





'''simple docstring'''



import os
from typing import Dict, List, Tuple, TypeVar, Union


_SCREAMING_SNAKE_CASE					=    TypeVar('''T''')

_SCREAMING_SNAKE_CASE					=    Union[List[T], Tuple[T, ...]]
_SCREAMING_SNAKE_CASE					=    Union[T, List[T], Dict[str, T]]
_SCREAMING_SNAKE_CASE					=    Union[str, bytes, os.PathLike]










'''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


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				int											=       ["pixel_values"]


	def __init__(self     ,_lowerCamelCase = True     ,_lowerCamelCase = 32     ,_lowerCamelCase=PILImageResampling.BILINEAR     ,_lowerCamelCase = True     ,**_lowerCamelCase     ,)							->							None:







				'''simple docstring'''





				__lowercase										=							do_resize
				__lowercase										=							do_rescale
				__lowercase										=							size_divisor
				__lowercase										=							resample
				super().__init__(**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase )							->							np.ndarray:







				'''simple docstring'''





				__lowercase						,		__lowercase										=							get_image_size(_lowerCamelCase )
				# Rounds the height and width down to the closest multiple of size_divisor
				__lowercase										=							height // size_divisor * size_divisor
				__lowercase										=							width // size_divisor * size_divisor
				__lowercase										=							resize(_lowerCamelCase     ,(new_h, new_w)     ,resample=_lowerCamelCase     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )
				return image


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase )							->							np.ndarray:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase=None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = ChannelDimension.FIRST     ,**_lowerCamelCase     ,)							->							BatchFeature:







				'''simple docstring'''





				__lowercase										=							do_resize if do_resize is not None else self.do_resize
				__lowercase										=							do_rescale if do_rescale is not None else self.do_rescale
				__lowercase										=							size_divisor if size_divisor is not None else self.size_divisor
				__lowercase										=							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''' )

				__lowercase										=							make_list_of_images(_lowerCamelCase )

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

				# All transformations expect numpy arrays.
				__lowercase										=							[to_numpy_array(_lowerCamelCase ) for img in images]

				if do_resize:
							__lowercase										=							[self.resize(_lowerCamelCase     ,size_divisor=_lowerCamelCase     ,resample=_lowerCamelCase ) for image in images]

				if do_rescale:
							__lowercase										=							[self.rescale(_lowerCamelCase     ,scale=1 / 255 ) for image in images]

				__lowercase										=							[to_channel_dimension_format(_lowerCamelCase     ,_lowerCamelCase ) for image in images]

				__lowercase										=							{'''pixel_values''': images}
				return BatchFeature(data=_lowerCamelCase     ,tensor_type=_lowerCamelCase )





