



"""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,
    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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


snake_case_    					=	logging.get_logger(__name__)

def 			_lowerCAmelCase				(       lowercase_						):






						if isinstance(lowercase_		,     (list, tuple)						) and isinstance(videos[0]		,     (list, tuple)						) and is_valid_image(videos[0][0]						):
												return videos

						elif isinstance(lowercase_		,     (list, tuple)						) and is_valid_image(videos[0]						):
												return [videos]

						elif is_valid_image(lowercase_						):
												return [[videos]]

						raise ValueError(F"""Could not make batched video from {videos}"""						)







class 				A_      (					lowercase_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							["pixel_values"]






						def __init__( self			:List[Any]       ,  lowercase_			:Tuple = True       ,  lowercase_			:List[str] = None       ,  lowercase_			:List[str] = PILImageResampling.BILINEAR       ,  lowercase_			:Tuple = True       ,  lowercase_			:Union[str, Any] = None       ,  lowercase_			:List[Any] = True       ,  lowercase_			:Optional[int] = 1 / 2_55       ,  lowercase_			:List[Any] = True       ,  lowercase_			:List[Any] = None       ,  lowercase_			:Optional[Any] = None       ,  **lowercase_			:Union[str, Any]       ,  )    ->      None:
												super().__init__(**a__ )
												UpperCAmelCase					=			size if size is not None else {'shortest_edge': 2_24}
												UpperCAmelCase					=			get_size_dict(a__       ,  default_to_square=a__ )
												UpperCAmelCase					=			crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												UpperCAmelCase					=			get_size_dict(a__       ,  param_name='crop_size' )

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






						def      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:str       ,  lowercase_			:str       ,  lowercase_			:int = PILImageResampling.BILINEAR       ,  lowercase_			:Optional[int] = None       ,  **lowercase_			:Optional[int]       ,  )    ->      np.ndarray:
												UpperCAmelCase					=			get_size_dict(a__       ,  default_to_square=a__ )
												if "shortest_edge" in size:
																		UpperCAmelCase					=			get_resize_output_image_size(a__       ,  size['shortest_edge']       ,  default_to_square=a__ )
												elif "height" in size and "width" in size:
																		UpperCAmelCase					=			(size['height'], size['width'])
												else:
																		raise ValueError(f"""Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}""" )
												return resize(a__       ,  size=a__       ,  resample=a__       ,  data_format=a__       ,  **a__ )






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:Tuple       ,  lowercase_			:Dict       ,  lowercase_			:Any = None       ,  **lowercase_			:Dict       ,  )    ->      np.ndarray:
												UpperCAmelCase					=			get_size_dict(a__ )
												if "height" not in size or "width" not in size:
																		raise ValueError(f"""Size must have \'height\' and \'width\' as keys. Got {size.keys()}""" )
												return center_crop(a__       ,  size=(size['height'], size['width'])       ,  data_format=a__       ,  **a__ )






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:Optional[Any]       ,  lowercase_			:List[Any]       ,  lowercase_			:Union[str, Any] = None       ,  **lowercase_			:Any       ,  )    ->      List[str]:
												return rescale(a__       ,  scale=a__       ,  data_format=a__       ,  **a__ )






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:Optional[int]       ,  lowercase_			:Any       ,  lowercase_			:Dict = None       ,  **lowercase_			:Optional[Any]       ,  )    ->      np.ndarray:
												return normalize(a__       ,  mean=a__       ,  std=a__       ,  data_format=a__       ,  **a__ )






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:str       ,  lowercase_			:str = None       ,  lowercase_			:int = None       ,  lowercase_			:Optional[int] = None       ,  lowercase_			:str = None       ,  lowercase_			:Dict = None       ,  lowercase_			:Union[str, Any] = None       ,  lowercase_			:Optional[Any] = None       ,  lowercase_			:List[str] = None       ,  lowercase_			:Any = None       ,  lowercase_			:Any = None       ,  lowercase_			:str = ChannelDimension.FIRST       ,  )    ->      np.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.
												UpperCAmelCase					=			to_numpy_array(a__ )

												if do_resize:
																		UpperCAmelCase					=			self.resize(image=a__       ,  size=a__       ,  resample=a__ )

												if do_center_crop:
																		UpperCAmelCase					=			self.center_crop(a__       ,  size=a__ )

												if do_rescale:
																		UpperCAmelCase					=			self.rescale(image=a__       ,  scale=a__ )

												if do_normalize:
																		UpperCAmelCase					=			self.normalize(image=a__       ,  mean=a__       ,  std=a__ )

												UpperCAmelCase					=			to_channel_dimension_format(a__       ,  a__ )
												return image






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

												UpperCAmelCase					=			size if size is not None else self.size
												UpperCAmelCase					=			get_size_dict(a__       ,  default_to_square=a__ )
												UpperCAmelCase					=			crop_size if crop_size is not None else self.crop_size
												UpperCAmelCase					=			get_size_dict(a__       ,  param_name='crop_size' )

												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.' )

												UpperCAmelCase					=			make_batched(a__ )

												UpperCAmelCase					=			[
												    [
												        self._preprocess_image(
												            image=a__       ,  do_resize=a__       ,  size=a__       ,  resample=a__       ,  do_center_crop=a__       ,  crop_size=a__       ,  do_rescale=a__       ,  rescale_factor=a__       ,  do_normalize=a__       ,  image_mean=a__       ,  image_std=a__       ,  data_format=a__       ,  )
												        for img in video
												    ]
												    for video in videos
												]

												UpperCAmelCase					=			{'pixel_values': videos}
												return BatchFeature(data=a__       ,  tensor_type=a__ )










"""simple docstring"""


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

import torch_xla.distributed.xla_multiprocessing as xmp

def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			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=lowercase_		,     default=1		,     help='Number of TPU cores to use (1 or 8).'						)

						# positional
						parser.add_argument(
						    'training_script'		,     type=lowercase_		,     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=lowercase_						)

						return parser.parse_args()

def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			parse_args()

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

						# Patch sys.argv
						UpperCAmelCase					=			[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()





