






'''simple docstring'''
import copy
import re






class   __SCREAMING_SNAKE_CASE   :
 snake_case_					=				'hp'
 snake_case_					=				{}
 snake_case_					=				None




 @classmethod
 def   __magic_name__						(   cls						:				Tuple       ,   __lowercase						:				List[Any]       ,   __lowercase						:				Optional[Any]   )						->    Tuple:
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=prefix
       SCREAMING_SNAKE_CASE__  :					Dict						=defaults
       cls.build_naming_info()




 @staticmethod
 def   __magic_name__						(   __lowercase						:				int       ,   __lowercase						:				List[Any]   )						->    Optional[Any]:
       if len(lowerCAmelCase_   ) == 0:
             return ""
       SCREAMING_SNAKE_CASE__  :					List[str]						=None
       if any(char.isdigit() for char in word   ):
             raise Exception(F"Parameters should not contain numbers: '{word}' contains a number"   )
       if word in info["short_word"]:
             return info["short_word"][word]
       for prefix_len in range(1       ,   len(lowerCAmelCase_   ) + 1   ):
             SCREAMING_SNAKE_CASE__  :					List[str]						=word[:prefix_len]
             if prefix in info["reverse_short_word"]:
                   continue
             else:
                   SCREAMING_SNAKE_CASE__  :					Optional[Any]						=prefix
                   break

       if short_word is None:
             # Paranoid fallback
             def int_to_alphabetic(__lowercase						:				Optional[int]   ):
                   SCREAMING_SNAKE_CASE__  :					str						=''
                   while integer != 0:
                         SCREAMING_SNAKE_CASE__  :					Optional[Any]						=chr(ord('''A'''   ) + integer % 10   ) + s
                         integer //= 10
                   return s

             SCREAMING_SNAKE_CASE__  :					List[str]						=0
             while True:
                   SCREAMING_SNAKE_CASE__  :					Optional[Any]						=word + '#' + int_to_alphabetic(lowerCAmelCase_   )
                   if sword in info["reverse_short_word"]:
                         continue
                   else:
                         SCREAMING_SNAKE_CASE__  :					int						=sword
                         break

       SCREAMING_SNAKE_CASE__  :					str						=short_word
       SCREAMING_SNAKE_CASE__  :					List[Any]						=word
       return short_word




 @staticmethod
 def   __magic_name__						(   __lowercase						:				List[str]       ,   __lowercase						:				int   )						->    str:
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=param_name.split('''_'''   )

       SCREAMING_SNAKE_CASE__  :					Optional[int]						=[TrialShortNamer.shortname_for_word(lowerCAmelCase_       ,   lowerCAmelCase_   ) for word in words]

       # We try to create a separatorless short name, but if there is a collision we have to fallback
       # to a separated short name
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=['', '_']

       for separator in separators:
             SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=separator.join(lowerCAmelCase_   )
             if shortname not in info["reverse_short_param"]:
                   SCREAMING_SNAKE_CASE__  :					str						=shortname
                   SCREAMING_SNAKE_CASE__  :					List[Any]						=param_name
                   return shortname

       return param_name




 @staticmethod
 def   __magic_name__						(   __lowercase						:				Union[str, Any]       ,   __lowercase						:				Any   )						->    Dict:
       SCREAMING_SNAKE_CASE__  :					str						=TrialShortNamer.shortname_for_key(lowerCAmelCase_       ,   lowerCAmelCase_   )
       SCREAMING_SNAKE_CASE__  :					int						=short_name
       SCREAMING_SNAKE_CASE__  :					List[str]						=param_name




 @classmethod
 def   __magic_name__						(   cls						:				str   )						->    List[Any]:
       if cls.NAMING_INFO is not None:
             return

       SCREAMING_SNAKE_CASE__  :					Optional[Any]						={
           'short_word': {},
           'reverse_short_word': {},
           'short_param': {},
           'reverse_short_param': {},
       }

       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=list(cls.DEFAULTS.keys()   )

       for k in field_keys:
             cls.add_new_param_name(lowerCAmelCase_       ,   lowerCAmelCase_   )

       SCREAMING_SNAKE_CASE__  :					Tuple						=info




 @classmethod
 def   __magic_name__						(   cls						:				Any       ,   __lowercase						:				Any   )						->    Optional[int]:
       cls.build_naming_info()
       assert cls.PREFIX is not None
       SCREAMING_SNAKE_CASE__  :					List[Any]						=[copy.copy(cls.PREFIX   )]

       for k, v in params.items():
             if k not in cls.DEFAULTS:
                   raise Exception(F"You should provide a default value for the param name {k} with value {v}"   )
             if v == cls.DEFAULTS[k]:
                   # The default value is not added to the name
                   continue

             SCREAMING_SNAKE_CASE__  :					Optional[Any]						=cls.NAMING_INFO['short_param'][k]

             if isinstance(lowerCAmelCase_       ,   lowerCAmelCase_   ):
                   SCREAMING_SNAKE_CASE__  :					List[str]						=1 if v else 0

             SCREAMING_SNAKE_CASE__  :					int						='' if isinstance(lowerCAmelCase_       ,   (int, float)   ) else '-'
             SCREAMING_SNAKE_CASE__  :					str						=F"{key}{sep}{v}"
             name.append(lowerCAmelCase_   )

       return "_".join(lowerCAmelCase_   )




 @classmethod
 def   __magic_name__						(   cls						:				Union[str, Any]       ,   __lowercase						:				Optional[int]   )						->    List[Any]:
       SCREAMING_SNAKE_CASE__  :					List[str]						=repr[len(cls.PREFIX   ) + 1 :]
       if repr == "":
             SCREAMING_SNAKE_CASE__  :					Any						=[]
       else:
             SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=repr.split('''_'''   )

       SCREAMING_SNAKE_CASE__  :					List[Any]						={}

       for value in values:
             if "-" in value:
                   SCREAMING_SNAKE_CASE__  :					Optional[Any]						=value.split('''-'''   )
             else:
                   SCREAMING_SNAKE_CASE__  :					List[str]						=re.sub('''[0-9.]'''       ,   ''''''       ,   lowerCAmelCase_   )
                   SCREAMING_SNAKE_CASE__  :					List[Any]						=float(re.sub('''[^0-9.]'''       ,   ''''''       ,   lowerCAmelCase_   )   )

             SCREAMING_SNAKE_CASE__  :					Tuple						=cls.NAMING_INFO['reverse_short_param'][p_k]

             SCREAMING_SNAKE_CASE__  :					str						=p_v

       for k in cls.DEFAULTS:
             if k not in parameters:
                   SCREAMING_SNAKE_CASE__  :					List[str]						=cls.DEFAULTS[k]

       return parameters







"""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)

