






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


__UpperCamelCase      :      Tuple     						=						logging.get_logger(__name__)

__UpperCamelCase      :      List[str]     						=						{
    """edbeeching/decision-transformer-gym-hopper-medium""": (
        """https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:Union[str, Any]   =       'decision_transformer'
	__snake_case	:List[str]   =       ['past_key_values']
	__snake_case	:str   =       {
	    'max_position_embeddings': 'n_positions',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}







	def __init__(  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							List[str]=17       ,				_lowerCAmelCase      :							Optional[int]=4       ,				_lowerCAmelCase      :							Any=128       ,				_lowerCAmelCase      :							List[str]=4096       ,				_lowerCAmelCase      :							int=True       ,				_lowerCAmelCase      :							Optional[Any]=1       ,				_lowerCAmelCase      :							List[str]=1024       ,				_lowerCAmelCase      :							Union[str, Any]=3       ,				_lowerCAmelCase      :							List[str]=1       ,				_lowerCAmelCase      :							List[str]=None       ,				_lowerCAmelCase      :							Any="relu"       ,				_lowerCAmelCase      :							Optional[int]=0.1       ,				_lowerCAmelCase      :							Tuple=0.1       ,				_lowerCAmelCase      :							Optional[int]=0.1       ,				_lowerCAmelCase      :							List[str]=1e-5       ,				_lowerCAmelCase      :							str=0.02       ,				_lowerCAmelCase      :							List[Any]=True       ,				_lowerCAmelCase      :							List[Any]=True       ,				_lowerCAmelCase      :							List[str]=5_0256       ,				_lowerCAmelCase      :							Tuple=5_0256       ,				_lowerCAmelCase      :							Optional[int]=False       ,				_lowerCAmelCase      :							List[Any]=False       ,				**_lowerCAmelCase      :							str       ,				)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  state_dim
		__lowercase             =  act_dim
		__lowercase             =  hidden_size
		__lowercase             =  max_ep_len
		__lowercase             =  action_tanh
		__lowercase             =  vocab_size
		__lowercase             =  n_positions
		__lowercase             =  n_layer
		__lowercase             =  n_head
		__lowercase             =  n_inner
		__lowercase             =  activation_function
		__lowercase             =  resid_pdrop
		__lowercase             =  embd_pdrop
		__lowercase             =  attn_pdrop
		__lowercase             =  layer_norm_epsilon
		__lowercase             =  initializer_range
		__lowercase             =  scale_attn_weights
		__lowercase             =  use_cache
		__lowercase             =  scale_attn_by_inverse_layer_idx
		__lowercase             =  reorder_and_upcast_attn

		__lowercase             =  bos_token_id
		__lowercase             =  eos_token_id

		super().__init__(bos_token_id=_lowerCAmelCase       ,				eos_token_id=_lowerCAmelCase       ,				**_lowerCAmelCase			)









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      :      str     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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

	# convert state_dict
	__lowercase             =  torch.load(hf_hub_download(repo_id=lowerCamelCase				,  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=lowerCamelCase				,  config=lowerCamelCase				,  state_dict=lowerCamelCase					)
	model.save_pretrained(lowerCamelCase					)

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


if __name__ == "__main__":
						__UpperCamelCase      :      List[Any]     						=						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      :      Dict     						=						parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

