




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class 			_lowerCamelCase    (      utils.Parser ):

      """simple docstring"""

      lowerCAmelCase__      :			str     =       "halfcheetah-medium-expert-v2"
      lowerCAmelCase__      :			str     =       "config.offline"







def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    utils.load_model(lowercase_       ,  lowercase_       ,  lowercase_       ,  epoch=lowercase_       ,  device=lowercase_   )
    __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__":
 a_ 		=  Parser().parse_args("plan")
 convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
     args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
 )











'''simple docstring'''


from typing import TYPE_CHECKING

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


lowercase											=      {
    '''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:
  lowercase											=      [
      '''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

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


