'''simple docstring'''

from collections.abc import Sequence
from queue import Queue





class 							SCREAMING_SNAKE_CASE__						:


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Union[str, Any]=None   ,		__lowerCAmelCase				:	int=None)      ->					Union[str, Any]:
         lowercase_     =      start
         lowercase_     =      end
         lowercase_     =      val
         lowercase_     =      (start + end) // 2
         lowercase_     =      left
         lowercase_     =      right


   def __repr__(   self				:	str)      ->					int:
         return F'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'





class 							SCREAMING_SNAKE_CASE__						:


   def __init__(   self				:	Dict   ,		__lowerCAmelCase				:	Sequence   ,		__lowerCAmelCase				:	Dict)      ->					Tuple:
         lowercase_     =      collection
         lowercase_     =      function
         if self.collection:
               lowercase_     =      self._build_tree(0   ,		len(__lowerCAmelCase) - 1)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	Optional[int])      ->					Tuple:
         self._update_tree(self.root   ,		__lowerCAmelCase   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Optional[Any])      ->					Any:
         return self._query_range(self.root   ,		__lowerCAmelCase   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	Tuple)      ->					Tuple:
         if start == end:
               return SegmentTreeNode(__lowerCAmelCase   ,		__lowerCAmelCase   ,		self.collection[start])
         lowercase_     =      (start + end) // 2
         lowercase_     =      self._build_tree(__lowerCAmelCase   ,		__lowerCAmelCase)
         lowercase_     =      self._build_tree(mid + 1   ,		__lowerCAmelCase)
         return SegmentTreeNode(__lowerCAmelCase   ,		__lowerCAmelCase   ,		self.fn(left.val   ,		right.val)   ,		__lowerCAmelCase   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	int)      ->					int:
         if node.start == i and node.end == i:
               lowercase_     =      val
               return
         if i <= node.mid:
               self._update_tree(node.left   ,		__lowerCAmelCase   ,		__lowerCAmelCase)
         else:
               self._update_tree(node.right   ,		__lowerCAmelCase   ,		__lowerCAmelCase)
         lowercase_     =      self.fn(node.left.val   ,		node.right.val)


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	str)      ->					int:
         if node.start == i and node.end == j:
               return node.val

         if i <= node.mid:
               if j <= node.mid:
                     # range in left child tree
                     return self._query_range(node.left   ,		__lowerCAmelCase   ,		__lowerCAmelCase)
               else:
                     # range in left child tree and right child tree
                     return self.fn(
                         self._query_range(node.left   ,		__lowerCAmelCase   ,		node.mid)   ,		self._query_range(node.right   ,		node.mid + 1   ,		__lowerCAmelCase)   ,		)
         else:
               # range in right child tree
               return self._query_range(node.right   ,		__lowerCAmelCase   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Tuple:
         if self.root is not None:
               lowercase_     =      Queue()
               queue.put(self.root)
               while not queue.empty():
                     lowercase_     =      queue.get()
                     yield node

                     if node.left is not None:
                           queue.put(node.left)

                     if node.right is not None:
                           queue.put(node.right)


if __name__ == "__main__":
       import operator

       for fn in [operator.add, max, min]:
              print("*" * 50)
              lowerCAmelCase_ : Optional[Any]						  =      SegmentTree([2, 1, 5, 3, 4], fn)
              for node in arr.traverse():
                     print(node)
              print()

              arr.update(1, 5)
              for node in arr.traverse():
                     print(node)
              print()

              print(arr.query_range(3, 4))  # 7
              print(arr.query_range(2, 2))  # 5
              print(arr.query_range(1, 3))  # 13
              print()







'''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


lowerCAmelCase_ : Any						  =      logging.get_logger(__name__)





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=['pixel_values']


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	int = 32   ,		__lowerCAmelCase				:	List[str]=PILImageResampling.BILINEAR   ,		__lowerCAmelCase				:	bool = True   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					None:
         lowercase_     =      do_resize
         lowercase_     =      do_rescale
         lowercase_     =      size_divisor
         lowercase_     =      resample
         super().__init__(**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Optional[ChannelDimension] = None   ,		**__lowerCAmelCase				:	Any)      ->					np.ndarray:
         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				:	str   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	float   ,		__lowerCAmelCase				:	Optional[ChannelDimension] = None   ,		**__lowerCAmelCase				:	int)      ->					np.ndarray:
         return rescale(image=__lowerCAmelCase   ,		scale=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


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






