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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


__lowerCamelCase					:	Optional[int]       =  logging.get_logger(__name__)




def       _snake_case	(   lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    b.T
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    np.sum(np.square(lowerCAmelCase	)   , axis=1	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    np.sum(np.square(lowerCAmelCase	)   , axis=0	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    np.matmul(lowerCAmelCase   , lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    aa[:, None] - 2 * ab + ba[None, :]
		return d




def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    x.reshape(-1   , 3	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    squared_euclidean_distance(lowerCAmelCase   , lowerCAmelCase	)
		return np.argmin(lowerCAmelCase   , axis=1	)







class 		a__						(					A__					):
					A	       =							['pixel_values']

					def __init__(    self      :       Any,_A      :       Optional[Union[List[List[int]], np.ndarray]] = None,_A      :       bool = True,_A      :       Dict[str, int] = None,_A      :       PILImageResampling = PILImageResampling.BILINEAR,_A      :       bool = True,_A      :       bool = True,**_A      :       int,):







							"""simple docstring"""






							super().__init__(**_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    size if size is not None else {"height": 256, "width": 256}
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    get_size_dict(_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    np.array(_A    ) if clusters is not None else None
							SCREAMING_SNAKE_CASE_  :		int				      =    do_resize
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    size
							SCREAMING_SNAKE_CASE_  :		Dict				      =    resample
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    do_normalize
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    do_color_quantize

					def 				__UpperCamelCase   (    self      :       Any,_A      :       np.ndarray,_A      :       Dict[str, int],_A      :       PILImageResampling = PILImageResampling.BILINEAR,_A      :       Optional[Union[str, ChannelDimension]] = None,**_A      :       Union[str, Any],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    get_size_dict(_A    )
							if "height" not in size or "width" not in size:
									raise ValueError(F'Size dictionary must contain both height and width keys. Got {size.keys()}'    )
							return resize(
							    _A,size=(size["height"], size["width"]),resample=_A,data_format=_A,**_A    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       np.ndarray,_A      :       Optional[Union[str, ChannelDimension]] = None,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    rescale(image=_A,scale=1 / 127.5,data_format=_A    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    image - 1
							return image






					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       ImageInput,_A      :       bool = None,_A      :       Dict[str, int] = None,_A      :       PILImageResampling = None,_A      :       bool = None,_A      :       Optional[bool] = None,_A      :       Optional[Union[List[List[int]], np.ndarray]] = None,_A      :       Optional[Union[str, TensorType]] = None,_A      :       Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST,**_A      :       List[str],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    do_resize if do_resize is not None else self.do_resize
							SCREAMING_SNAKE_CASE_  :		str				      =    size if size is not None else self.size
							SCREAMING_SNAKE_CASE_  :		Dict				      =    get_size_dict(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    resample if resample is not None else self.resample
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    do_normalize if do_normalize is not None else self.do_normalize
							SCREAMING_SNAKE_CASE_  :		int				      =    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
							SCREAMING_SNAKE_CASE_  :		int				      =    clusters if clusters is not None else self.clusters
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    np.array(_A    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    make_list_of_images(_A    )

							if not valid_images(_A    ):
									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_color_quantize and clusters is None:
									raise ValueError("Clusters must be specified if do_color_quantize is True."    )

							# All transformations expect numpy arrays.
							SCREAMING_SNAKE_CASE_  :		int				      =    [to_numpy_array(_A    ) for image in images]

							if do_resize:
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [self.resize(image=_A,size=_A,resample=_A    ) for image in images]

							if do_normalize:
									SCREAMING_SNAKE_CASE_  :		int				      =    [self.normalize(image=_A    ) for image in images]

							if do_color_quantize:
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [to_channel_dimension_format(_A,ChannelDimension.LAST    ) for image in images]
									# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
									SCREAMING_SNAKE_CASE_  :		Dict				      =    np.array(_A    )
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    color_quantize(_A,_A    ).reshape(images.shape[:-1]    )

									# flatten to (batch_size, height*width)
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    images.shape[0]
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    images.reshape(_A,-1    )

									# We need to convert back to a list of images to keep consistent behaviour across processors.
									SCREAMING_SNAKE_CASE_  :		str				      =    list(_A    )
							else:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [to_channel_dimension_format(_A,_A    ) for image in images]

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    {"input_ids": images}
							return BatchFeature(data=_A,tensor_type=_A    )

from collections.abc import Sequence
from queue import Queue






class 		a__						:

					def __init__(    self      :       int,_A      :       List[Any],_A      :       Optional[Any],_A      :       Optional[int],_A      :       int=None,_A      :       List[str]=None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    start
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    end
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    val
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    left
							SCREAMING_SNAKE_CASE_  :		str				      =    right






					def __repr__(    self      :       Tuple    ):







							"""simple docstring"""






							return F'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'







class 		a__						:

					def __init__(    self      :       Any,_A      :       Sequence,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    collection
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    function
							if self.collection:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    self._build_tree(0,len(_A    ) - 1    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							self._update_tree(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							return self._query_range(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       Any,_A      :       Optional[int],_A      :       int    ):







							"""simple docstring"""






							if start == end:
									return SegmentTreeNode(_A,_A,self.collection[start]    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self._build_tree(_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self._build_tree(mid + 1,_A    )
							return SegmentTreeNode(_A,_A,self.fn(left.val,right.val    ),_A,_A    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       int,_A      :       Tuple,_A      :       Dict    ):







							"""simple docstring"""






							if node.start == i and node.end == i:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    val
									return
							if i <= node.mid:
									self._update_tree(node.left,_A,_A    )
							else:
									self._update_tree(node.right,_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.fn(node.left.val,node.right.val    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str],_A      :       Optional[int],_A      :       Optional[Any]    ):







							"""simple docstring"""






							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,_A,_A    )
									else:
											# range in left child tree and right child tree
											return self.fn(
											    self._query_range(node.left,_A,node.mid    ),self._query_range(node.right,node.mid + 1,_A    ),)
							else:
									# range in right child tree
									return self._query_range(node.right,_A,_A    )






					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							if self.root is not None:
									SCREAMING_SNAKE_CASE_  :		int				      =    Queue()
									queue.put(self.root    )
									while not queue.empty():
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    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					:	int       =  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()
