






'''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,
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


lowerCamelCase_												=					logging.get_logger(__name__)

class    _UpperCAmelCase			(     snake_case_						):




   """simple docstring"""

   snake_case    				=						['''pixel_values''']

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





         '''simple docstring'''





         super().__init__(**__UpperCAmelCase		)
         _A			     =	size if size is not None else {"shortest_edge": 384}
         _A			     =	get_size_dict(__UpperCAmelCase      ,      default_to_square=__UpperCAmelCase		)

         _A			     =	do_resize
         _A			     =	size
         # Default value set here for backwards compatibility where the value in config is None
         _A			     =	crop_pct if crop_pct is not None else 224 / 256
         _A			     =	resample
         _A			     =	do_rescale
         _A			     =	rescale_factor
         _A			     =	do_normalize
         _A			     =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         _A			     =	image_std if image_std is not None else IMAGENET_STANDARD_STD

   def 						lowerCAmelCase (		self :	Optional[int]      ,      __UpperCAmelCase :	np.ndarray      ,      __UpperCAmelCase :	Dict[str, int]      ,      __UpperCAmelCase :	float      ,      __UpperCAmelCase :	PILImageResampling = PILImageResampling.BICUBIC      ,      __UpperCAmelCase :	Optional[Union[str, ChannelDimension]] = None      ,      **__UpperCAmelCase :	Optional[Any]      ,      ):





         '''simple docstring'''





         _A			     =	get_size_dict(__UpperCAmelCase      ,      default_to_square=__UpperCAmelCase		)
         if "shortest_edge" not in size:
               raise ValueError(f'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''		)
         _A			     =	size["shortest_edge"]

         if shortest_edge < 384:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               _A			     =	int(shortest_edge / crop_pct		)
               _A			     =	get_resize_output_image_size(__UpperCAmelCase      ,      size=__UpperCAmelCase      ,      default_to_square=__UpperCAmelCase		)
               _A			     =	resize(image=__UpperCAmelCase      ,      size=__UpperCAmelCase      ,      resample=__UpperCAmelCase      ,      data_format=__UpperCAmelCase      ,      **__UpperCAmelCase		)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=__UpperCAmelCase      ,      size=(shortest_edge, shortest_edge)      ,      data_format=__UpperCAmelCase      ,      **__UpperCAmelCase		)
         else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   __UpperCAmelCase      ,      size=(shortest_edge, shortest_edge)      ,      resample=__UpperCAmelCase      ,      data_format=__UpperCAmelCase      ,      **__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	str      ,      __UpperCAmelCase :	np.ndarray      ,      __UpperCAmelCase :	Union[int, float]      ,      __UpperCAmelCase :	Optional[Union[str, ChannelDimension]] = None      ,      **__UpperCAmelCase :	Union[str, Any]      ,      ):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	int      ,      __UpperCAmelCase :	np.ndarray      ,      __UpperCAmelCase :	Union[float, List[float]]      ,      __UpperCAmelCase :	Union[float, List[float]]      ,      __UpperCAmelCase :	Optional[Union[str, ChannelDimension]] = None      ,      **__UpperCAmelCase :	Union[str, Any]      ,      ):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	List[str]      ,      __UpperCAmelCase :	ImageInput      ,      __UpperCAmelCase :	bool = None      ,      __UpperCAmelCase :	Dict[str, int] = None      ,      __UpperCAmelCase :	float = None      ,      __UpperCAmelCase :	PILImageResampling = None      ,      __UpperCAmelCase :	bool = None      ,      __UpperCAmelCase :	float = None      ,      __UpperCAmelCase :	bool = None      ,      __UpperCAmelCase :	Optional[Union[float, List[float]]] = None      ,      __UpperCAmelCase :	Optional[Union[float, List[float]]] = None      ,      __UpperCAmelCase :	Optional[Union[str, TensorType]] = None      ,      __UpperCAmelCase :	ChannelDimension = ChannelDimension.FIRST      ,      **__UpperCAmelCase :	List[Any]      ,      ):





         '''simple docstring'''





