" , type=SCREAMING_SNAKE_CASE , default=1 , help="Number of TPU cores to use (1 or 8)."		)
  
    # positional
    parser.add_argument(
        "training_script" , type=SCREAMING_SNAKE_CASE , help=(
            "The full path to the single TPU training "
            "program/script to be launched in parallel, "
            "followed by all the arguments for the "
            "training script"
        ) , )
  
    # rest from the training program
    parser.add_argument("training_script_args" , nargs=SCREAMING_SNAKE_CASE		)
  
    return parser.parse_args()
  def   _A (	):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[Any]            =parse_args()
  
    # Import training_script as a module.
    a__			:      Tuple            =Path(args.training_script		)
    sys.path.append(str(script_fpath.parent.resolve()		)		)
    a__			:      Tuple            =script_fpath.stem
    a__			:      Any            =importlib.import_module(SCREAMING_SNAKE_CASE		)
  
    # Patch sys.argv
    a__			:      List[str]            =[args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores		)]
  
    xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores		)
  
  
  if __name__ == "__main__":
       main()
  
  
  
  
  
  
  