


'''simple docstring'''







from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case_     :       List[Any]						=  logging.get_logger(__name__)


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

							def __init__(							self							:     Optional[Any]       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     Dict[str, int] = None       ,     __magic_name__							:     PILImageResampling = PILImageResampling.BICUBIC       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     Dict[str, int] = None       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     Union[int, float] = 1 / 2_55       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN       ,     __magic_name__							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD       ,     **__magic_name__							:     List[str]       ,     )					->       None:







													"""simple docstring"""
													super().__init__(**__magic_name__   )
													UpperCAmelCase_			:      Any     						=  size if size is not None else {'''shortest_edge''': 2_24}
													UpperCAmelCase_			:      int     						=  get_size_dict(__magic_name__       ,     default_to_square=__magic_name__   )
													UpperCAmelCase_			:      Any     						=  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
													UpperCAmelCase_			:      Tuple     						=  get_size_dict(__magic_name__       ,     param_name='''crop_size'''   )

													UpperCAmelCase_			:      Tuple     						=  do_resize
													UpperCAmelCase_			:      List[str]     						=  size
													UpperCAmelCase_			:      int     						=  resample
													UpperCAmelCase_			:      Optional[int]     						=  do_center_crop
													UpperCAmelCase_			:      Dict     						=  crop_size
													UpperCAmelCase_			:      str     						=  do_rescale
													UpperCAmelCase_			:      List[str]     						=  rescale_factor
													UpperCAmelCase_			:      Tuple     						=  do_normalize
													UpperCAmelCase_			:      List[str]     						=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
													UpperCAmelCase_			:      Any     						=  image_std if image_std is not None else IMAGENET_DEFAULT_STD

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







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  get_size_dict(__magic_name__       ,     default_to_square=__magic_name__   )
													# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
													if "shortest_edge" in size:
																			UpperCAmelCase_			:      Dict     						=  int((2_56 / 2_24) * size['''shortest_edge''']   )
																			UpperCAmelCase_			:      Optional[Any]     						=  get_resize_output_image_size(__magic_name__       ,     size=__magic_name__       ,     default_to_square=__magic_name__   )
																			UpperCAmelCase_			:      Any     						=  {'''height''': output_size[0], '''width''': output_size[1]}
													if "height" not in size_dict or "width" not in size_dict:
																			raise ValueError(
																			    F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""   )
													return resize(
													    __magic_name__       ,     size=(size_dict['''height'''], size_dict['''width'''])       ,     resample=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )

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







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  get_size_dict(__magic_name__   )
													if "height" not in size or "width" not in size:
																			raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""   )
													return center_crop(__magic_name__       ,     size=(size['''height'''], size['''width'''])       ,     data_format=__magic_name__       ,     **__magic_name__   )

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







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

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







													"""simple docstring"""
													return normalize(__magic_name__       ,     mean=__magic_name__       ,     std=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )



							def     UpperCAmelCase__	(							self							:     Dict       ,     __magic_name__							:     ImageInput       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[Dict[str, int]] = None       ,     __magic_name__							:     PILImageResampling = None       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[Dict[str, int]] = None       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[float] = None       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[Union[float, Iterable[float]]] = None       ,     __magic_name__							:     Optional[Union[float, Iterable[float]]] = None       ,     __magic_name__							:     Optional[TensorType] = None       ,     __magic_name__							:     ChannelDimension = ChannelDimension.FIRST       ,     **__magic_name__							:     Any       ,     )					->       BatchFeature:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_			:      List[Any]     						=  resample if resample is not None else self.resample
													UpperCAmelCase_			:      List[str]     						=  do_center_crop if do_center_crop is not None else self.do_center_crop
													UpperCAmelCase_			:      List[Any]     						=  do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_			:      Optional[int]     						=  rescale_factor if rescale_factor is not None else self.rescale_factor
													UpperCAmelCase_			:      Tuple     						=  do_normalize if do_normalize is not None else self.do_normalize
													UpperCAmelCase_			:      str     						=  image_mean if image_mean is not None else self.image_mean
													UpperCAmelCase_			:      Any     						=  image_std if image_std is not None else self.image_std

													UpperCAmelCase_			:      Optional[int]     						=  size if size is not None else self.size
													UpperCAmelCase_			:      List[Any]     						=  get_size_dict(__magic_name__       ,     default_to_square=__magic_name__   )
													UpperCAmelCase_			:      Optional[int]     						=  crop_size if crop_size is not None else self.crop_size
													UpperCAmelCase_			:      int     						=  get_size_dict(__magic_name__       ,     param_name='''crop_size'''   )

													UpperCAmelCase_			:      int     						=  make_list_of_images(__magic_name__   )

													if not valid_images(__magic_name__   ):
																			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.
													UpperCAmelCase_			:      str     						=  [to_numpy_array(__magic_name__   ) for image in images]

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

													if do_center_crop:
																			UpperCAmelCase_			:      Optional[int]     						=  [self.center_crop(__magic_name__       ,     __magic_name__   ) for image in images]

													if do_rescale:
																			UpperCAmelCase_			:      Optional[Any]     						=  [self.rescale(__magic_name__       ,     __magic_name__   ) for image in images]

													if do_normalize:
																			UpperCAmelCase_			:      Union[str, Any]     						=  [self.normalize(__magic_name__       ,     __magic_name__       ,     __magic_name__   ) for image in images]

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

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












'''simple docstring'''







class 					__a      :

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     int   )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  size
													UpperCAmelCase_			:      Tuple     						=  [0] * size
													UpperCAmelCase_			:      Optional[Any]     						=  [0] * size

							@staticmethod
							def     UpperCAmelCase__	(							__magic_name__							:     int   )					->       int:







													"""simple docstring"""
													return index | (index + 1)

							@staticmethod
							def     UpperCAmelCase__	(							__magic_name__							:     int   )					->       int:







													"""simple docstring"""
													return (index & (index + 1)) - 1

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     int       ,     __magic_name__							:     int   )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  value
													while index < self.size:
																			UpperCAmelCase_			:      str     						=  self.get_prev(__magic_name__   ) + 1
																			if current_left_border == index:
																									UpperCAmelCase_			:      List[str]     						=  value
																			else:
																									UpperCAmelCase_			:      Optional[int]     						=  max(__magic_name__       ,     __magic_name__       ,     __magic_name__   )
																			UpperCAmelCase_			:      Tuple     						=  self.get_next(__magic_name__   )



							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     int       ,     __magic_name__							:     int   )					->       int:







													"""simple docstring"""
													right -= 1  # Because of right is exclusive
													UpperCAmelCase_			:      List[str]     						=  0
													while left <= right:
																			UpperCAmelCase_			:      Optional[Any]     						=  self.get_prev(__magic_name__   )
																			if left <= current_left:
																									UpperCAmelCase_			:      Dict     						=  max(__magic_name__       ,     self.tree[right]   )
																									UpperCAmelCase_			:      Optional[Any]     						=  current_left
																			else:
																									UpperCAmelCase_			:      str     						=  max(__magic_name__       ,     self.arr[right]   )
																									right -= 1
													return result


if __name__ == "__main__":
					import doctest

					doctest.testmod()





