"""simple docstring"""






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


_A   					=    logging.get_logger(__name__)

_A   					=    {
    'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
        'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		"trajectory_transformer"
					a   	=		["past_key_values"]
					a   	=		{
					    "hidden_size": "n_embd",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}



					def __init__(						self			:				List[str]   , _snake_case			:				Tuple=100   , _snake_case			:				List[str]=5   , _snake_case			:				Optional[int]=1   , _snake_case			:				Optional[int]=1   , _snake_case			:				Optional[int]=249   , _snake_case			:				Optional[Any]=6   , _snake_case			:				Optional[int]=17   , _snake_case			:				str=25   , _snake_case			:				Union[str, Any]=4   , _snake_case			:				Union[str, Any]=4   , _snake_case			:				Optional[Any]=128   , _snake_case			:				Any=0.1   , _snake_case			:				Any=0.1   , _snake_case			:				Optional[Any]=0.1   , _snake_case			:				Tuple=0.0006   , _snake_case			:				List[Any]=512   , _snake_case			:				Union[str, Any]=0.02   , _snake_case			:				str=1e-12   , _snake_case			:				Tuple=1   , _snake_case			:				Tuple=True   , _snake_case			:				str=1   , _snake_case			:				Any=50256   , _snake_case			:				str=50256   , **_snake_case			:				Dict   , )							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				vocab_size
									SCREAMING_SNAKE_CASE__		  =				action_weight
									SCREAMING_SNAKE_CASE__		  =				reward_weight
									SCREAMING_SNAKE_CASE__		  =				value_weight
									SCREAMING_SNAKE_CASE__		  =				max_position_embeddings
									SCREAMING_SNAKE_CASE__		  =				block_size
									SCREAMING_SNAKE_CASE__		  =				action_dim
									SCREAMING_SNAKE_CASE__		  =				observation_dim
									SCREAMING_SNAKE_CASE__		  =				transition_dim
									SCREAMING_SNAKE_CASE__		  =				learning_rate
									SCREAMING_SNAKE_CASE__		  =				n_layer
									SCREAMING_SNAKE_CASE__		  =				n_head
									SCREAMING_SNAKE_CASE__		  =				n_embd
									SCREAMING_SNAKE_CASE__		  =				embd_pdrop
									SCREAMING_SNAKE_CASE__		  =				attn_pdrop
									SCREAMING_SNAKE_CASE__		  =				resid_pdrop
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				layer_norm_eps
									SCREAMING_SNAKE_CASE__		  =				kaiming_initializer_range
									SCREAMING_SNAKE_CASE__		  =				use_cache
									super().__init__(pad_token_id=_snake_case   , bos_token_id=_snake_case   , eos_token_id=_snake_case   , **_snake_case		)





"""simple docstring"""






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_A   					=    ['small', 'medium', 'large']

_A   					=    'lm_head.decoder.weight'
_A   					=    'lm_head.weight'




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				d.pop(__UpperCAmelCase       )
				os.makedirs(__UpperCAmelCase  ,					exist_ok=__UpperCAmelCase       )
				torch.save(__UpperCAmelCase  ,					os.path.join(__UpperCAmelCase  ,					__UpperCAmelCase       )       )


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()
						parser.add_argument('--dialogpt_path', default='.', type=str)
						_A   					=    parser.parse_args()
						for MODEL in DIALOGPT_MODELS:
												_A   					=    os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
												_A   					=    F'./DialoGPT-{MODEL}'
												convert_dialogpt_checkpoint(
												    checkpoint_path,
												    pytorch_dump_folder_path,
												)




