


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


_A		       =						logging.get_logger(__name__)


if is_vision_available():
   import PIL






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






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






    def __init__(			self					,   A_ = True					,   A_ = None					,   A_ = PILImageResampling.BICUBIC					,   A_ = True					,   A_ = None					,   A_ = True					,   A_ = 1 / 255					,   A_ = True					,   A_ = None					,   A_ = None					,   A_ = True					,   **A_					,   )  ->       None:
         super().__init__(**A_    )
         __UpperCamelCase    							=size if size is not None else {'shortest_edge': 224}
         __UpperCamelCase    							=get_size_dict(A_					,   default_to_square=A_    )
         __UpperCamelCase    							=crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __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 _a					(			self					,   A_					,   A_					,   A_ = PILImageResampling.BICUBIC					,   A_ = None					,   **A_					,   )  ->       np.ndarray:
         __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 _a					(			self					,   A_					,   A_					,   A_ = None					,   **A_					,   )  ->       np.ndarray:
         __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 _a					(			self					,   A_					,   A_					,   A_ = None					,   **A_					,   )  ->       Union[str, Any]:
         return rescale(A_					,   scale=A_					,   data_format=A_					,   **A_    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_ = None					,   **A_					,   )  ->       np.ndarray:
         return normalize(A_					,   mean=A_					,   std=A_					,   data_format=A_					,   **A_    )






    def _a					(			self					,   A_					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = None					,   A_ = ChannelDimension.FIRST					,   **A_					,   )  ->       PIL.Image.Image:
         __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)





