






'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


__A      :       Union[str, Any]      =	logging.get_logger(__name__)






class 					a__    (			lowerCamelCase__						):
     '''simple docstring'''







     _snake_case             =  ['pixel_values']






     def __init__(self    :	List[str]				,				lowercase__    :	Any = True				,				lowercase__    :	Optional[int] = None				,				lowercase__    :	Tuple = PIL.Image.NEAREST				,				lowercase__    :	List[str] = False				,				lowercase__    :	Optional[Any] = None				,				lowercase__    :	Optional[int] = 1 / 255				,				lowercase__    :	Optional[int] = False				,				lowercase__    :	str = True				,				lowercase__    :	List[Any] = True				,				lowercase__    :	Optional[int] = None				,				lowercase__    :	Optional[Any] = None				,				lowercase__    :	List[str] = True				,				**lowercase__    :	List[str]				,				)  ->       None:
           super().__init__(**lowercase_					)
           __A		   =      size if size is not None else {"height": 346, "width": 346}
           __A		   =      get_size_dict(lowercase_					)
           __A		   =      crop_size if crop_size is not None else {"height": 289, "width": 289}
           __A		   =      get_size_dict(lowercase_				,				param_name="crop_size"					)

           __A		   =      do_resize
           __A		   =      size
           __A		   =      resample
           __A		   =      do_center_crop
           __A		   =      crop_size
           __A		   =      do_rescale
           __A		   =      rescale_factor
           __A		   =      rescale_offset
           __A		   =      do_normalize
           __A		   =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           __A		   =      image_std if image_std is not None else IMAGENET_STANDARD_STD
           __A		   =      include_top






     def     _UpperCamelCase   (self    :	List[Any]				,				lowercase__    :	List[str]				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	Optional[int] = PIL.Image.NEAREST				,				lowercase__    :	int = None				,				**lowercase__    :	int				,				)  ->       np.ndarray:
           __A		   =      get_size_dict(lowercase_					)
           if "height" not in size or "width" not in size:
                 raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"					)
           return resize(
               lowercase_				,				size=(size["height"], size["width"])				,				resample=lowercase_				,				data_format=lowercase_				,				**lowercase_					)






     def     _UpperCamelCase   (self    :	List[Any]				,				lowercase__    :	Dict				,				lowercase__    :	List[Any]				,				lowercase__    :	Any = None				,				**lowercase__    :	List[str]				,				)  ->       np.ndarray:
           __A		   =      get_size_dict(lowercase_					)
           if "height" not in size or "width" not in size:
                 raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"					)
           return center_crop(lowercase_				,				size=(size["height"], size["width"])				,				data_format=lowercase_				,				**lowercase_					)






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	Tuple				,				lowercase__    :	int				,				lowercase__    :	Optional[int] = True				,				lowercase__    :	List[Any] = None				,				**lowercase__    :	List[Any]				,				)  ->       Union[str, Any]:






           if offset:
                 __A		   =      (image - 1_27.5) * scale
                 if data_format is not None:
                       __A		   =      to_channel_dimension_format(lowercase_				,				lowercase_					)
                 __A		   =      rescaled_image.astype(np.floataa					)
           else:
                 __A		   =      rescale(lowercase_				,				scale=lowercase_				,				data_format=lowercase_				,				**lowercase_					)
           return rescaled_image






     def     _UpperCamelCase   (self    :	Dict				,				lowercase__    :	str				,				lowercase__    :	List[str]				,				lowercase__    :	List[str]				,				lowercase__    :	str = None				,				**lowercase__    :	Dict				,				)  ->       np.ndarray:
           return normalize(lowercase_				,				mean=lowercase_				,				std=lowercase_				,				data_format=lowercase_				,				**lowercase_					)






