
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    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_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
						import PIL

if is_torch_available():
						import torch


lowercase__						       =      logging.get_logger(__name__)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						["""pixel_values"""]



		def __init__(      self				:			Optional[Any]  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			Dict[str, int] = None  ,	UpperCamelCase__				:			PILImageResampling = PILImageResampling.BILINEAR  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			Union[int, float] = 1 / 255  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			Dict[str, int] = None  ,	UpperCamelCase__				:			bool = True  ,	**UpperCamelCase__				:			Any  ,	)						->			None:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )
				snake_case  :       str     			=						size if size is not None else {'''shortest_edge''': 224}
				snake_case  :       Optional[Any]     			=						get_size_dict(UpperCamelCase__  ,	default_to_square=UpperCamelCase__      )
				snake_case  :       Any     			=						crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
				snake_case  :       Tuple     			=						get_size_dict(UpperCamelCase__  ,	param_name='''crop_size'''      )

				snake_case  :       List[str]     			=						do_resize
				snake_case  :       Tuple     			=						size
				snake_case  :       Tuple     			=						resample
				snake_case  :       Tuple     			=						do_rescale
				snake_case  :       List[Any]     			=						rescale_factor
				snake_case  :       List[Any]     			=						do_center_crop
				snake_case  :       Dict     			=						crop_size
				snake_case  :       int     			=						do_flip_channel_order



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			np.ndarray  ,	UpperCamelCase__				:			Dict[str, int]  ,	UpperCamelCase__				:			PILImageResampling = PIL.Image.BILINEAR  ,	UpperCamelCase__				:			Optional[Union[str, ChannelDimension]] = None  ,	**UpperCamelCase__				:			int  ,	)						->			np.ndarray:






				"""simple docstring"""







				snake_case  :       List[str]     			=						get_size_dict(UpperCamelCase__  ,	default_to_square=UpperCamelCase__      )
				if "shortest_edge" not in size:
						raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'      )
				snake_case  :       List[str]     			=						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       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			np.ndarray  ,	UpperCamelCase__				:			Dict[str, int]  ,	UpperCamelCase__				:			Optional[Union[str, ChannelDimension]] = None  ,	**UpperCamelCase__				:			Optional[int]  ,	)						->			np.ndarray:






				"""simple docstring"""







				snake_case  :       List[str]     			=						get_size_dict(UpperCamelCase__      )
				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()}'      )
				return center_crop(UpperCamelCase__  ,	size=(size['''height'''], size['''width'''])  ,	data_format=UpperCamelCase__  ,	**UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			np.ndarray  ,	UpperCamelCase__				:			Union[int, float]  ,	UpperCamelCase__				:			Optional[Union[str, ChannelDimension]] = None  ,	**UpperCamelCase__				:			int  ,	)						->			Tuple:






				"""simple docstring"""







				return rescale(UpperCamelCase__  ,	scale=UpperCamelCase__  ,	data_format=UpperCamelCase__  ,	**UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			np.ndarray  ,	UpperCamelCase__				:			Optional[Union[str, ChannelDimension]] = None      )						->			np.ndarray:






				"""simple docstring"""







				return flip_channel_order(UpperCamelCase__  ,	data_format=UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			ImageInput  ,	UpperCamelCase__				:			bool = None  ,	UpperCamelCase__				:			Dict[str, int] = None  ,	UpperCamelCase__				:			PILImageResampling = None  ,	UpperCamelCase__				:			bool = None  ,	UpperCamelCase__				:			float = None  ,	UpperCamelCase__				:			bool = None  ,	UpperCamelCase__				:			Dict[str, int] = None  ,	UpperCamelCase__				:			bool = None  ,	UpperCamelCase__				:			Optional[Union[str, TensorType]] = None  ,	UpperCamelCase__				:			ChannelDimension = ChannelDimension.FIRST  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			PIL.Image.Image:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						do_resize if do_resize is not None else self.do_resize
				snake_case  :       Optional[int]     			=						resample if resample is not None else self.resample
				snake_case  :       Union[str, Any]     			=						do_rescale if do_rescale is not None else self.do_rescale
				snake_case  :       int     			=						rescale_factor if rescale_factor is not None else self.rescale_factor
				snake_case  :       int     			=						do_center_crop if do_center_crop is not None else self.do_center_crop
				snake_case  :       Optional[Any]     			=						(
				    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
				)

				snake_case  :       Dict     			=						size if size is not None else self.size
				snake_case  :       Optional[Any]     			=						get_size_dict(UpperCamelCase__  ,	default_to_square=UpperCamelCase__      )
				snake_case  :       Any     			=						crop_size if crop_size is not None else self.crop_size
				snake_case  :       List[Any]     			=						get_size_dict(UpperCamelCase__  ,	param_name='''crop_size'''      )

				snake_case  :       List[Any]     			=						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_rescale and rescale_factor is None:
						raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

				if do_center_crop and crop_size is None:
						raise ValueError('''Crop size must be specified if do_center_crop is True.'''      )

