





'''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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    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


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


if is_vision_available():
  import PIL







class   A_	(  A_			):
 _lowerCamelCase  : Union[str, Any]           =							["pixel_values"]




 def __init__(					self       :    Tuple	,			snake_case_       :    str = True	,			snake_case_       :    List[str] = None	,			snake_case_       :    Union[str, Any] = PILImageResampling.BICUBIC	,			snake_case_       :    Dict = True	,			snake_case_       :    Optional[int] = None	,			snake_case_       :    Any = True	,			snake_case_       :    str = 1 / 2_5_5	,			snake_case_       :    str = True	,			snake_case_       :    Optional[Any] = None	,			snake_case_       :    Union[str, Any] = None	,			snake_case_       :    Union[str, Any] = True	,			**snake_case_       :    List[str]	,			):
      super().__init__(**A_    )
      _UpperCAmelCase     							=   size if size is not None else {"shortest_edge": 2_2_4}
      _UpperCAmelCase     							=   get_size_dict(A_	,			default_to_square=A_    )
      _UpperCAmelCase     							=   crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _UpperCAmelCase     							=   get_size_dict(A_	,			default_to_square=A_	,			param_name="crop_size"    )

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




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    Tuple	,			snake_case_       :    Optional[Any]	,			snake_case_       :    Dict = PILImageResampling.BICUBIC	,			snake_case_       :    List[Any] = None	,			**snake_case_       :    List[str]	,			):
      _UpperCAmelCase     							=   get_size_dict(A_	,			default_to_square=A_    )
      if "shortest_edge" not in size:
           raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'    )
      _UpperCAmelCase     							=   get_resize_output_image_size(A_	,			size=size["shortest_edge"]	,			default_to_square=A_    )
      return resize(A_	,			size=A_	,			resample=A_	,			data_format=A_	,			**A_    )




 def      lowercase	(					self       :    int	,			snake_case_       :    int	,			snake_case_       :    int	,			snake_case_       :    str = None	,			**snake_case_       :    Tuple	,			):
      _UpperCAmelCase     							=   get_size_dict(A_    )
      if "height" not in size or "width" not in size:
           raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'    )
      return center_crop(A_	,			size=(size["height"], size["width"])	,			data_format=A_	,			**A_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    Dict	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    List[Any] = None	,			**snake_case_       :    Optional[int]	,			):
      return rescale(A_	,			scale=A_	,			data_format=A_	,			**A_    )




 def      lowercase	(					self       :    int	,			snake_case_       :    Tuple	,			snake_case_       :    Any	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    Optional[int] = None	,			**snake_case_       :    Tuple	,			):
      return normalize(A_	,			mean=A_	,			std=A_	,			data_format=A_	,			**A_    )




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Optional[Any]	,			snake_case_       :    Optional[Any] = None	,			snake_case_       :    int = None	,			snake_case_       :    str = None	,			snake_case_       :    Tuple = None	,			snake_case_       :    Tuple = None	,			snake_case_       :    Any = None	,			snake_case_       :    Dict = None	,			snake_case_       :    Any = None	,			snake_case_       :    Any = None	,			snake_case_       :    Optional[int] = None	,			snake_case_       :    Optional[int] = None	,			snake_case_       :    int = None	,			snake_case_       :    List[str] = ChannelDimension.FIRST	,			**snake_case_       :    int	,			):
      _UpperCAmelCase     							=   do_resize if do_resize is not None else self.do_resize
      _UpperCAmelCase     							=   size if size is not None else self.size
      _UpperCAmelCase     							=   get_size_dict(A_	,			param_name="size"	,			default_to_square=A_    )
      _UpperCAmelCase     							=   resample if resample is not None else self.resample
      _UpperCAmelCase     							=   do_center_crop if do_center_crop is not None else self.do_center_crop
      _UpperCAmelCase     							=   crop_size if crop_size is not None else self.crop_size
      _UpperCAmelCase     							=   get_size_dict(A_	,			param_name="crop_size"	,			default_to_square=A_    )
      _UpperCAmelCase     							=   do_rescale if do_rescale is not None else self.do_rescale
      _UpperCAmelCase     							=   rescale_factor if rescale_factor is not None else self.rescale_factor
      _UpperCAmelCase     							=   do_normalize if do_normalize is not None else self.do_normalize
      _UpperCAmelCase     							=   image_mean if image_mean is not None else self.image_mean
      _UpperCAmelCase     							=   image_std if image_std is not None else self.image_std
      _UpperCAmelCase     							=   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      _UpperCAmelCase     							=   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:
           raise ValueError("Size must be specified if do_resize is True."    )

