
from __future__ import annotations

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

lowerCamelCase  =TypeVar("T")




class 					_lowerCamelCase			(   Generic[T]			):





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> None:




           """simple docstring"""







           UpperCamelCase__     : Any | T											=					None

           UpperCamelCase__     : int											=					len(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : list[T]											=					[any_type for _ in range(self.N	)] + arr
           UpperCamelCase__     : Any											=					fnc
           self.build()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> None:




           """simple docstring"""







           for p in range(self.N - 1    ,    0    ,    -1	):
                 UpperCamelCase__     : int											=					self.fn(self.st[p * 2]    ,    self.st[p * 2 + 1]	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> None:




           """simple docstring"""







           p += self.N
           UpperCamelCase__     : Optional[Any]											=					v
           while p > 1:
                 UpperCamelCase__     : Union[str, Any]											=					p // 2
                 UpperCamelCase__     : Any											=					self.fn(self.st[p * 2]    ,    self.st[p * 2 + 1]	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> T | None:  # noqa: E741




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : Tuple											=					l + self.N, r + self.N

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


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

    lowerCamelCase  =[1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

    lowerCamelCase  ={
        0: 7,
        1: 2,
        2: 6,
        3: -1_4,
        4: 5,
        5: 4,
        6: 7,
        7: -1_0,
        8: 9,
        9: 1_0,
        1_0: 1_2,
        1_1: 1,
    }

    lowerCamelCase  =SegmentTree(test_array, min)
    lowerCamelCase  =SegmentTree(test_array, max)
    lowerCamelCase  =SegmentTree(test_array, lambda a, b: a + b)


    def 				SCREAMING_SNAKE_CASE_   (      ):
          for i in range(len(UpperCamelCase__		)		):
                for j in range(UpperCamelCase__			,		len(UpperCamelCase__		)		):
                      UpperCamelCase__     : Optional[Any]											=					reduce(UpperCamelCase__			,		test_array[i : j + 1]		)
                      UpperCamelCase__     : Optional[Any]											=					reduce(UpperCamelCase__			,		test_array[i : j + 1]		)
                      UpperCamelCase__     : Union[str, Any]											=					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():
        lowerCamelCase  =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 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


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''pixel_values''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 3_2    ,    __SCREAMING_SNAKE_CASE=PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize
           UpperCamelCase__     : Tuple											=					do_rescale
           UpperCamelCase__     : Dict											=					size_divisor
           UpperCamelCase__     : str											=					resample
           super().__init__(**__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







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






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




           """simple docstring"""







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






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




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__     : List[Any]											=					do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__     : Union[str, Any]											=					size_divisor if size_divisor is not None else self.size_divisor
           UpperCamelCase__     : List[str]											=					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__     : List[Any]											=					make_list_of_images(__SCREAMING_SNAKE_CASE	)

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

           # All transformations expect numpy arrays.
           UpperCamelCase__     : Dict											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for img in images]

           if do_resize:
                 UpperCamelCase__     : Optional[int]											=					[self.resize(__SCREAMING_SNAKE_CASE    ,    size_divisor=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE	) for image in images]

           if do_rescale:
                 UpperCamelCase__     : Union[str, Any]											=					[self.rescale(__SCREAMING_SNAKE_CASE    ,    scale=1 / 2_5_5	) for image in images]

           UpperCamelCase__     : List[str]											=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) for image in images]

           UpperCamelCase__     : Optional[Any]											=					{'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,    tensor_type=__SCREAMING_SNAKE_CASE	)