				# All transformations expect numpy arrays.
				snake_case  :       Union[str, Any]     			=						[to_numpy_array(UpperCamelCase__      ) for image in images]

				if do_resize:
						snake_case  :       Tuple     			=						[self.resize(image=UpperCamelCase__  ,	size=UpperCamelCase__  ,	resample=UpperCamelCase__      ) for image in images]

				if do_center_crop:
						snake_case  :       str     			=						[self.center_crop(image=UpperCamelCase__  ,	size=UpperCamelCase__      ) for image in images]

				if do_rescale:
						snake_case  :       List[Any]     			=						[self.rescale(image=UpperCamelCase__  ,	scale=UpperCamelCase__      ) for image in images]

				# the pretrained checkpoints assume images are BGR, not RGB
				if do_flip_channel_order:
						snake_case  :       Optional[Any]     			=						[self.flip_channel_order(image=UpperCamelCase__      ) for image in images]

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

				snake_case  :       Any     			=						{'''pixel_values''': images}
				return BatchFeature(data=UpperCamelCase__  ,	tensor_type=UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			Dict  ,	UpperCamelCase__				:			List[Tuple] = None      )						->			Dict:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
						if len(UpperCamelCase__      ) != len(UpperCamelCase__      ):
								raise ValueError(
								    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''      )

						if is_torch_tensor(UpperCamelCase__      ):
								snake_case  :       Dict     			=						target_sizes.numpy()

						snake_case  :       str     			=						[]

						for idx in range(len(UpperCamelCase__      )      ):
								snake_case  :       Union[str, Any]     			=						torch.nn.functional.interpolate(
								    logits[idx].unsqueeze(dim=0      )  ,	size=target_sizes[idx]  ,	mode='''bilinear'''  ,	align_corners=UpperCamelCase__      )
								snake_case  :       List[Any]     			=						resized_logits[0].argmax(dim=0      )
								semantic_segmentation.append(UpperCamelCase__      )
				else:
						snake_case  :       Tuple     			=						logits.argmax(dim=1      )
						snake_case  :       str     			=						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

				return semantic_segmentation




'''simple docstring'''
from __future__ import annotations

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

lowercase__						       =      TypeVar("T")






class 	snake_case__       (      Generic[T]					):







		"""simple docstring"""

		def __init__(      self				:			str  ,	UpperCamelCase__				:			list[T]  ,	UpperCamelCase__				:			Callable[[T, T], T]      )						->			None:






				"""simple docstring"""







				snake_case  :       Any | T     			=						None

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



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			None:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			T      )						->			None:






				"""simple docstring"""







				p += self.N
				snake_case  :       int     			=						v
				while p > 1:
						snake_case  :       List[Any]     			=						p // 2
						snake_case  :       List[Any]     			=						self.fn(self.st[p * 2]  ,	self.st[p * 2 + 1]      )



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			int      )						->			T | None:  # noqa: E741






				"""simple docstring"""







				snake_case			,snake_case  :       Union[str, Any]     			=						l + self.N, r + self.N

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


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

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

						lowercase__						       =      {
						    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,
						}

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




						def    _UpperCamelCase						(       )  ->							None:


								'''simple docstring'''


								for i in range(len(SCREAMING_SNAKE_CASE__			)			):
										for j in range(SCREAMING_SNAKE_CASE__ ,       len(SCREAMING_SNAKE_CASE__			)			):
												snake_case  :       int     			=						reduce(SCREAMING_SNAKE_CASE__ ,       test_array[i : j + 1]			)
												snake_case  :       List[str]     			=						reduce(SCREAMING_SNAKE_CASE__ ,       test_array[i : j + 1]			)
												snake_case  :       Tuple     			=						reduce(lambda SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			: a + b ,       test_array[i : j + 1]			)
												assert min_range == min_segment_tree.query(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
												assert max_range == max_segment_tree.query(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
												assert sum_range == sum_segment_tree.query(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)

						test_all_segments()

						for index, value in test_updates.items():
												lowercase__						       =      value
												min_segment_tree.update(index, value)
												max_segment_tree.update(index, value)
												sum_segment_tree.update(index, value)
												test_all_segments()


