

"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
    import PIL

_A							=  logging.get_logger(__name__)




def 					a__						(						lowerCAmelCase    )	-> List[List[ImageInput]]:
      if isinstance(lowerCAmelCase			,	(list, tuple)    ) and isinstance(videos[0]			,	(list, tuple)    ) and is_valid_image(videos[0][0]    ):
            return videos

      elif isinstance(lowerCAmelCase			,	(list, tuple)    ) and is_valid_image(videos[0]    ):
            return [videos]

      elif is_valid_image(lowerCAmelCase    ):
            return [[videos]]

      raise ValueError(F"""Could not make batched video from {videos}"""    )





class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  ['pixel_values']



  def __init__(self							,       _lowerCamelCase = True							,       _lowerCamelCase = None							,       _lowerCamelCase = PILImageResampling.BILINEAR							,       _lowerCamelCase = True							,       _lowerCamelCase = None							,       _lowerCamelCase = True							,       _lowerCamelCase = 1 / 255							,       _lowerCamelCase = True							,       _lowerCamelCase = True							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        super().__init__(**_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    size if size is not None else {"""shortest_edge""": 256}
        UpperCAmelCase__			:							Optional[Any]							=    get_size_dict(_lowerCamelCase							,       default_to_square=_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        UpperCAmelCase__			:							Dict							=    get_size_dict(_lowerCamelCase							,       param_name="""crop_size"""	)

        UpperCAmelCase__			:							Optional[Any]							=    do_resize
        UpperCAmelCase__			:							int							=    size
        UpperCAmelCase__			:							Optional[Any]							=    do_center_crop
        UpperCAmelCase__			:							Optional[int]							=    crop_size
        UpperCAmelCase__			:							Optional[Any]							=    resample
        UpperCAmelCase__			:							List[Any]							=    do_rescale
        UpperCAmelCase__			:							Optional[Any]							=    rescale_factor
        UpperCAmelCase__			:							Optional[Any]							=    offset
        UpperCAmelCase__			:							Dict							=    do_normalize
        UpperCAmelCase__			:							Optional[int]							=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        UpperCAmelCase__			:							int							=    image_std if image_std is not None else IMAGENET_STANDARD_STD



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase = PILImageResampling.BILINEAR							,       _lowerCamelCase = None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    get_size_dict(_lowerCamelCase							,       default_to_square=_lowerCamelCase	)
        if "shortest_edge" in size:
              UpperCAmelCase__			:							Dict							=    get_resize_output_image_size(_lowerCamelCase							,       size["""shortest_edge"""]							,       default_to_square=_lowerCamelCase	)
        elif "height" in size and "width" in size:
              UpperCAmelCase__			:							List[Any]							=    (size["""height"""], size["""width"""])
        else:
              raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""	)
        return resize(_lowerCamelCase							,       size=_lowerCamelCase							,       resample=_lowerCamelCase							,       data_format=_lowerCamelCase							,       **_lowerCamelCase	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase = None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    get_size_dict(_lowerCamelCase	)
        if "height" not in size or "width" not in size:
              raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""	)
        return center_crop(_lowerCamelCase							,       size=(size["""height"""], size["""width"""])							,       data_format=_lowerCamelCase							,       **_lowerCamelCase	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase = True							,       _lowerCamelCase = None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    image.astype(np.floataa	)
        if offset:
              UpperCAmelCase__			:							Tuple							=    image - (scale / 2)
        return rescale(_lowerCamelCase							,       scale=_lowerCamelCase							,       data_format=_lowerCamelCase							,       **_lowerCamelCase	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase = None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        return normalize(_lowerCamelCase							,       mean=_lowerCamelCase							,       std=_lowerCamelCase							,       data_format=_lowerCamelCase							,       **_lowerCamelCase	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = ChannelDimension.FIRST							,       ):


        """simple docstring"""

        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_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."""	)

        if offset and not do_rescale:
              raise ValueError("""For offset, do_rescale must also be set to True."""	)

        # All transformations expect numpy arrays.
        UpperCAmelCase__			:							Optional[Any]							=    to_numpy_array(_lowerCamelCase	)

        if do_resize:
              UpperCAmelCase__			:							Tuple							=    self.resize(image=_lowerCamelCase							,       size=_lowerCamelCase							,       resample=_lowerCamelCase	)

        if do_center_crop:
              UpperCAmelCase__			:							Optional[Any]							=    self.center_crop(_lowerCamelCase							,       size=_lowerCamelCase	)

        if do_rescale:
              UpperCAmelCase__			:							List[str]							=    self.rescale(image=_lowerCamelCase							,       scale=_lowerCamelCase							,       offset=_lowerCamelCase	)

        if do_normalize:
              UpperCAmelCase__			:							Optional[Any]							=    self.normalize(image=_lowerCamelCase							,       mean=_lowerCamelCase							,       std=_lowerCamelCase	)

        UpperCAmelCase__			:							Tuple							=    to_channel_dimension_format(_lowerCamelCase							,       _lowerCamelCase	)
        return image




