



import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel







class      __lowerCAmelCase       (     utils.Parser		):
			UpperCamelCase     =	"halfcheetah-medium-expert-v2"
			UpperCamelCase     =	"config.offline"





def 				A      (	_UpperCAmelCase							:  int				,  _UpperCAmelCase							:  int				,  _UpperCAmelCase							:  List[Any]				,  _UpperCAmelCase							:  Union[str, Any]				,  _UpperCAmelCase							:  List[str]       )							->					List[Any]:







			'''simple docstring'''
			_UpperCAmelCase			,	_UpperCAmelCase									=  utils.load_model(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  epoch=_UpperCAmelCase				,  device=_UpperCAmelCase       )
			_UpperCAmelCase									=  TrajectoryTransformerModel(gpt.config       )

			trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()       )
			_UpperCAmelCase									=  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__":
							UpperCAmelCase__  =			Parser().parse_args("plan")
							convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
							    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
							)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase__  =			{
    "configuration_align": [
        "ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AlignConfig",
        "AlignTextConfig",
        "AlignVisionConfig",
    ],
    "processing_align": ["AlignProcessor"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  =			[
							    "ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "AlignModel",
							    "AlignPreTrainedModel",
							    "AlignTextModel",
							    "AlignVisionModel",
							]

if TYPE_CHECKING:
							from .configuration_align import (
							    ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    AlignConfig,
							    AlignTextConfig,
							    AlignVisionConfig,
							)
							from .processing_align import AlignProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_align import (
														    ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AlignModel,
														    AlignPreTrainedModel,
														    AlignTextModel,
														    AlignVisionModel,
														)

else:
							import sys

							UpperCAmelCase__  =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