      if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop 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:
           _UpperCAmelCase     							=   [convert_to_rgb(A_    ) for image in images]

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

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

      if do_center_crop:
           _UpperCAmelCase     							=   [self.center_crop(image=A_	,			size=A_    ) for image in images]

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

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

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

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








from __future__ import annotations

import math






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_    )  ->       None:
         __UpperCamelCase    							=size
         # approximate the overall size of segment tree with given value
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]
         # create array to store lazy update
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]  # flag for lazy update






    def _a					(			self					,   A_    )  ->       int:
         return idx * 2






    def _a					(			self					,   A_    )  ->       int:
         return idx * 2 + 1






    def _a					(			self					,   A_					,   A_					,   A_					,   A_    )  ->       None:
         if left_element == right_element:
              __UpperCamelCase    							=a[left_element - 1]
         else:
              __UpperCamelCase    							=(left_element + right_element) // 2
              self.build(self.left(A_    )					,   A_					,   A_					,   A_    )
              self.build(self.right(A_    )					,   mid + 1					,   A_					,   A_    )
              __UpperCamelCase    							=max(
                  self.segment_tree[self.left(A_    )]					,   self.segment_tree[self.right(A_    )]    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       bool:
         if self.flag[idx] is True:
              __UpperCamelCase    							=self.lazy[idx]
              __UpperCamelCase    							=False
              if left_element != right_element:
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=True

         if right_element < a or left_element > b:
              return True
         if left_element >= a and right_element <= b:
              __UpperCamelCase    							=val
              if left_element != right_element:
                   __UpperCamelCase    							=val
                   __UpperCamelCase    							=val
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=True
              return True
         __UpperCamelCase    							=(left_element + right_element) // 2
         self.update(self.left(A_    )					,   A_					,   A_					,   A_					,   A_					,   A_    )
         self.update(self.right(A_    )					,   mid + 1					,   A_					,   A_					,   A_					,   A_    )
         __UpperCamelCase    							=max(
             self.segment_tree[self.left(A_    )]					,   self.segment_tree[self.right(A_    )]    )
         return True






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       int | float:
         if self.flag[idx] is True:
              __UpperCamelCase    							=self.lazy[idx]
              __UpperCamelCase    							=False
              if left_element != right_element:
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=True
         if right_element < a or left_element > b:
              return -math.inf
         if left_element >= a and right_element <= b:
              return self.segment_tree[idx]
         __UpperCamelCase    							=(left_element + right_element) // 2
         __UpperCamelCase    							=self.query(self.left(A_    )					,   A_					,   A_					,   A_					,   A_    )
         __UpperCamelCase    							=self.query(self.right(A_    )					,   mid + 1					,   A_					,   A_					,   A_    )
         return max(A_					,   A_    )






    def __str__(			self    )  ->       str:
         return str([self.query(1					,   1					,   self.size					,   A_					,   A_    ) for i in range(1					,   self.size + 1    )]    )


if __name__ == "__main__":
   _A		       =						[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
   _A		       =						15
   _A		       =						SegmentTree(size)
   segt.build(1, 1, size, A)
   print(segt.query(1, 1, size, 4, 6))
   print(segt.query(1, 1, size, 7, 11))
   print(segt.query(1, 1, size, 7, 12))
   segt.update(1, 1, size, 1, 3, 111)
   print(segt.query(1, 1, size, 1, 15))
   segt.update(1, 1, size, 7, 8, 235)
   print(segt)





