




'''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)





from __future__ import annotations

from typing import Any

class __UpperCamelCase     :






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= num_of_nodes
						UpperCAmelCase__:	list[list[int]]									= []
						UpperCAmelCase__:	dict[int, int]									= {}





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						self.m_edges.append([u_node, v_node, weight]						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):

						if self.m_component[u_node] == u_node:
							return u_node
						return self.find_component(self.m_component[u_node]						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):

						if self.m_component[u_node] != u_node:
							for k in self.m_component:
								UpperCAmelCase__:	Optional[Any]									= self.find_component(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):

						if component_size[u_node] <= component_size[v_node]:
							UpperCAmelCase__:	Tuple									= v_node
							component_size[v_node] += component_size[u_node]
							self.set_component(lowerCamelCase__						)

						elif component_size[u_node] >= component_size[v_node]:
							UpperCAmelCase__:	Dict									= self.find_component(lowerCamelCase__						)
							component_size[u_node] += component_size[v_node]
							self.set_component(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= []
						UpperCAmelCase__:	int									= 0

						UpperCAmelCase__:	list[Any]									= [-1] * self.m_num_of_nodes

						# A list of components (initialized to all of the nodes)
						for node in range(self.m_num_of_nodes						):
							self.m_component.update({node: node}						)
							component_size.append(1						)

						UpperCAmelCase__:	Union[str, Any]									= self.m_num_of_nodes

						while num_of_components > 1:
							for edge in self.m_edges:
								UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	str									= edge

								UpperCAmelCase__:	str									= self.m_component[u]
								UpperCAmelCase__:	Optional[Any]									= self.m_component[v]

								if u_component != v_component:

									for component in (u_component, v_component):
										if (
										    minimum_weight_edge[component] == -1
										    or minimum_weight_edge[component][2] > w
										):
											UpperCAmelCase__:	List[Any]									= [u, v, w]

							for edge in minimum_weight_edge:
								if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
									UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	str									= edge

									UpperCAmelCase__:	str									= self.m_component[u]
									UpperCAmelCase__:	int									= self.m_component[v]

									if u_component != v_component:
										mst_weight += w
										self.union(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)
										print(F"Added edge [{u} - {v}]\nAdded weight: {w}\n"						)
										num_of_components -= 1

							UpperCAmelCase__:	Any									= [-1] * self.m_num_of_nodes
						print(F"The total weight of the minimal spanning tree is: {mst_weight}"						)



def 			_A	(      ):
	pass


if __name__ == "__main__":
					import doctest

					doctest.testmod()