






from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

__snake_case      :      Optional[Any]             =       TypeVar("""T""")


class       __SCREAMING_SNAKE_CASE   (				Generic[T]):






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  None

												lowerCAmelCase__        =  len(_UpperCamelCase				)
												lowerCAmelCase__        =  [any_type for _ in range(self.N				)] + arr
												lowerCAmelCase__        =  fnc
												self.build()






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												for p in range(self.N - 1       ,  0       ,  -1				):
																		lowerCAmelCase__        =  self.fn(self.st[p * 2]       ,  self.st[p * 2 + 1]				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												p += self.N
												lowerCAmelCase__        =  v
												while p > 1:
																		lowerCAmelCase__        =  p // 2
																		lowerCAmelCase__        =  self.fn(self.st[p * 2]       ,  self.st[p * 2 + 1]				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):  # noqa: E741






												"""simple docstring"""
												lowerCAmelCase__  ,    lowerCAmelCase__        =  l + self.N, r + self.N

												lowerCAmelCase__        =  None
												while l <= r:
																		if l % 2 == 1:
																								lowerCAmelCase__        =  self.st[l] if res is None else self.fn(_UpperCamelCase       ,  self.st[l]				)
																		if r % 2 == 0:
																								lowerCAmelCase__        =  self.st[r] if res is None else self.fn(_UpperCamelCase       ,  self.st[r]				)
																		lowerCAmelCase__  ,    lowerCAmelCase__        =  (l + 1) // 2, (r - 1) // 2
												return res


if __name__ == "__main__":
					from functools import reduce

					__snake_case      :      Any             =       [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

					__snake_case      :      Optional[int]             =       {
					    0: 7,
					    1: 2,
					    2: 6,
					    3: -14,
					    4: 5,
					    5: 4,
					    6: 7,
					    7: -10,
					    8: 9,
					    9: 10,
					    10: 12,
					    11: 1,
					}

					__snake_case      :      Optional[int]             =       SegmentTree(test_array, min)
					__snake_case      :      Dict             =       SegmentTree(test_array, max)
					__snake_case      :      Union[str, Any]             =       SegmentTree(test_array, lambda a, b: a + b)







					def 		_UpperCamelCase				(      )					->				None:

											"""simple docstring"""



											for i in range(len(UpperCamelCase_						)						):
																	for j in range(UpperCamelCase_      ,    len(UpperCamelCase_						)						):
																							lowerCAmelCase__        =  reduce(UpperCamelCase_      ,    test_array[i : j + 1]						)
																							lowerCAmelCase__        =  reduce(UpperCamelCase_      ,    test_array[i : j + 1]						)
																							lowerCAmelCase__        =  reduce(lambda UpperCamelCase_      ,    UpperCamelCase_						: a + b      ,    test_array[i : j + 1]						)
																							assert min_range == min_segment_tree.query(UpperCamelCase_      ,    UpperCamelCase_						)
																							assert max_range == max_segment_tree.query(UpperCamelCase_      ,    UpperCamelCase_						)
																							assert sum_range == sum_segment_tree.query(UpperCamelCase_      ,    UpperCamelCase_						)

					test_all_segments()

					for index, value in test_updates.items():
										__snake_case      :      List[str]             =       value
										min_segment_tree.update(index, value)
										max_segment_tree.update(index, value)
										sum_segment_tree.update(index, value)
										test_all_segments()













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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case      :      Any             =       logging.get_logger(__name__)


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   ['''pixel_values''']






						def __init__(		self       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = PILImageResampling.BILINEAR       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = True       ,  _UpperCamelCase = 1 / 2_55       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												super().__init__(**_UpperCamelCase				)
												lowerCAmelCase__        =  size if size is not None else {'shortest_edge': 2_56}
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												lowerCAmelCase__        =  crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												lowerCAmelCase__        =  do_resize
												lowerCAmelCase__        =  size
												lowerCAmelCase__        =  resample
												lowerCAmelCase__        =  do_center_crop
												lowerCAmelCase__        =  crop_size
												lowerCAmelCase__        =  do_rescale
												lowerCAmelCase__        =  rescale_factor
												lowerCAmelCase__        =  do_normalize
												lowerCAmelCase__        =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												lowerCAmelCase__        =  image_std if image_std is not None else IMAGENET_STANDARD_STD






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = PILImageResampling.BICUBIC       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												if "shortest_edge" not in size:
																		raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"				)
												lowerCAmelCase__        =  get_resize_output_image_size(_UpperCamelCase       ,  size=size['shortest_edge']       ,  default_to_square=_UpperCamelCase				)
												return resize(_UpperCamelCase       ,  size=_UpperCamelCase       ,  resample=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												return center_crop(_UpperCamelCase       ,  size=(size['height'], size['width'])       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase				):






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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






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







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = ChannelDimension.FIRST       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  do_resize if do_resize is not None else self.do_resize
												lowerCAmelCase__        =  size if size is not None else self.size
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												lowerCAmelCase__        =  resample if resample is not None else self.resample
												lowerCAmelCase__        =  do_center_crop if do_center_crop is not None else self.do_center_crop
												lowerCAmelCase__        =  crop_size if crop_size is not None else self.crop_size
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												lowerCAmelCase__        =  do_rescale if do_rescale is not None else self.do_rescale
												lowerCAmelCase__        =  rescale_factor if rescale_factor is not None else self.rescale_factor
												lowerCAmelCase__        =  do_normalize if do_normalize is not None else self.do_normalize
												lowerCAmelCase__        =  image_mean if image_mean is not None else self.image_mean
												lowerCAmelCase__        =  image_std if image_std is not None else self.image_std

												lowerCAmelCase__        =  make_list_of_images(_UpperCamelCase				)

												if not valid_images(_UpperCamelCase				):
																		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.
												lowerCAmelCase__        =  [to_numpy_array(_UpperCamelCase				) for image in images]

												if do_resize:
																		lowerCAmelCase__        =  [self.resize(image=_UpperCamelCase       ,  size=_UpperCamelCase       ,  resample=_UpperCamelCase				) for image in images]

												if do_center_crop:
																		lowerCAmelCase__        =  [self.center_crop(image=_UpperCamelCase       ,  size=_UpperCamelCase				) for image in images]

												if do_rescale:
																		lowerCAmelCase__        =  [self.rescale(image=_UpperCamelCase       ,  scale=_UpperCamelCase				) for image in images]

												if do_normalize:
																		lowerCAmelCase__        =  [self.normalize(image=_UpperCamelCase       ,  mean=_UpperCamelCase       ,  std=_UpperCamelCase				) for image in images]

												lowerCAmelCase__        =  [to_channel_dimension_format(_UpperCamelCase       ,  _UpperCamelCase				) for image in images]

												lowerCAmelCase__        =  {'pixel_values': images}
												return BatchFeature(data=_UpperCamelCase       ,  tensor_type=_UpperCamelCase				)





