

from collections.abc import Sequence
from queue import Queue






class 						__magic_name__					:






     def __init__(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase=None		,						_lowercase=None	)->     Optional[int]:
         UpperCamelCase_			  =						start
         UpperCamelCase_			  =						end
         UpperCamelCase_			  =						val
         UpperCamelCase_			  =						(start + end) // 2
         UpperCamelCase_			  =						left
         UpperCamelCase_			  =						right






     def __repr__(				self	)->     Tuple:
         return F"SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"






class 						__magic_name__					:






     def __init__(				self		,						_lowercase		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						collection
         UpperCamelCase_			  =						function
         if self.collection:
             UpperCamelCase_			  =						self._build_tree(0		,						len(_lowercase	) - 1	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     Any:
         self._update_tree(self.root		,						_lowercase		,						_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     Union[str, Any]:
         return self._query_range(self.root		,						_lowercase		,						_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     str:
         if start == end:
             return SegmentTreeNode(_lowercase		,						_lowercase		,						self.collection[start]	)
         UpperCamelCase_			  =						(start + end) // 2
         UpperCamelCase_			  =						self._build_tree(_lowercase		,						_lowercase	)
         UpperCamelCase_			  =						self._build_tree(mid + 1		,						_lowercase	)
         return SegmentTreeNode(_lowercase		,						_lowercase		,						self.fn(left.val		,						right.val	)		,						_lowercase		,						_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase	)->     Union[str, Any]:
         if node.start == i and node.end == i:
             UpperCamelCase_			  =						val
             return
         if i <= node.mid:
             self._update_tree(node.left		,						_lowercase		,						_lowercase	)
         else:
             self._update_tree(node.right		,						_lowercase		,						_lowercase	)
         UpperCamelCase_			  =						self.fn(node.left.val		,						node.right.val	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase	)->     List[Any]:
         if node.start == i and node.end == j:
             return node.val

         if i <= node.mid:
             if j <= node.mid:
                 # range in left child tree
                 return self._query_range(node.left		,						_lowercase		,						_lowercase	)
             else:
                 # range in left child tree and right child tree
                 return self.fn(
                     self._query_range(node.left		,						_lowercase		,						node.mid	)		,						self._query_range(node.right		,						node.mid + 1		,						_lowercase	)		,						)
         else:
             # range in right child tree
             return self._query_range(node.right		,						_lowercase		,						_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         if self.root is not None:
             UpperCamelCase_			  =						Queue()
             queue.put(self.root	)
             while not queue.empty():
                 UpperCamelCase_			  =						queue.get()
                 yield node

                 if node.left is not None:
                     queue.put(node.left	)

                 if node.right is not None:
                     queue.put(node.right	)


if __name__ == "__main__":
 import operator

 for fn in [operator.add, max, min]:
  print("""*""" * 50)
  SCREAMING_SNAKE_CASE		:Dict						=     SegmentTree([2, 1, 5, 3, 4], fn)
  for node in arr.traverse():
   print(node)
  print()

  arr.update(1, 5)
  for node in arr.traverse():
   print(node)
  print()

  print(arr.query_range(3, 4))  # 7
  print(arr.query_range(2, 2))  # 5
  print(arr.query_range(1, 3))  # 13
  print()



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Optional[int]						=     {
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str						=     ["""RemBertTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     ["""RemBertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict						=     [
     """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RemBertForCausalLM""",
     """RemBertForMaskedLM""",
     """RemBertForMultipleChoice""",
     """RemBertForQuestionAnswering""",
     """RemBertForSequenceClassification""",
     """RemBertForTokenClassification""",
     """RemBertLayer""",
     """RemBertModel""",
     """RemBertPreTrainedModel""",
     """load_tf_weights_in_rembert""",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[int]						=     [
     """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFRemBertForCausalLM""",
     """TFRemBertForMaskedLM""",
     """TFRemBertForMultipleChoice""",
     """TFRemBertForQuestionAnswering""",
     """TFRemBertForSequenceClassification""",
     """TFRemBertForTokenClassification""",
     """TFRemBertLayer""",
     """TFRemBertModel""",
     """TFRemBertPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert import RemBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert_fast import RemBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rembert import (
      REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      RemBertForCausalLM,
      RemBertForMaskedLM,
      RemBertForMultipleChoice,
      RemBertForQuestionAnswering,
      RemBertForSequenceClassification,
      RemBertForTokenClassification,
      RemBertLayer,
      RemBertModel,
      RemBertPreTrainedModel,
      load_tf_weights_in_rembert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rembert import (
      TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRemBertForCausalLM,
      TFRemBertForMaskedLM,
      TFRemBertForMultipleChoice,
      TFRemBertForQuestionAnswering,
      TFRemBertForSequenceClassification,
      TFRemBertForTokenClassification,
      TFRemBertLayer,
      TFRemBertModel,
      TFRemBertPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE		:List[Any]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
