




"""simple docstring"""


from __future__ import annotations

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

A_      			=       TypeVar('''T''')






class       lowercase(  Generic[T]				):







 '''simple docstring'''





 def __init__(       self:	Optional[int],   a_:	list[T],   a_:	Callable[[T, T], T]	):




   '''simple docstring'''



   _snake_case							:	Any | T			=						None

   _snake_case							:	int			=						len(a_	)
   _snake_case							:	list[T]			=						[any_type for _ in range(self.N	)] + arr
   _snake_case							:	List[str]			=						fnc
   self.build()


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   for p in range(self.N - 1,   0,   -1	):
     _snake_case							:	Optional[int]			=						self.fn(self.st[p * 2],   self.st[p * 2 + 1]	)


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	int,   a_:	T	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	int,   a_:	int	):  # noqa: E741




   '''simple docstring'''



   _snake_case	,      _snake_case							:	Optional[int]			=						l + self.N, r + self.N

   _snake_case							:	T | None			=						None
   while l <= r:
     if l % 2 == 1:
       _snake_case							:	int			=						self.st[l] if res is None else self.fn(a_,   self.st[l]	)
     if r % 2 == 0:
       _snake_case							:	str			=						self.st[r] if res is None else self.fn(a_,   self.st[r]	)
     _snake_case	,      _snake_case							:	Optional[int]			=						(l + 1) // 2, (r - 1) // 2
   return res


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

     A_      			=       [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

     A_      			=       {
         0: 7,
         1: 2,
         2: 6,
         3: -14,
         4: 5,
         5: 4,
         6: 7,
         7: -10,
         8: 9,
         9: 10,
         10: 12,
         11: 1,
     }

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


     def       UpperCAmelCase__ ():
       """simple docstring"""

       for i in range(len(snake_case__					)					):
         for j in range(snake_case__	,     len(snake_case__					)					):
           _snake_case							:	List[Any]			=						reduce(snake_case__	,     test_array[i : j + 1]					)
           _snake_case							:	Dict			=						reduce(snake_case__	,     test_array[i : j + 1]					)
           _snake_case							:	Optional[Any]			=						reduce(lambda snake_case__	,     snake_case__					: a + b	,     test_array[i : j + 1]					)
           assert min_range == min_segment_tree.query(snake_case__	,     snake_case__					)
           assert max_range == max_segment_tree.query(snake_case__	,     snake_case__					)
           assert sum_range == sum_segment_tree.query(snake_case__	,     snake_case__					)

     test_all_segments()

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








"""simple docstring"""


from ...processing_utils import ProcessorMixin






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      ["image_processor", "feature_extractor"]
 lowercase__				      =      "TvltImageProcessor"
 lowercase__				      =      "TvltFeatureExtractor"


 def __init__(       self:	Dict,   a_:	Union[str, Any],   a_:	Union[str, Any]	):




   '''simple docstring'''



   super().__init__(image_processor=a_,   feature_extractor=a_	)

   _snake_case							:	Any			=						image_processor
   _snake_case							:	Dict			=						feature_extractor


 def __call__(       self:	int,   a_:	str=None,   a_:	Tuple=None,   a_:	Dict=None,   a_:	str=None,   a_:	Optional[int]=False,   a_:	Tuple=False,   *a_:	List[str],   **a_:	int,   ):




   '''simple docstring'''



   if images is None and audio is None:
     raise ValueError("""You need to specify either an `images` or `audio` input to process."""	)

   _snake_case							:	Optional[int]			=						None
   if images is not None:
     _snake_case							:	Tuple			=						self.image_processor(a_,   mask_pixel=a_,   *a_,   **a_	)
   if images_mixed is not None:
     _snake_case							:	Optional[int]			=						self.image_processor(a_,   is_mixed=a_,   *a_,   **a_	)
   if audio is not None:
     _snake_case							:	Any			=						self.feature_extractor(
         a_,   *a_,   sampling_rate=a_,   mask_audio=a_,   **a_	)

   _snake_case							:	List[str]			=						{}
   if audio is not None:
     output_dict.update(a_	)
   if images is not None:
     output_dict.update(a_	)
   if images_mixed_dict is not None:
     output_dict.update(a_	)
   return output_dict


 @property
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.image_processor.model_input_names
   _snake_case							:	List[str]			=						self.feature_extractor.model_input_names
   return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names	)	)


