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,
    get_resize_output_image_size,
    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, logging


lowerCAmelCase			:							Any =logging.get_logger(__name__)




class 	__snake_case	( _lowerCamelCase		):





      '''simple docstring'''






      _snake_case    		=       ['pixel_values']



      def __init__(					self     :				Optional[int]    ,  _UpperCamelCase     :				Dict = True    ,  _UpperCamelCase     :				str = None    ,  _UpperCamelCase     :				Optional[int] = PILImageResampling.BILINEAR    ,  _UpperCamelCase     :				Dict = True    ,  _UpperCamelCase     :				str = None    ,  _UpperCamelCase     :				str = True    ,  _UpperCamelCase     :				str = 1 / 255    ,  _UpperCamelCase     :				List[str] = True    ,  _UpperCamelCase     :				Any = None    ,  _UpperCamelCase     :				List[Any] = None    ,  **_UpperCamelCase     :				List[Any]    ,  )				->None:


          """simple docstring"""


          super().__init__(**A__)
          _lowerCamelCase     :		Any 						=						size if size is not None else {"""shortest_edge""": 256}
          _lowerCamelCase     :		List[str] 						=						get_size_dict(A__    ,  default_to_square=A__)
          _lowerCamelCase     :		Optional[int] 						=						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          _lowerCamelCase     :		Union[str, Any] 						=						get_size_dict(A__)
          _lowerCamelCase     :		Tuple 						=						do_resize
          _lowerCamelCase     :		List[str] 						=						size
          _lowerCamelCase     :		Union[str, Any] 						=						resample
          _lowerCamelCase     :		Union[str, Any] 						=						do_center_crop
          _lowerCamelCase     :		Union[str, Any] 						=						crop_size
          _lowerCamelCase     :		str 						=						do_rescale
          _lowerCamelCase     :		str 						=						rescale_factor
          _lowerCamelCase     :		Tuple 						=						do_normalize
          _lowerCamelCase     :		Optional[int] 						=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          _lowerCamelCase     :		Optional[int] 						=						image_std if image_std is not None else IMAGENET_STANDARD_STD



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Optional[Any] = PILImageResampling.BICUBIC    ,  _UpperCamelCase     :				Union[str, Any] = None    ,  **_UpperCamelCase     :				List[str]    ,  )				->np.ndarray:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						get_size_dict(A__    ,  default_to_square=A__)
          if "shortest_edge" not in size:
              raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
          _lowerCamelCase     :		Tuple 						=						get_resize_output_image_size(A__    ,  size=size["""shortest_edge"""]    ,  default_to_square=A__)
          return resize(A__    ,  size=A__    ,  resample=A__    ,  data_format=A__    ,  **A__)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				Dict = None    ,  **_UpperCamelCase     :				Optional[Any]    ,  )				->np.ndarray:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						get_size_dict(A__)
          return center_crop(A__    ,  size=(size["""height"""], size["""width"""])    ,  data_format=A__    ,  **A__)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				List[str] = None    ,  **_UpperCamelCase     :				Any)				->np.ndarray:


          """simple docstring"""


          return rescale(A__    ,  scale=A__    ,  data_format=A__    ,  **A__)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Any = None    ,  **_UpperCamelCase     :				Optional[int]    ,  )				->np.ndarray:


          """simple docstring"""


          return normalize(A__    ,  mean=A__    ,  std=A__    ,  data_format=A__    ,  **A__)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				List[str] = None    ,  _UpperCamelCase     :				Tuple = None    ,  _UpperCamelCase     :				Optional[Any] = None    ,  _UpperCamelCase     :				int = None    ,  _UpperCamelCase     :				Optional[int] = None    ,  _UpperCamelCase     :				Optional[Any] = None    ,  _UpperCamelCase     :				Optional[int] = None    ,  _UpperCamelCase     :				List[str] = None    ,  _UpperCamelCase     :				List[Any] = None    ,  _UpperCamelCase     :				str = None    ,  _UpperCamelCase     :				Union[str, Any] = None    ,  _UpperCamelCase     :				Optional[Any] = ChannelDimension.FIRST    ,  **_UpperCamelCase     :				Dict    ,  )				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						do_resize if do_resize is not None else self.do_resize
          _lowerCamelCase     :		Union[str, Any] 						=						size if size is not None else self.size
          _lowerCamelCase     :		List[Any] 						=						get_size_dict(A__    ,  default_to_square=A__)
          _lowerCamelCase     :		Tuple 						=						resample if resample is not None else self.resample
          _lowerCamelCase     :		Dict 						=						do_center_crop if do_center_crop is not None else self.do_center_crop
          _lowerCamelCase     :		Dict 						=						crop_size if crop_size is not None else self.crop_size
          _lowerCamelCase     :		Optional[int] 						=						get_size_dict(A__)
          _lowerCamelCase     :		List[str] 						=						do_rescale if do_rescale is not None else self.do_rescale
          _lowerCamelCase     :		int 						=						rescale_factor if rescale_factor is not None else self.rescale_factor
          _lowerCamelCase     :		Tuple 						=						do_normalize if do_normalize is not None else self.do_normalize
          _lowerCamelCase     :		Tuple 						=						image_mean if image_mean is not None else self.image_mean
          _lowerCamelCase     :		Tuple 						=						image_std if image_std is not None else self.image_std

          _lowerCamelCase     :		Optional[Any] 						=						make_list_of_images(A__)

          if not valid_images(A__):
              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:
              raise ValueError("""Size 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.
          _lowerCamelCase     :		Optional[Any] 						=						[to_numpy_array(A__) for image in images]

          if do_resize:
              _lowerCamelCase     :		Dict 						=						[self.resize(image=A__    ,  size=A__    ,  resample=A__) for image in images]

          if do_center_crop:
              _lowerCamelCase     :		Tuple 						=						[self.center_crop(image=A__    ,  size=A__) for image in images]

          if do_rescale:
              _lowerCamelCase     :		Any 						=						[self.rescale(image=A__    ,  scale=A__) for image in images]

          if do_normalize:
              _lowerCamelCase     :		Optional[int] 						=						[self.normalize(image=A__    ,  mean=A__    ,  std=A__) for image in images]

          _lowerCamelCase     :		Optional[int] 						=						[to_channel_dimension_format(A__    ,  A__) for image in images]

          _lowerCamelCase     :		Dict 						=						{"""pixel_values""": images}
          return BatchFeature(data=A__    ,  tensor_type=A__)





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'''
    _lowerCamelCase     :		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=__A   , default=1   , help="""Number of TPU cores to use (1 or 8)."""	)

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

    return parser.parse_args()







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						parse_args()

    # Import training_script as a module.
    _lowerCamelCase     :		List[Any] 						=						Path(args.training_script	)
    sys.path.append(str(script_fpath.parent.resolve()	)	)
    _lowerCamelCase     :		Optional[Any] 						=						script_fpath.stem
    _lowerCamelCase     :		Dict 						=						importlib.import_module(__A	)

    # Patch sys.argv
    _lowerCamelCase     :		Union[str, Any] 						=						[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()