         _A			     =	do_resize if do_resize is not None else self.do_resize
         _A			     =	crop_pct if crop_pct is not None else self.crop_pct
         _A			     =	resample if resample is not None else self.resample
         _A			     =	do_rescale if do_rescale is not None else self.do_rescale
         _A			     =	rescale_factor if rescale_factor is not None else self.rescale_factor
         _A			     =	do_normalize if do_normalize is not None else self.do_normalize
         _A			     =	image_mean if image_mean is not None else self.image_mean
         _A			     =	image_std if image_std is not None else self.image_std

         _A			     =	size if size is not None else self.size
         _A			     =	get_size_dict(__UpperCAmelCase      ,      default_to_square=__UpperCAmelCase		)

         _A			     =	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 or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."		)

         if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
               raise ValueError("crop_pct must be specified if size < 384."		)

         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."		)

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

         if do_resize:
               _A			     =	[self.resize(image=__UpperCAmelCase      ,      size=__UpperCAmelCase      ,      crop_pct=__UpperCAmelCase      ,      resample=__UpperCAmelCase		) for image in images]

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

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

         _A			     =	[to_channel_dimension_format(__UpperCAmelCase      ,      __UpperCAmelCase		) for image in images]

         _A			     =	{"pixel_values": images}
         return BatchFeature(data=__UpperCAmelCase      ,      tensor_type=__UpperCAmelCase		)












'''simple docstring'''




from __future__ import annotations

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

__SCREAMING_SNAKE_CASE     :Optional[int]       =				TypeVar('''T''')







class   A_	(  Generic[T]			):




 def __init__(					self       :    List[Any]	,			snake_case_       :    list[T]	,			snake_case_       :    Callable[[T, T], T]    ):
      _UpperCAmelCase     							=   None

      _UpperCAmelCase     							=   len(snake_case_    )
      _UpperCAmelCase     							=   [any_type for _ in range(self.N    )] + arr
      _UpperCAmelCase     							=   fnc
      self.build()




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




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    T    ):
      p += self.N
      _UpperCAmelCase     							=   v
      while p > 1:
           _UpperCAmelCase     							=   p // 2
           _UpperCAmelCase     							=   self.fn(self.st[p * 2]	,			self.st[p * 2 + 1]    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    int    ):  # noqa: E741
      _UpperCAmelCase							,  _UpperCAmelCase     							=   l + self.N, r + self.N

      _UpperCAmelCase     							=   None
      while l <= r:
           if l % 2 == 1:
                _UpperCAmelCase     							=   self.st[l] if res is None else self.fn(snake_case_	,			self.st[l]    )
           if r % 2 == 0:
                _UpperCAmelCase     							=   self.st[r] if res is None else self.fn(snake_case_	,			self.st[r]    )
           _UpperCAmelCase							,  _UpperCAmelCase     							=   (l + 1) // 2, (r - 1) // 2
      return res


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

  __SCREAMING_SNAKE_CASE     :Union[str, Any]       =				[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

  __SCREAMING_SNAKE_CASE     :List[str]       =				{
      0: 7,
      1: 2,
      2: 6,
      3: -14,
      4: 5,
      5: 4,
      6: 7,
      7: -10,
      8: 9,
      9: 10,
      10: 12,
      11: 1,
  }

  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, min)
  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, max)
  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, lambda a, b: a + b)
  def 	UpperCAmelCase_							(					)		->       None:


       '''simple docstring'''



       for i in range(len(__lowercase       )       ):
            for j in range(__lowercase			,     len(__lowercase       )       ):
                 _UpperCAmelCase     							=   reduce(__lowercase			,     test_array[i : j + 1]       )
                 _UpperCAmelCase     							=   reduce(__lowercase			,     test_array[i : j + 1]       )
                 _UpperCAmelCase     							=   reduce(lambda __lowercase			,     __lowercase       : a + b			,     test_array[i : j + 1]       )
                 assert min_range == min_segment_tree.query(__lowercase			,     __lowercase       )
                 assert max_range == max_segment_tree.query(__lowercase			,     __lowercase       )
                 assert sum_range == sum_segment_tree.query(__lowercase			,     __lowercase       )

  test_all_segments()

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