     def     _UpperCamelCase   (self    :	Optional[int]				,				lowercase__    :	int				,				lowercase__    :	Optional[int] = None				,				lowercase__    :	int = None				,				lowercase__    :	Optional[int]=None				,				lowercase__    :	Union[str, Any] = None				,				lowercase__    :	Optional[Any] = None				,				lowercase__    :	List[str] = None				,				lowercase__    :	str = None				,				lowercase__    :	Dict = None				,				lowercase__    :	Tuple = None				,				lowercase__    :	Optional[Any] = None				,				lowercase__    :	Dict = None				,				lowercase__    :	List[Any] = None				,				lowercase__    :	str = None				,				lowercase__    :	Tuple = ChannelDimension.FIRST				,				**lowercase__    :	Dict				,				)  ->       PIL.Image.Image:
           __A		   =      do_resize if do_resize is not None else self.do_resize
           __A		   =      resample if resample is not None else self.resample
           __A		   =      do_center_crop if do_center_crop is not None else self.do_center_crop
           __A		   =      do_rescale if do_rescale is not None else self.do_rescale
           __A		   =      rescale_factor if rescale_factor is not None else self.rescale_factor
           __A		   =      rescale_offset if rescale_offset is not None else self.rescale_offset
           __A		   =      do_normalize if do_normalize is not None else self.do_normalize
           __A		   =      image_mean if image_mean is not None else self.image_mean
           __A		   =      image_std if image_std is not None else self.image_std
           __A		   =      include_top if include_top is not None else self.include_top

           __A		   =      size if size is not None else self.size
           __A		   =      get_size_dict(lowercase_					)
           __A		   =      crop_size if crop_size is not None else self.crop_size
           __A		   =      get_size_dict(lowercase_				,				param_name="crop_size"					)

           __A		   =      make_list_of_images(lowercase_					)

           if not valid_images(lowercase_					):
                 raise ValueError(
                     "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                     "torch.Tensor, tf.Tensor or jax.ndarray."					)

           if do_resize and size is None or resample is None:
                 raise ValueError("Size and resample must be specified if do_resize is True."					)

           if do_center_crop and crop_size is None:
                 raise ValueError("Crop size must be specified if do_center_crop is True."					)

           if do_rescale and rescale_factor is None:
                 raise ValueError("Rescale factor must be specified if do_rescale is True."					)

           if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError("Image mean and std must be specified if do_normalize is True."					)

           # All transformations expect numpy arrays.
           __A		   =      [to_numpy_array(lowercase_					) for image in images]

           if do_resize:
                 __A		   =      [self.resize(image=lowercase_				,				size=lowercase_				,				resample=lowercase_					) for image in images]

           if do_center_crop:
                 __A		   =      [self.center_crop(image=lowercase_				,				size=lowercase_					) for image in images]

           if do_rescale:
                 __A		   =      [self.rescale(image=lowercase_				,				scale=lowercase_				,				offset=lowercase_					) for image in images]

           if do_normalize:
                 __A		   =      [self.normalize(image=lowercase_				,				mean=lowercase_				,				std=lowercase_					) for image in images]

           if include_top:
                 __A		   =      [self.normalize(image=lowercase_				,				mean=[0, 0, 0]				,				std=lowercase_					) for image in images]

           __A		   =      [to_channel_dimension_format(lowercase_				,				lowercase_					) for image in images]

           __A		   =      {"pixel_values": images}
           return BatchFeature(data=lowercase_				,				tensor_type=lowercase_					)









'''simple docstring'''







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def 						_UpperCamelCase							(     ):
      __A		   =      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=snake_case__							,			default=1							,			help="Number of TPU cores to use (1 or 8)."							)

      # positional
      parser.add_argument(
          "training_script"							,			type=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=snake_case__							)

      return parser.parse_args()


def 						_UpperCamelCase							(     ):
      __A		   =      parse_args()

      # Import training_script as a module.
      __A		   =      Path(args.training_script							)
      sys.path.append(str(script_fpath.parent.resolve()							)							)
      __A		   =      script_fpath.stem
      __A		   =      importlib.import_module(snake_case__							)

      # Patch sys.argv
      __A		   =      [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()