  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = ChannelDimension.FIRST							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    do_resize if do_resize is not None else self.do_resize
        UpperCAmelCase__			:							Tuple							=    resample if resample is not None else self.resample
        UpperCAmelCase__			:							List[str]							=    do_center_crop if do_center_crop is not None else self.do_center_crop
        UpperCAmelCase__			:							Any							=    do_rescale if do_rescale is not None else self.do_rescale
        UpperCAmelCase__			:							List[str]							=    rescale_factor if rescale_factor is not None else self.rescale_factor
        UpperCAmelCase__			:							Any							=    offset if offset is not None else self.offset
        UpperCAmelCase__			:							str							=    do_normalize if do_normalize is not None else self.do_normalize
        UpperCAmelCase__			:							List[str]							=    image_mean if image_mean is not None else self.image_mean
        UpperCAmelCase__			:							Union[str, Any]							=    image_std if image_std is not None else self.image_std

        UpperCAmelCase__			:							List[Any]							=    size if size is not None else self.size
        UpperCAmelCase__			:							Tuple							=    get_size_dict(_lowerCamelCase							,       default_to_square=_lowerCamelCase	)
        UpperCAmelCase__			:							Dict							=    crop_size if crop_size is not None else self.crop_size
        UpperCAmelCase__			:							Optional[Any]							=    get_size_dict(_lowerCamelCase							,       param_name="""crop_size"""	)

        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."""	)

        UpperCAmelCase__			:							Optional[int]							=    make_batched(_lowerCamelCase	)

        UpperCAmelCase__			:							List[Any]							=    [
            [
                self._preprocess_image(
                    image=_lowerCamelCase							,       do_resize=_lowerCamelCase							,       size=_lowerCamelCase							,       resample=_lowerCamelCase							,       do_center_crop=_lowerCamelCase							,       crop_size=_lowerCamelCase							,       do_rescale=_lowerCamelCase							,       rescale_factor=_lowerCamelCase							,       offset=_lowerCamelCase							,       do_normalize=_lowerCamelCase							,       image_mean=_lowerCamelCase							,       image_std=_lowerCamelCase							,       data_format=_lowerCamelCase							,       )
                for img in video
            ]
            for video in videos
        ]

        UpperCAmelCase__			:							Union[str, Any]							=    {"""pixel_values""": videos}
        return BatchFeature(data=_lowerCamelCase							,       tensor_type=_lowerCamelCase	)




"""simple docstring"""
from __future__ import annotations

import math

class       lowerCamelCase	:


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    size
        # approximate the overall size of segment tree with given value
        UpperCAmelCase__			:							Tuple							=    [0 for i in range(0							,       4 * size	)]
        # create array to store lazy update
        UpperCAmelCase__			:							int							=    [0 for i in range(0							,       4 * size	)]
        UpperCAmelCase__			:							Tuple							=    [0 for i in range(0							,       4 * size	)]  # flag for lazy update



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        return idx * 2



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        return idx * 2 + 1



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if left_element == right_element:
              UpperCAmelCase__			:							Union[str, Any]							=    a[left_element - 1]
        else:
              UpperCAmelCase__			:							List[str]							=    (left_element + right_element) // 2
              self.build(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
              self.build(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase	)
              UpperCAmelCase__			:							int							=    max(
                  self.segment_tree[self.left(_lowerCamelCase	)]							,       self.segment_tree[self.right(_lowerCamelCase	)]	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.flag[idx] is True:
              UpperCAmelCase__			:							Union[str, Any]							=    self.lazy[idx]
              UpperCAmelCase__			:							Dict							=    False
              if left_element != right_element:
                    UpperCAmelCase__			:							Optional[Any]							=    self.lazy[idx]
                    UpperCAmelCase__			:							Optional[int]							=    self.lazy[idx]
                    UpperCAmelCase__			:							Tuple							=    True
                    UpperCAmelCase__			:							str							=    True

        if right_element < a or left_element > b:
              return True
        if left_element >= a and right_element <= b:
              UpperCAmelCase__			:							Union[str, Any]							=    val
              if left_element != right_element:
                    UpperCAmelCase__			:							Union[str, Any]							=    val
                    UpperCAmelCase__			:							List[Any]							=    val
                    UpperCAmelCase__			:							Union[str, Any]							=    True
                    UpperCAmelCase__			:							List[Any]							=    True
              return True
        UpperCAmelCase__			:							Tuple							=    (left_element + right_element) // 2
        self.update(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        self.update(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							Dict							=    max(
            self.segment_tree[self.left(_lowerCamelCase	)]							,       self.segment_tree[self.right(_lowerCamelCase	)]	)
        return True



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.flag[idx] is True:
              UpperCAmelCase__			:							List[Any]							=    self.lazy[idx]
              UpperCAmelCase__			:							Optional[Any]							=    False
              if left_element != right_element:
                    UpperCAmelCase__			:							Tuple							=    self.lazy[idx]
                    UpperCAmelCase__			:							Union[str, Any]							=    self.lazy[idx]
                    UpperCAmelCase__			:							List[str]							=    True
                    UpperCAmelCase__			:							str							=    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__			:							Union[str, Any]							=    (left_element + right_element) // 2
        UpperCAmelCase__			:							Tuple							=    self.query(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							List[Any]							=    self.query(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        return max(_lowerCamelCase							,       _lowerCamelCase	)




  def __str__(self	):


        """simple docstring"""

        return str([self.query(1							,       1							,       self.size							,       _lowerCamelCase							,       _lowerCamelCase	) 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, 1_11)
    print(segt.query(1, 1, size, 1, 15))
    segt.update(1, 1, size, 7, 8, 2_35)
    print(segt)

