

"""simple docstring"""





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()
_a       =							logging.get_logger(__name__)




def   _A  (  UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       Union[str, Any])	->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						RobertaPreLayerNormConfig.from_pretrained(
       lowercase__,			architectures=["RobertaPreLayerNormForMaskedLM"])

   # convert state_dict
   __lowercase								=						torch.load(hf_hub_download(repo_id=lowercase__,			filename="pytorch_model.bin"))
   __lowercase								=						{}
   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."):
         __lowercase								=						'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

      __lowercase								=						tensor_value

   __lowercase								=						RobertaPreLayerNormForMaskedLM.from_pretrained(
       pretrained_model_name_or_path=lowercase__,			config=lowercase__,			state_dict=lowercase__)
   model.save_pretrained(lowercase__)

   # convert tokenizer
   __lowercase								=						AutoTokenizer.from_pretrained(lowercase__)
   tokenizer.save_pretrained(lowercase__)


if __name__ == "__main__":
   _a       =							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.'
   )
   _a       =							parser.parse_args()
   convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)










"""simple docstring"""






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


lowercase__     						=						logging.get_logger(__name__)

lowercase__     						=						{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			"""ctrl"""
			lowerCamelCase__      =			["""past_key_values"""]
			lowerCamelCase__      =			{
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}
			def __init__(  self  ,      lowercase=246534  ,      lowercase=256  ,      lowercase=1280  ,      lowercase=8192  ,      lowercase=48  ,      lowercase=16  ,      lowercase=0.1  ,      lowercase=0.1  ,      lowercase=1E-6  ,      lowercase=0.02  ,      lowercase=True  ,      **lowercase  ,      ):
							_lowerCamelCase     :			Any    =    vocab_size
							_lowerCamelCase     :			Dict    =    n_positions
							_lowerCamelCase     :			Optional[int]    =    n_embd
							_lowerCamelCase     :			str    =    n_layer
							_lowerCamelCase     :			Union[str, Any]    =    n_head
							_lowerCamelCase     :			Any    =    dff
							_lowerCamelCase     :			int    =    resid_pdrop
							_lowerCamelCase     :			Dict    =    embd_pdrop
							_lowerCamelCase     :			Union[str, Any]    =    layer_norm_epsilon
							_lowerCamelCase     :			Tuple    =    initializer_range

							_lowerCamelCase     :			str    =    use_cache

							super().__init__(**lowercase  )