






"""simple docstring"""


from typing import TYPE_CHECKING

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


lowercase_								=	{
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'GPTSanJapaneseForConditionalGeneration',
           'GPTSanJapaneseModel',
           'GPTSanJapanesePreTrainedModel',
       ]
       lowercase_								=	[
           'GPTSanJapaneseTokenizer',
       ]


if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptsan_japanese import (
                  GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTSanJapaneseForConditionalGeneration,
                  GPTSanJapaneseModel,
                  GPTSanJapanesePreTrainedModel,
              )
              from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
       import sys

       lowercase_								=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










"""simple docstring"""

import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel
class      A__   (   utils.Parser  ):
	A_				:					str      =   "halfcheetah-medium-expert-v2"
	A_				:					str      =   "config.offline"




def    snake_case__						(							lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]		):
					__snake_case   ,   __snake_case        =						utils.load_model(lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase					,       epoch=lowerCAmelCase					,       device=lowerCAmelCase		)
					__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__":
		UpperCamelCase__			:     int			       =				Parser().parse_args("plan")
		convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
		    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
		)


