





'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A   : Any             =			logging.get_logger(__name__)







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    ["pixel_values"]


					def __init__(					self	:  List[Any]       ,     __snake_case	:  bool = True       ,     __snake_case	:  int = 32       ,     __snake_case	:  str=PILImageResampling.BILINEAR       ,     __snake_case	:  bool = True       ,     **__snake_case	:  List[Any]       ,     ):





											'''simple docstring'''





											_snake_case:   Dict  					=		do_resize
											_snake_case:   List[Any]  					=		do_rescale
											_snake_case:   int  					=		size_divisor
											_snake_case:   Dict  					=		resample
											super().__init__(**__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ,     __snake_case	:  np.ndarray       ,     __snake_case	:  int       ,     __snake_case	:  Tuple       ,     __snake_case	:  Optional[ChannelDimension] = None       ,     **__snake_case	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case					,						_snake_case:   Dict  					=		get_image_size(__snake_case       )
											# Rounds the height and width down to the closest multiple of size_divisor
											_snake_case:   Tuple  					=		height // size_divisor * size_divisor
											_snake_case:   List[Any]  					=		width // size_divisor * size_divisor
											_snake_case:   str  					=		resize(__snake_case       ,     (new_h, new_w)       ,     resample=__snake_case       ,     data_format=__snake_case       ,     **__snake_case       )
											return image


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     __snake_case	:  np.ndarray       ,     __snake_case	:  float       ,     __snake_case	:  Optional[ChannelDimension] = None       ,     **__snake_case	:  Any       ):





											'''simple docstring'''





											return rescale(image=__snake_case       ,     scale=__snake_case       ,     data_format=__snake_case       ,     **__snake_case       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     __snake_case	:  Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,     __snake_case	:  Optional[bool] = None       ,     __snake_case	:  Optional[int] = None       ,     __snake_case	:  Dict=None       ,     __snake_case	:  Optional[bool] = None       ,     __snake_case	:  Optional[Union[TensorType, str]] = None       ,     __snake_case	:  ChannelDimension = ChannelDimension.FIRST       ,     **__snake_case	:  Optional[Any]       ,     ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		do_resize if do_resize is not None else self.do_resize
											_snake_case:   Dict  					=		do_rescale if do_rescale is not None else self.do_rescale
											_snake_case:   Tuple  					=		size_divisor if size_divisor is not None else self.size_divisor
											_snake_case:   Any  					=		resample if resample is not None else self.resample

											if do_resize and size_divisor is None:
																	raise ValueError('size_divisor is required for resizing'       )

											_snake_case:   Optional[Any]  					=		make_list_of_images(__snake_case       )

											if not valid_images(__snake_case       ):
																	raise ValueError('Invalid image(s)'       )

											# All transformations expect numpy arrays.
											_snake_case:   int  					=		[to_numpy_array(__snake_case       ) for img in images]

											if do_resize:
																	_snake_case:   List[Any]  					=		[self.resize(__snake_case       ,     size_divisor=__snake_case       ,     resample=__snake_case       ) for image in images]

											if do_rescale:
																	_snake_case:   List[Any]  					=		[self.rescale(__snake_case       ,     scale=1 / 2_55       ) for image in images]

											_snake_case:   Union[str, Any]  					=		[to_channel_dimension_format(__snake_case       ,     __snake_case       ) for image in images]

											_snake_case:   Any  					=		{'pixel_values': images}
											return BatchFeature(data=__snake_case       ,     tensor_type=__snake_case       )












'''simple docstring'''






import torch





def       lowercase_      (		)   ->Dict:
						if torch.cuda.is_available():
												_snake_case:   Any  					=		torch.cuda.device_count()
						else:
												_snake_case:   Optional[int]  					=		0
						print(F'''Successfully ran on {num_gpus} GPUs'''						)


if __name__ == "__main__":
		main()





