






from collections.abc import Sequence
from queue import Queue




class 							_lowerCAmelCase:





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None				):
         UpperCamelCase_:   Tuple        =							start
         UpperCamelCase_:   Tuple        =							end
         UpperCamelCase_:   Optional[Any]        =							val
         UpperCamelCase_:   List[str]        =							(start + end) // 2
         UpperCamelCase_:   List[str]        =							left
         UpperCamelCase_:   List[Any]        =							right





       def __repr__(   self				):
         return f'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''







class 							_lowerCAmelCase:





       """simple docstring"""







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





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         self._update_tree(self.root    ,     _lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         return self._query_range(self.root    ,     _lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         if start == end:
           return SegmentTreeNode(_lowerCamelCase    ,     _lowerCamelCase    ,     self.collection[start]				)
         UpperCamelCase_:   int        =							(start + end) // 2
         UpperCamelCase_:   List[str]        =							self._build_tree(_lowerCamelCase    ,     _lowerCamelCase				)
         UpperCamelCase_:   Any        =							self._build_tree(mid + 1    ,     _lowerCamelCase				)
         return SegmentTreeNode(_lowerCamelCase    ,     _lowerCamelCase    ,     self.fn(left.val    ,     right.val				)    ,     _lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         if node.start == i and node.end == i:
           UpperCamelCase_:   List[Any]        =							val
           return
         if i <= node.mid:
           self._update_tree(node.left    ,     _lowerCamelCase    ,     _lowerCamelCase				)
         else:
           self._update_tree(node.right    ,     _lowerCamelCase    ,     _lowerCamelCase				)
         UpperCamelCase_:   Any        =							self.fn(node.left.val    ,     node.right.val				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         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      _a       (   self				):
         if self.root is not None:
           UpperCamelCase_:   Union[str, Any]        =							Queue()
           queue.put(self.root				)
           while not queue.empty():
             UpperCamelCase_:   List[str]        =							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)
      A_						:						List[str]						      =					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 BertForMaskedLM


if __name__ == "__main__":
	__snake_case				:  Optional[Any] =       argparse.ArgumentParser(
	    description=(
	        'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
	        ' Distillation'
	    )
	)
	parser.add_argument('--model_type', default='bert', choices=['bert'])
	parser.add_argument('--model_name', default='bert-base-uncased', type=str)
	parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
	parser.add_argument('--vocab_transform', action='store_true')
	__snake_case				:  List[Any] =       parser.parse_args()

	if args.model_type == "bert":
		__snake_case				:  int =       BertForMaskedLM.from_pretrained(args.model_name)
		__snake_case				:  Tuple =       'bert'
	else:
		raise ValueError('args.model_type should be "bert".')

	__snake_case				:  List[Any] =       model.state_dict()
	__snake_case				:  Any =       {}

	for w in ["word_embeddings", "position_embeddings"]:
		__snake_case				:  List[str] =       state_dict[F"""{prefix}.embeddings.{w}.weight"""]
	for w in ["weight", "bias"]:
		__snake_case				:  Optional[int] =       state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

	__snake_case				:  List[str] =       0
	for teacher_idx in [0, 2, 4, 7, 9, 11]:
		for w in ["weight", "bias"]:
			__snake_case				:  str =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
			]
			__snake_case				:  Tuple =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
			]
			__snake_case				:  str =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
			]

			__snake_case				:  Any =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
			]
			__snake_case				:  Union[str, Any] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
			]

			__snake_case				:  Union[str, Any] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
			]
			__snake_case				:  Any =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
			]
			__snake_case				:  Optional[int] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
			]
		std_idx += 1

	__snake_case				:  Union[str, Any] =       state_dict['cls.predictions.decoder.weight']
	__snake_case				:  Union[str, Any] =       state_dict['cls.predictions.bias']
	if args.vocab_transform:
		for w in ["weight", "bias"]:
			__snake_case				:  Union[str, Any] =       state_dict[F"""cls.predictions.transform.dense.{w}"""]
			__snake_case				:  List[str] =       state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

	print(F"""N layers selected for distillation: {std_idx}""")
	print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

	print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
	torch.save(compressed_sd, args.dump_checkpoint)