



"""simple docstring"""




from __future__ import annotations

import math






class      UpperCAmelCase_  :



      def __init__(					self					:     List[Any]					,     A					:     int						):
           _UpperCAmelCase      :     List[Any]        =   size
           # approximate the overall size of segment tree with given value
           _UpperCAmelCase      :     Any        =   [0 for i in range(0					,     4 * size						)]
           # create array to store lazy update
           _UpperCAmelCase      :     Union[str, Any]        =   [0 for i in range(0					,     4 * size						)]
           _UpperCAmelCase      :     Any        =   [0 for i in range(0					,     4 * size						)]  # flag for lazy update



      def 						snake_case_ (					self					:     Optional[Any]					,     A					:     int						):
           return idx * 2



      def 						snake_case_ (					self					:     Dict					,     A					:     int						):
           return idx * 2 + 1



      def 						snake_case_ (					self					:     int					,     A					:     int					,     A					:     int					,     A					:     int					,     A					:     list[int]						):
           if left_element == right_element:
                _UpperCAmelCase      :     List[Any]        =   a[left_element - 1]
           else:
                _UpperCAmelCase      :     Tuple        =   (left_element + right_element) // 2
                self.build(self.left(A						)					,     A					,     A					,     A						)
                self.build(self.right(A						)					,     mid + 1					,     A					,     A						)
                _UpperCAmelCase      :     Tuple        =   max(
                    self.segment_tree[self.left(A						)]					,     self.segment_tree[self.right(A						)]						)



      def 						snake_case_ (					self					:     str					,     A					:     int					,     A					:     int					,     A					:     int					,     A					:     int					,     A					:     int					,     A					:     int						):
           if self.flag[idx] is True:
                _UpperCAmelCase      :     Optional[Any]        =   self.lazy[idx]
                _UpperCAmelCase      :     Tuple        =   False
                if left_element != right_element:
                     _UpperCAmelCase      :     Optional[Any]        =   self.lazy[idx]
                     _UpperCAmelCase      :     List[str]        =   self.lazy[idx]
                     _UpperCAmelCase      :     Any        =   True
                     _UpperCAmelCase      :     Optional[Any]        =   True

           if right_element < a or left_element > b:
                return True
           if left_element >= a and right_element <= b:
                _UpperCAmelCase      :     List[str]        =   val
                if left_element != right_element:
                     _UpperCAmelCase      :     Optional[Any]        =   val
                     _UpperCAmelCase      :     Union[str, Any]        =   val
                     _UpperCAmelCase      :     Tuple        =   True
                     _UpperCAmelCase      :     Tuple        =   True
                return True
           _UpperCAmelCase      :     str        =   (left_element + right_element) // 2
           self.update(self.left(A						)					,     A					,     A					,     A					,     A					,     A						)
           self.update(self.right(A						)					,     mid + 1					,     A					,     A					,     A					,     A						)
           _UpperCAmelCase      :     List[Any]        =   max(
               self.segment_tree[self.left(A						)]					,     self.segment_tree[self.right(A						)]						)
           return True



      def 						snake_case_ (					self					:     str					,     A					:     int					,     A					:     int					,     A					:     int					,     A					:     int					,     A					:     int						):
           if self.flag[idx] is True:
                _UpperCAmelCase      :     List[str]        =   self.lazy[idx]
                _UpperCAmelCase      :     Optional[Any]        =   False
                if left_element != right_element:
                     _UpperCAmelCase      :     int        =   self.lazy[idx]
                     _UpperCAmelCase      :     Any        =   self.lazy[idx]
                     _UpperCAmelCase      :     int        =   True
                     _UpperCAmelCase      :     int        =   True
           if right_element < a or left_element > b:
                return -math.inf
           if left_element >= a and right_element <= b:
                return self.segment_tree[idx]
           _UpperCAmelCase      :     Union[str, Any]        =   (left_element + right_element) // 2
           _UpperCAmelCase      :     Dict        =   self.query(self.left(A						)					,     A					,     A					,     A					,     A						)
           _UpperCAmelCase      :     Any        =   self.query(self.right(A						)					,     mid + 1					,     A					,     A					,     A						)
           return max(A					,     A						)



      def __str__(					self					:     List[Any]						):
           return str([self.query(1					,     1					,     self.size					,     A					,     A						) for i in range(1					,     self.size + 1						)]						)


if __name__ == "__main__":
 _lowerCAmelCase    : List[Any]							=							[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
 _lowerCAmelCase    : List[str]							=							15
 _lowerCAmelCase    : str							=							SegmentTree(size)
 segt.build(1, 1, size, A)
 print(segt.query(1, 1, size, 4, 6))
 print(segt.query(1, 1, size, 7, 11))
 print(segt.query(1, 1, size, 7, 12))
 segt.update(1, 1, size, 1, 3, 1_11)
 print(segt.query(1, 1, size, 1, 15))
 segt.update(1, 1, size, 7, 8, 2_35)
 print(segt)





"""simple docstring"""




from typing import TYPE_CHECKING

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


_lowerCAmelCase    : Union[str, Any]							=							{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Any							=							["RoFormerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Any							=							[
     "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "RoFormerForCausalLM",
     "RoFormerForMaskedLM",
     "RoFormerForMultipleChoice",
     "RoFormerForQuestionAnswering",
     "RoFormerForSequenceClassification",
     "RoFormerForTokenClassification",
     "RoFormerLayer",
     "RoFormerModel",
     "RoFormerPreTrainedModel",
     "load_tf_weights_in_roformer",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Optional[int]							=							[
     "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFRoFormerForCausalLM",
     "TFRoFormerForMaskedLM",
     "TFRoFormerForMultipleChoice",
     "TFRoFormerForQuestionAnswering",
     "TFRoFormerForSequenceClassification",
     "TFRoFormerForTokenClassification",
     "TFRoFormerLayer",
     "TFRoFormerModel",
     "TFRoFormerPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : List[str]							=							[
     "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "FlaxRoFormerForMaskedLM",
     "FlaxRoFormerForMultipleChoice",
     "FlaxRoFormerForQuestionAnswering",
     "FlaxRoFormerForSequenceClassification",
     "FlaxRoFormerForTokenClassification",
     "FlaxRoFormerModel",
     "FlaxRoFormerPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
 from .tokenization_roformer import RoFormerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_roformer_fast import RoFormerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roformer import (
      ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      RoFormerForCausalLM,
      RoFormerForMaskedLM,
      RoFormerForMultipleChoice,
      RoFormerForQuestionAnswering,
      RoFormerForSequenceClassification,
      RoFormerForTokenClassification,
      RoFormerLayer,
      RoFormerModel,
      RoFormerPreTrainedModel,
      load_tf_weights_in_roformer,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roformer import (
      TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerLayer,
      TFRoFormerModel,
      TFRoFormerPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roformer import (
      FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
      FlaxRoFormerPreTrainedModel,
  )


else:
 import sys

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