


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)






from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Union[str, Any]     ,  lowercase_		:	NestedDataStructureLike[PathLike]     ,  lowercase_		:	Optional[NamedSplit] = None     ,  lowercase_		:	Optional[Features] = None     ,  lowercase_		:	str = None     ,  lowercase_		:	bool = False     ,  lowercase_		:	bool = False     ,  lowercase_		:	Optional[int] = None     ,  **lowercase_		:	Optional[int]     ,  )   ->		Union[str, Any]:
             """simple docstring"""




             super().__init__(
                 lowercase_     ,  split=lowercase_     ,  features=lowercase_     ,  cache_dir=lowercase_     ,  keep_in_memory=lowercase_     ,  streaming=lowercase_     ,  num_proc=lowercase_     ,  **lowercase_     ,  )
             _lowerCamelCase			:						Optional[int]            =path_or_paths if isinstance(lowercase_     ,  lowercase_					) else {self.split: path_or_paths}
             _lowerCamelCase			:						int            =Text(
                 cache_dir=lowercase_     ,  data_files=lowercase_     ,  features=lowercase_     ,  **lowercase_     ,  )






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Dict:
             """simple docstring"""




             if self.streaming:
                    _lowerCamelCase			:						Optional[Any]            =self.builder.as_streaming_dataset(split=self.split					)
             # Build regular (map-style) dataset
             else:
                    _lowerCamelCase			:						List[str]            =None
                    _lowerCamelCase			:						int            =None
                    _lowerCamelCase			:						Optional[Any]            =None
                    _lowerCamelCase			:						Any            =None

                    self.builder.download_and_prepare(
                        download_config=lowercase_     ,  download_mode=lowercase_     ,  verification_mode=lowercase_     ,  base_path=lowercase_     ,  num_proc=self.num_proc     ,  )
                    _lowerCamelCase			:						Tuple            =self.builder.as_dataset(
                        split=self.split     ,  verification_mode=lowercase_     ,  in_memory=self.keep_in_memory					)
             return dataset


