






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


lowercase  							= logging.get_logger(__name__)

lowercase  							= {
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}




class 			UpperCamelCase_				(      snake_case_    ):



							'''simple docstring'''




							lowerCAmelCase													=			'''qdqbert'''




							def __init__(	self	,      a=3_05_22	,      a=7_68	,      a=12	,      a=12	,      a=30_72	,      a="gelu"	,      a=0.1	,      a=0.1	,      a=5_12	,      a=2	,      a=0.02	,      a=1E-12	,      a=True	,      a=1	,      a=0	,      a=2	,      **a	,      )	->   Tuple:
														super().__init__(pad_token_id=a	,      bos_token_id=a	,      eos_token_id=a	,      **a )

														snake_case_           =							vocab_size
														snake_case_           =							max_position_embeddings
														snake_case_           =							hidden_size
														snake_case_           =							num_hidden_layers
														snake_case_           =							num_attention_heads
														snake_case_           =							intermediate_size
														snake_case_           =							hidden_act
														snake_case_           =							hidden_dropout_prob
														snake_case_           =							attention_probs_dropout_prob
														snake_case_           =							initializer_range
														snake_case_           =							type_vocab_size
														snake_case_           =							layer_norm_eps
														snake_case_           =							use_cache













import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 			UpperCamelCase_				(      utils.Parser    ):



	'''simple docstring'''




	lowerCAmelCase													=			"halfcheetah-medium-expert-v2"
	lowerCAmelCase													=			"config.offline"






def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_							,					a_):
							snake_case_      ,		snake_case_           =							utils.load_model(a_							,					a_							,					a_							,					epoch=a_							,					device=a_)
							snake_case_           =							TrajectoryTransformerModel(gpt.config)

							trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict())
							snake_case_           =							gpt.pos_emb
							trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict())
							trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict())
							trajectory_transformer.head.load_state_dict(gpt.head.state_dict())

							for i, block in enumerate(gpt.blocks):
														trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict())
														trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict())
														trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict())

														trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict())
														trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict())
														trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict())
														trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict())

							torch.save(trajectory_transformer.state_dict()							,					'pytorch_model.bin')


if __name__ == "__main__":
	lowercase  							= Parser().parse_args("plan")
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)





