




'''simple docstring'''



import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
      UpperCamelCase_    	=				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")
      UpperCamelCase_    	=				parser.parse_args()

      if args.model_type == "bert":
            UpperCamelCase_    	=				BertForMaskedLM.from_pretrained(args.model_name)
            UpperCamelCase_    	=				"bert"
      else:
            raise ValueError("args.model_type should be \"bert\".")

      UpperCamelCase_    	=				model.state_dict()
      UpperCamelCase_    	=				{}

      for w in ["word_embeddings", "position_embeddings"]:
            UpperCamelCase_    	=				state_dict[F"""{prefix}.embeddings.{w}.weight"""]
      for w in ["weight", "bias"]:
            UpperCamelCase_    	=				state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

      UpperCamelCase_    	=				0
      for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
            for w in ["weight", "bias"]:
                  UpperCamelCase_    	=				state_dict[
                      F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
                  ]
                  UpperCamelCase_    	=				state_dict[
                      F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
                  ]
                  UpperCamelCase_    	=				state_dict[
                      F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
                  ]

                  UpperCamelCase_    	=				state_dict[
                      F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
                  ]
                  UpperCamelCase_    	=				state_dict[
                      F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
                  ]

                  UpperCamelCase_    	=				state_dict[
                      F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
                  ]
                  UpperCamelCase_    	=				state_dict[
                      F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
                  ]
                  UpperCamelCase_    	=				state_dict[
                      F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
                  ]
            std_idx += 1

      UpperCamelCase_    	=				state_dict["cls.predictions.decoder.weight"]
      UpperCamelCase_    	=				state_dict["cls.predictions.bias"]
      if args.vocab_transform:
            for w in ["weight", "bias"]:
                  UpperCamelCase_    	=				state_dict[F"""cls.predictions.transform.dense.{w}"""]
                  UpperCamelCase_    	=				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)








'''simple docstring'''



class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						val
             SCREAMING_SNAKE_CASE   :      Any    					=						None
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						None







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if self.val:
                   if val < self.val:
                         if self.left is None:
                               SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Node(A  )
                         else:
                               self.left.insert(A  )
                   elif val > self.val:
                         if self.right is None:
                               SCREAMING_SNAKE_CASE   :      int    					=						Node(A  )
                         else:
                               self.right.insert(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      int    					=						val




def   lowercase__(			__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      if root:
            inorder(root.left      ,__UpperCamelCase    )
            res.append(root.val    )
            inorder(root.right      ,__UpperCamelCase    )




def   lowercase__(			__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      if len(__UpperCamelCase    ) == 0:
            return arr
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Node(arr[0]    )
      for i in range(1      ,len(__UpperCamelCase    )    ):
            root.insert(arr[i]    )
      # Traverse BST in order.
      SCREAMING_SNAKE_CASE   :      Dict    					=						[]
      inorder(__UpperCamelCase      ,__UpperCamelCase    )
      return res


if __name__ == "__main__":
      print(tree_sort([1_0, 1, 3, 2, 9, 1_4, 1_3]))


