






'''simple docstring'''
class   __SCREAMING_SNAKE_CASE   :




 def __init__(   self						:				Any       ,   __lowercase						:				str       ,   __lowercase						:				Union[str, Any]=None       ,   __lowercase						:				str=None   )						->    int:
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=data
       SCREAMING_SNAKE_CASE__  :					Tuple						=previous
       SCREAMING_SNAKE_CASE__  :					Any						=next_node




 def __str__(   self						:				Dict   )						->    str:
       return F"{self.data}"




 def   __magic_name__						(   self						:				List[str]   )						->    int:
       return self.data




 def   __magic_name__						(   self						:				Optional[Any]   )						->    Dict:
       return self.next




 def   __magic_name__						(   self						:				List[Any]   )						->    Union[str, Any]:
       return self.previous






class   __SCREAMING_SNAKE_CASE   :




 def __init__(   self						:				Dict       ,   __lowercase						:				Union[str, Any]   )						->    List[str]:
       SCREAMING_SNAKE_CASE__  :					List[Any]						=head




 def __iter__(   self						:				Dict   )						->    Any:
       return self




 def   __magic_name__						(   self						:				List[str]   )						->    Dict:
       if not self.current:
             raise StopIteration
       else:
             SCREAMING_SNAKE_CASE__  :					Any						=self.current.get_data()
             SCREAMING_SNAKE_CASE__  :					Dict						=self.current.get_next()
             return value






class   __SCREAMING_SNAKE_CASE   :




 def __init__(   self						:				List[str]   )						->    Dict:
       SCREAMING_SNAKE_CASE__  :					Dict						=None  # First node in list
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=None  # Last node in list




 def __str__(   self						:				Optional[Any]   )						->    Any:
       SCREAMING_SNAKE_CASE__  :					str						=self.head
       SCREAMING_SNAKE_CASE__  :					int						=[]
       while current is not None:
             nodes.append(current.get_data()   )
             SCREAMING_SNAKE_CASE__  :					List[Any]						=current.get_next()
       return " ".join(str(lowerCAmelCase_   ) for node in nodes   )




 def __contains__(   self						:				Tuple       ,   __lowercase						:				str   )						->    Any:
       SCREAMING_SNAKE_CASE__  :					Any						=self.head
       while current:
             if current.get_data() == value:
                   return True
             SCREAMING_SNAKE_CASE__  :					int						=current.get_next()
       return False




 def __iter__(   self						:				Optional[Any]   )						->    str:
       return LinkedListIterator(self.head   )




 def   __magic_name__						(   self						:				List[str]   )						->    Dict:
       if self.head:
             return self.head.get_data()
       return None




 def   __magic_name__						(   self						:				str   )						->    List[Any]:
       if self.tail:
             return self.tail.get_data()
       return None




 def   __magic_name__						(   self						:				List[str]       ,   __lowercase						:				Any   )						->    None:
       if self.head is None:
             SCREAMING_SNAKE_CASE__  :					List[Any]						=node
             SCREAMING_SNAKE_CASE__  :					Dict						=node
       else:
             self.insert_before_node(self.head       ,   lowerCAmelCase_   )




 def   __magic_name__						(   self						:				Tuple       ,   __lowercase						:				Dict   )						->    None:
       if self.head is None:
             self.set_head(lowerCAmelCase_   )
       else:
             self.insert_after_node(self.tail       ,   lowerCAmelCase_   )




 def   __magic_name__						(   self						:				List[Any]       ,   __lowercase						:				Union[str, Any]   )						->    None:
       SCREAMING_SNAKE_CASE__  :					Any						=Node(lowerCAmelCase_   )
       if self.head is None:
             self.set_head(lowerCAmelCase_   )
       else:
             self.set_tail(lowerCAmelCase_   )




 def   __magic_name__						(   self						:				Optional[Any]       ,   __lowercase						:				str       ,   __lowercase						:				Union[str, Any]   )						->    None:
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=node
       SCREAMING_SNAKE_CASE__  :					List[Any]						=node.previous

       if node.get_previous() is None:
             SCREAMING_SNAKE_CASE__  :					List[Any]						=node_to_insert
       else:
             SCREAMING_SNAKE_CASE__  :					Optional[int]						=node_to_insert

       SCREAMING_SNAKE_CASE__  :					Any						=node_to_insert




 def   __magic_name__						(   self						:				List[Any]       ,   __lowercase						:				Dict       ,   __lowercase						:				int   )						->    None:
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=node
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=node.next

       if node.get_next() is None:
             SCREAMING_SNAKE_CASE__  :					Tuple						=node_to_insert
       else:
             SCREAMING_SNAKE_CASE__  :					Any						=node_to_insert

