





'''simple docstring'''







from ..utils import DummyObject, requires_backends


class 					__a      (metaclass=a__     ):
							__a					:   Optional[Any]									=       ["""transformers""", """torch""", """note_seq"""]

							def __init__(							self							:     Tuple       ,     *__magic_name__							:     int       ,     **__magic_name__							:     Dict   )					->       Union[str, Any]:







													"""simple docstring"""
													requires_backends(self       ,     ['''transformers''', '''torch''', '''note_seq''']   )

							@classmethod
							def     UpperCAmelCase__	(							cls							:     Dict       ,     *__magic_name__							:     Optional[Any]       ,     **__magic_name__							:     List[Any]   )					->       Any:







													"""simple docstring"""
													requires_backends(cls       ,     ['''transformers''', '''torch''', '''note_seq''']   )



							@classmethod
							def     UpperCAmelCase__	(							cls							:     List[Any]       ,     *__magic_name__							:     str       ,     **__magic_name__							:     List[str]   )					->       int:







													"""simple docstring"""
													requires_backends(cls       ,     ['''transformers''', '''torch''', '''note_seq''']   )












'''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


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


class 					__a      (lowerCamelCase     ):
							__a					:   Tuple									=       ["pixel_values"]

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     int = 32       ,     __magic_name__							:     Union[str, Any]=PILImageResampling.BILINEAR       ,     __magic_name__							:     bool = True       ,     **__magic_name__							:     List[str]       ,     )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  do_resize
													UpperCAmelCase_			:      Tuple     						=  do_rescale
													UpperCAmelCase_			:      List[Any]     						=  size_divisor
													UpperCAmelCase_			:      Any     						=  resample
													super().__init__(**__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     int       ,     __magic_name__							:     str       ,     __magic_name__							:     Optional[ChannelDimension] = None       ,     **__magic_name__							:     Tuple   )					->       np.ndarray:







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

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     float       ,     __magic_name__							:     Optional[ChannelDimension] = None       ,     **__magic_name__							:     Optional[Any]   )					->       np.ndarray:







													"""simple docstring"""
													return rescale(image=__magic_name__       ,     scale=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )



							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[int] = None       ,     __magic_name__							:     Any=None       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[Union[TensorType, str]] = None       ,     __magic_name__							:     ChannelDimension = ChannelDimension.FIRST       ,     **__magic_name__							:     Tuple       ,     )					->       BatchFeature:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_			:      str     						=  do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_			:      Any     						=  size_divisor if size_divisor is not None else self.size_divisor
													UpperCAmelCase_			:      Dict     						=  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'''   )

													UpperCAmelCase_			:      Optional[int]     						=  make_list_of_images(__magic_name__   )

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

													# All transformations expect numpy arrays.
													UpperCAmelCase_			:      List[str]     						=  [to_numpy_array(__magic_name__   ) for img in images]

													if do_resize:
																			UpperCAmelCase_			:      str     						=  [self.resize(__magic_name__       ,     size_divisor=__magic_name__       ,     resample=__magic_name__   ) for image in images]

													if do_rescale:
																			UpperCAmelCase_			:      Tuple     						=  [self.rescale(__magic_name__       ,     scale=1 / 2_55   ) for image in images]

													UpperCAmelCase_			:      Union[str, Any]     						=  [to_channel_dimension_format(__magic_name__       ,     __magic_name__   ) for image in images]

													UpperCAmelCase_			:      int     						=  {'''pixel_values''': images}
													return BatchFeature(data=__magic_name__       ,     tensor_type=__magic_name__   )





