





"""simple docstring"""
class       a							:



 def __init__(     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= size
  lowerCAmelCase    				= [0] * size
  lowerCAmelCase    				= [0] * size



 @staticmethod
 def    UpperCamelCase__  (     _snake_case     ):







  """simple docstring"""






  return index | (index + 1)



 @staticmethod
 def    UpperCamelCase__  (     _snake_case     ):







  """simple docstring"""






  return (index & (index + 1)) - 1



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= value
  while index < self.size:
   lowerCAmelCase    				= self.get_prev(_snake_case     ) + 1
   if current_left_border == index:
    lowerCAmelCase    				= value
   else:
    lowerCAmelCase    				= max(_snake_case				,       _snake_case				,       _snake_case     )
   lowerCAmelCase    				= self.get_next(_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  right -= 1  # Because of right is exclusive
  lowerCAmelCase    				= 0
  while left <= right:
   lowerCAmelCase    				= self.get_prev(_snake_case     )
   if left <= current_left:
    lowerCAmelCase    				= max(_snake_case				,       self.tree[right]     )
    lowerCAmelCase    				= current_left
   else:
    lowerCAmelCase    				= max(_snake_case				,       self.arr[right]     )
    right -= 1
  return result


if __name__ == "__main__":
       import doctest

       doctest.testmod()













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__   :      Dict				=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      str				=		[
					    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """CanineForMultipleChoice""",
					    """CanineForQuestionAnswering""",
					    """CanineForSequenceClassification""",
					    """CanineForTokenClassification""",
					    """CanineLayer""",
					    """CanineModel""",
					    """CaninePreTrainedModel""",
					    """load_tf_weights_in_canine""",
					]


if TYPE_CHECKING:
					from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
					from .tokenization_canine import CanineTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_canine import (
										    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CanineForMultipleChoice,
										    CanineForQuestionAnswering,
										    CanineForSequenceClassification,
										    CanineForTokenClassification,
										    CanineLayer,
										    CanineModel,
										    CaninePreTrainedModel,
										    load_tf_weights_in_canine,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






