






'''simple docstring'''



from collections.abc import Sequence
from queue import Queue
class 				__SCREAMING_SNAKE_CASE							:




      def __init__(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase=None			, lowerCamelCase=None   )				->List[str]:



         '''simple docstring'''
         __a					  =     start
         __a					  =     end
         __a					  =     val
         __a					  =     (start + end) // 2
         __a					  =     left
         __a					  =     right






      def __repr__(	self   )				->int:



         '''simple docstring'''
         return F"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""







class 				__SCREAMING_SNAKE_CASE							:




      def __init__(	self			, lowerCamelCase			, lowerCamelCase   )				->Dict:



         '''simple docstring'''
         __a					  =     collection
         __a					  =     function
         if self.collection:
            __a					  =     self._build_tree(0			, len(lowerCamelCase   ) - 1   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''
         self._update_tree(self.root			, lowerCamelCase			, lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase   )				->List[Any]:



         '''simple docstring'''
         return self._query_range(self.root			, lowerCamelCase			, lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''
         if start == end:
            return SegmentTreeNode(lowerCamelCase			, lowerCamelCase			, self.collection[start]   )
         __a					  =     (start + end) // 2
         __a					  =     self._build_tree(lowerCamelCase			, lowerCamelCase   )
         __a					  =     self._build_tree(mid + 1			, lowerCamelCase   )
         return SegmentTreeNode(lowerCamelCase			, lowerCamelCase			, self.fn(left.val			, right.val   )			, lowerCamelCase			, lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->int:



         '''simple docstring'''
         if node.start == i and node.end == i:
            __a					  =     val
            return
         if i <= node.mid:
            self._update_tree(node.left			, lowerCamelCase			, lowerCamelCase   )
         else:
            self._update_tree(node.right			, lowerCamelCase			, lowerCamelCase   )
         __a					  =     self.fn(node.left.val			, node.right.val   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''
         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			, lowerCamelCase			, lowerCamelCase   )
            else:
               # range in left child tree and right child tree
               return self.fn(
                   self._query_range(node.left			, lowerCamelCase			, node.mid   )			, self._query_range(node.right			, node.mid + 1			, lowerCamelCase   )			, )
         else:
            # range in right child tree
            return self._query_range(node.right			, lowerCamelCase			, lowerCamelCase   )






      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         if self.root is not None:
            __a					  =     Queue()
            queue.put(self.root   )
            while not queue.empty():
               __a					  =     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)
  __UpperCamelCase   :							Union[str, Any]         =   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()







'''simple docstring'''



import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Dict     )	->      List[Any]:

   """simple docstring"""
   # Initialise PyTorch model
   __a					  =     RemBertConfig.from_json_file(SCREAMING_SNAKE_CASE__     )
   print('Building PyTorch model from configuration: {}'.format(str(SCREAMING_SNAKE_CASE__     )     )     )
   __a					  =     RemBertModel(SCREAMING_SNAKE_CASE__     )

   # Load weights from tf checkpoint
   load_tf_weights_in_rembert(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )

   # Save pytorch-model
   print('Save PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__     )     )
   torch.save(model.state_dict(),       SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 __UpperCamelCase   :							List[str]         =   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--rembert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained RemBERT model. \n"""
         """This specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 __UpperCamelCase   :							List[str]         =   parser.parse_args()
 convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)