from __future__ import annotations

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

SCREAMING_SNAKE_CASE			:						List[Any]				  =		TypeVar("""T""")
class  A_			(							Generic[T]			):

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   list[T]  ,				__SCREAMING_SNAKE_CASE			:   Callable[[T, T], T]							):
   __a							=	None

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

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

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

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   int							):  # noqa: E741
   __a							,  __a							=	l + self.N, r + self.N

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


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

  SCREAMING_SNAKE_CASE			:						Dict				  =		[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			:						Dict				  =		SegmentTree(test_array, min)
  SCREAMING_SNAKE_CASE			:						int				  =		SegmentTree(test_array, max)
  SCREAMING_SNAKE_CASE			:						str				  =		SegmentTree(test_array, lambda a, b: a + b)



  def 	__A		(      ):



   """simple docstring"""


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

  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()



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""image_processor""", """tokenizer"""]
  _SCREAMING_SNAKE_CASE     					=      """ChineseCLIPImageProcessor"""
  _SCREAMING_SNAKE_CASE     					=      ("""BertTokenizer""", """BertTokenizerFast""")

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Any=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):
   __a							=	None
   if "feature_extractor" in kwargs:
    warnings.warn(
        "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
        " instead."  ,				__SCREAMING_SNAKE_CASE  ,				)
    __a							=	kwargs.pop("feature_extractor"							)

   __a							=	image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
    raise ValueError("You need to specify an `image_processor`."							)
   if tokenizer is None:
    raise ValueError("You need to specify a `tokenizer`."							)

   super().__init__(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	self.image_processor

  def __call__(		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):

   if text is None and images is None:
    raise ValueError("You have to specify either text or images. Both cannot be none."							)

   if text is not None:
    __a							=	self.tokenizer(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if images is not None:
    __a							=	self.image_processor(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if text is not None and images is not None:
    __a							=	image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE							)  ,				tensor_type=__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   int  ,				*__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				**__SCREAMING_SNAKE_CASE			:   str							):
   return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				*__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  @property
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	self.tokenizer.model_input_names
   __a							=	self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)

  @property
  def 		_UpperCAmelCase (		self			:   Any							):
   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,				__SCREAMING_SNAKE_CASE  ,				)
   return self.image_processor_class