       SCREAMING_SNAKE_CASE__  :					List[str]						=node_to_insert




 def   __magic_name__						(   self						:				Tuple       ,   __lowercase						:				Optional[Any]       ,   __lowercase						:				List[str]   )						->    None:
       SCREAMING_SNAKE_CASE__  :					List[Any]						=1
       SCREAMING_SNAKE_CASE__  :					str						=Node(lowerCAmelCase_   )
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=self.head
       while node:
             if current_position == position:
                   self.insert_before_node(lowerCAmelCase_       ,   lowerCAmelCase_   )
                   return
             current_position += 1
             SCREAMING_SNAKE_CASE__  :					List[str]						=node.next
       self.insert_after_node(self.tail       ,   lowerCAmelCase_   )




 def   __magic_name__						(   self						:				Union[str, Any]       ,   __lowercase						:				Tuple   )						->    Node:
       SCREAMING_SNAKE_CASE__  :					Tuple						=self.head
       while node:
             if node.get_data() == item:
                   return node
             SCREAMING_SNAKE_CASE__  :					Dict						=node.get_next()
       raise Exception('''Node not found'''   )




 def   __magic_name__						(   self						:				int       ,   __lowercase						:				Tuple   )						->    int:
       if (node := self.get_node(lowerCAmelCase_   )) is not None:
             if node == self.head:
                   SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=self.head.get_next()

             if node == self.tail:
                   SCREAMING_SNAKE_CASE__  :					int						=self.tail.get_previous()

             self.remove_node_pointers(lowerCAmelCase_   )




 @staticmethod
 def   __magic_name__						(   __lowercase						:				List[Any]   )						->    None:
       if node.get_next():
             SCREAMING_SNAKE_CASE__  :					List[Any]						=node.previous

       if node.get_previous():
             SCREAMING_SNAKE_CASE__  :					str						=node.next

       SCREAMING_SNAKE_CASE__  :					Optional[int]						=None
       SCREAMING_SNAKE_CASE__  :					List[Any]						=None




 def   __magic_name__						(   self						:				Optional[int]   )						->    List[str]:
       return self.head is None





def 			_a(	):




      '''simple docstring'''






      pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()







"""simple docstring"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
			lowerCAmelCase__    						=			argparse.ArgumentParser(
			    description=(
			        'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
			        ' Distillation'
			    )
			)
			parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
			parser.add_argument('--model_name', default='roberta-large', type=str)
			parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
			parser.add_argument('--vocab_transform', action='store_true')
			lowerCAmelCase__    						=			parser.parse_args()

			if args.model_type == "roberta":
						lowerCAmelCase__    						=			RobertaForMaskedLM.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'roberta'
			elif args.model_type == "gpt2":
						lowerCAmelCase__    						=			GPTaLMHeadModel.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'transformer'

			lowerCAmelCase__    						=			model.state_dict()
			lowerCAmelCase__    						=			{}

			# Embeddings #
			if args.model_type == "gpt2":
						for param_name in ["wte.weight", "wpe.weight"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.{param_name}']
			else:
						for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.{w}.weight'
									lowerCAmelCase__    						=			state_dict[param_name]
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.LayerNorm.{w}'
									lowerCAmelCase__    						=			state_dict[param_name]

    # Transformer Blocks #
			lowerCAmelCase__    						=			0
			for teacher_idx in [0, 2, 4, 7, 9, 11]:
						if args.model_type == "gpt2":
									for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
												for w in ["weight", "bias"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
															]
									lowerCAmelCase__    						=			state_dict[F'{prefix}.h.{teacher_idx}.attn.bias']
						else:
									for layer in [
									    "attention.self.query",
									    "attention.self.key",
									    "attention.self.value",
									    "attention.output.dense",
									    "attention.output.LayerNorm",
									    "intermediate.dense",
									    "output.dense",
									    "output.LayerNorm",
									]:
												for w in ["weight", "bias"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
															]
						std_idx += 1

			# Language Modeling Head ###s
			if args.model_type == "roberta":
						for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
									lowerCAmelCase__    						=			state_dict[F'{layer}']
						if args.vocab_transform:
									for w in ["weight", "bias"]:
												lowerCAmelCase__    						=			state_dict[F'lm_head.dense.{w}']
												lowerCAmelCase__    						=			state_dict[F'lm_head.layer_norm.{w}']
			elif args.model_type == "gpt2":
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.ln_f.{w}']
						lowerCAmelCase__    						=			state_dict['lm_head.weight']

			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)

