
'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			_UpperCAmelCase			(			A_     ):

     """simple docstring"""

     a_     						=       ["image_processor", "tokenizer"]
     a_     						=       "CLIPImageProcessor"
     a_     						=       ("CLIPTokenizer", "CLIPTokenizerFast")


     def __init__(   self				,		lowerCAmelCase_=None				,		lowerCAmelCase_=None				,		**lowerCAmelCase_			):

       '''simple docstring'''

       a_   :						Tuple   =				None
       if "feature_extractor" in kwargs:
         warnings.warn(
             """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
             """ instead."""				,		snake_case__				,		)
         a_   :						Optional[int]   =				kwargs.pop("""feature_extractor"""			)

       a_   :						int   =				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__(snake_case__				,		snake_case__			)


     def __call__(   self				,		lowerCAmelCase_=None				,		lowerCAmelCase_=None				,		lowerCAmelCase_=None				,		**lowerCAmelCase_			):

       '''simple docstring'''

       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_   :						List[Any]   =				self.tokenizer(snake_case__				,		return_tensors=snake_case__				,		**snake_case__			)

       if images is not None:
         a_   :						Dict   =				self.image_processor(snake_case__				,		return_tensors=snake_case__				,		**snake_case__			)

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


     def 				_lowerCAmelCase	(   self				,		*lowerCAmelCase_				,		**lowerCAmelCase_			):

       '''simple docstring'''

       return self.tokenizer.batch_decode(*snake_case__				,		**snake_case__			)


     def 				_lowerCAmelCase	(   self				,		*lowerCAmelCase_				,		**lowerCAmelCase_			):

       '''simple docstring'''

       return self.tokenizer.decode(*snake_case__				,		**snake_case__			)


     @property
     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       a_   :						List[str]   =				self.tokenizer.model_input_names
       a_   :						Optional[Any]   =				self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


     @property
     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       warnings.warn(
           """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,		snake_case__				,		)
       return self.image_processor_class






     @property
     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       warnings.warn(
           """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,		snake_case__				,		)
       return self.image_processor




from __future__ import annotations

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

__lowerCamelCase				=			TypeVar("""T""")






class     UpperCAmelCase						(		Generic[T]					):



   def __init__(self						:   int       ,		snake_case__						:   list[T]       ,		snake_case__						:   Callable[[T, T], T]					)						->      None:


          '''simple docstring'''


          snake_case			:  Any | T     							= None

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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      None:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict       ,		snake_case__						:   int       ,		snake_case__						:   T					)						->      None:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   int       ,		snake_case__						:   int					)						->      T | None:  # 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			:  List[str]     							= self.st[l] if res is None else self.fn(snake_case__       ,		self.st[l]					)
                 if r % 2 == 0:
                        snake_case			:  int     							= self.st[r] if res is None else self.fn(snake_case__       ,		self.st[r]					)
                 snake_case							,   snake_case			:  str     							= (l + 1) // 2, (r - 1) // 2
          return res


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

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

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

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


   def      UpperCamelCase   (       ):
          for i in range(len(__lowerCamelCase       )       ):
                 for j in range(__lowerCamelCase							,   len(__lowerCamelCase       )       ):
                        snake_case			:  int     							= reduce(__lowerCamelCase							,   test_array[i : j + 1]       )
                        snake_case			:  Tuple     							= reduce(__lowerCamelCase							,   test_array[i : j + 1]       )
                        snake_case			:  Union[str, Any]     							= reduce(lambda __lowerCamelCase							,   __lowerCamelCase       : a + b							,   test_array[i : j + 1]       )
                        assert min_range == min_segment_tree.query(__lowerCamelCase							,   __lowerCamelCase       )
                        assert max_range == max_segment_tree.query(__lowerCamelCase							,   __lowerCamelCase       )
                        assert sum_range == sum_segment_tree.query(__lowerCamelCase							,   __lowerCamelCase       )

   test_all_segments()

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





