



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_		 =					logging.get_logger(__name__)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:str				)  ->     str:
   _A      =       RobertaPreLayerNormConfig.from_pretrained(
       _snake_case	,   architectures=['''RobertaPreLayerNormForMaskedLM''']				)

   # convert state_dict
   _A      =       torch.load(hf_hub_download(repo_id=_snake_case	,   filename='''pytorch_model.bin'''				)				)
   _A      =       {}
   for tensor_key, tensor_value in original_state_dict.items():
      # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
      if tensor_key.startswith('''roberta.'''				):
         _A      =       '''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''				) :]

      # The original implementation contains weights which are not used, remove them from the state_dict
      if tensor_key.endswith('''.self.LayerNorm.weight'''				) or tensor_key.endswith('''.self.LayerNorm.bias'''				):
         continue

      _A      =       tensor_value

   _A      =       RobertaPreLayerNormForMaskedLM.from_pretrained(
       pretrained_model_name_or_path=_snake_case	,   config=_snake_case	,   state_dict=_snake_case				)
   model.save_pretrained(_snake_case				)

   # convert tokenizer
   _A      =       AutoTokenizer.from_pretrained(_snake_case				)
   tokenizer.save_pretrained(_snake_case				)


if __name__ == "__main__":
   UpperCAmelCase_		 =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--checkpoint-repo""",
       default=None,
       type=str,
       required=True,
       help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   UpperCAmelCase_		 =					parser.parse_args()
   convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					{"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: int =  "openai-gpt"
 a__					: Dict =  {
     "max_position_embeddings": "n_positions",
     "hidden_size": "n_embd",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					int=4_04_78			,						__lowerCAmelCase					:					Tuple=5_12			,						__lowerCAmelCase					:					str=7_68			,						__lowerCAmelCase					:					List[Any]=12			,						__lowerCAmelCase					:					Any=12			,						__lowerCAmelCase					:					Dict="gelu"			,						__lowerCAmelCase					:					Union[str, Any]=0.1			,						__lowerCAmelCase					:					Optional[int]=0.1			,						__lowerCAmelCase					:					Any=0.1			,						__lowerCAmelCase					:					List[str]=1E-5			,						__lowerCAmelCase					:					Optional[int]=0.02			,						__lowerCAmelCase					:					Optional[Any]="cls_index"			,						__lowerCAmelCase					:					str=True			,						__lowerCAmelCase					:					int=None			,						__lowerCAmelCase					:					Optional[int]=True			,						__lowerCAmelCase					:					Optional[Any]=0.1			,						**__lowerCAmelCase					:					Tuple			,						)   ->	Optional[Any]:
    _A      =       vocab_size
    _A      =       n_positions
    _A      =       n_embd
    _A      =       n_layer
    _A      =       n_head
    _A      =       afn
    _A      =       resid_pdrop
    _A      =       embd_pdrop
    _A      =       attn_pdrop
    _A      =       layer_norm_epsilon
    _A      =       initializer_range
    _A      =       summary_type
    _A      =       summary_use_proj
    _A      =       summary_activation
    _A      =       summary_first_dropout
    _A      =       summary_proj_to_labels
    super().__init__(**__lowerCAmelCase      )



