


'''simple docstring'''
from collections.abc import Sequence
from queue import Queue






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      Tuple ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      Dict=None ,  lowerCAmelCase							:      Optional[int]=None	):
													A_			    =		start
													A_			    =		end
													A_			    =		val
													A_			    =		(start + end) // 2
													A_			    =		left
													A_			    =		right






						def __repr__(  self							:      Dict	):
													return F"SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      int ,  lowerCAmelCase							:      Sequence ,  lowerCAmelCase							:      Optional[Any]	):
													A_			    =		collection
													A_			    =		function
													if self.collection:
																				A_			    =		self._build_tree(0 ,  len(lowerCAmelCase	) - 1	)






						def 				_UpperCAmelCase       (  self							:      Union[str, Any] ,  lowerCAmelCase							:      List[Any] ,  lowerCAmelCase							:      Union[str, Any]	):
													self._update_tree(self.root ,  lowerCAmelCase ,  lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      Dict ,  lowerCAmelCase							:      Any	):
													return self._query_range(self.root ,  lowerCAmelCase ,  lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      List[str] ,  lowerCAmelCase							:      Optional[Any]	):
													if start == end:
																				return SegmentTreeNode(lowerCAmelCase ,  lowerCAmelCase ,  self.collection[start]	)
													A_			    =		(start + end) // 2
													A_			    =		self._build_tree(lowerCAmelCase ,  lowerCAmelCase	)
													A_			    =		self._build_tree(mid + 1 ,  lowerCAmelCase	)
													return SegmentTreeNode(lowerCAmelCase ,  lowerCAmelCase ,  self.fn(left.val ,  right.val	) ,  lowerCAmelCase ,  lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      Dict	):
													if node.start == i and node.end == i:
																				A_			    =		val
																				return
													if i <= node.mid:
																				self._update_tree(node.left ,  lowerCAmelCase ,  lowerCAmelCase	)
													else:
																				self._update_tree(node.right ,  lowerCAmelCase ,  lowerCAmelCase	)
													A_			    =		self.fn(node.left.val ,  node.right.val	)






						def 				_UpperCAmelCase       (  self							:      Union[str, Any] ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      Dict ,  lowerCAmelCase							:      List[Any]	):
													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							:      Optional[int]	):
													if self.root is not None:
																				A_			    =		Queue()
																				queue.put(self.root	)
																				while not queue.empty():
																											A_			    =		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)
														__SCREAMING_SNAKE_CASE					:				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 Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


__SCREAMING_SNAKE_CASE					:				List[str]									=		logging.get_logger(__name__)






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Union[str, Any]   						=["pixel_values"]






						def __init__(  self							:      Optional[int] ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Union[int, float] = 1 / 2_55 ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      bool = True ,  **lowerCAmelCase							:      List[Any] ,  ):
													super().__init__(**lowerCAmelCase	)
													A_			    =		size if size is not None else {"height": 3_84, "width": 3_84}
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)

													A_			    =		do_resize
													A_			    =		size
													A_			    =		resample
													A_			    =		do_rescale
													A_			    =		rescale_factor
													A_			    =		do_normalize
													A_			    =		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													A_			    =		image_std if image_std is not None else OPENAI_CLIP_STD
													A_			    =		do_convert_rgb






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Dict[str, int] ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Tuple ,  ):
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)
													if "height" not in size or "width" not in size:
																				raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"	)
													A_			    =		(size["height"], size["width"])
													return resize(lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[int, float] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      str ,  ):
													return rescale(lowerCAmelCase ,  scale=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[float, List[float]] ,  lowerCAmelCase							:      Union[float, List[float]] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Dict ,  ):
													return normalize(lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      ImageInput ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[Dict[str, int]] = None ,  lowerCAmelCase							:      PILImageResampling = None ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[float] = None ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[str, TensorType]] = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		do_resize if do_resize is not None else self.do_resize
													A_			    =		resample if resample is not None else self.resample
													A_			    =		do_rescale if do_rescale is not None else self.do_rescale
													A_			    =		rescale_factor if rescale_factor is not None else self.rescale_factor
													A_			    =		do_normalize if do_normalize is not None else self.do_normalize
													A_			    =		image_mean if image_mean is not None else self.image_mean
													A_			    =		image_std if image_std is not None else self.image_std
													A_			    =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													A_			    =		size if size is not None else self.size
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)

													A_			    =		make_list_of_images(lowerCAmelCase	)

													if not valid_images(lowerCAmelCase	):
																				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 or resample is None:
																				raise ValueError("Size and resample must be specified if do_resize 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."	)

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																				A_			    =		[convert_to_rgb(lowerCAmelCase	) for image in images]

													# All transformations expect numpy arrays.
													A_			    =		[to_numpy_array(lowerCAmelCase	) for image in images]

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

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

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

													A_			    =		[to_channel_dimension_format(lowerCAmelCase ,  lowerCAmelCase	) for image in images]

													A_			    =		BatchFeature(data={"pixel_values": images} ,  tensor_type=lowerCAmelCase	)

													return encoded_outputs





