


"""simple docstring"""
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 						lowercase				(	_snake_case		: Optional[int]	,  _snake_case		: Any	,  _snake_case		: str	,  _snake_case		: str	,  _snake_case		: List[str]	)	->Tuple:
	"""simple docstring"""





	__snake_case      ,				__snake_case      :		List[Any]         =							utils.load_model(_snake_case	,  _snake_case	,  _snake_case	,  epoch=_snake_case	,  device=_snake_case	)
	__snake_case      :		Optional[Any]         =							TrajectoryTransformerModel(gpt.config	)

	trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()	)
	__snake_case      :		Tuple         =							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__":
			SCREAMING_SNAKE_CASE					:       Any							   =							Parser().parse_args("""plan""")
			convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
			    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
			)








import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


lowercase_									=    set()
lowercase_									=    get_logger(__name__)
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[str] = None )				-> Optional[int]:





    '''simple docstring'''






    def decorator(SCREAMING_SNAKE_CASE__     :      Callable ):
        global _emitted_deprecation_warnings

        if inspect.isclass(SCREAMING_SNAKE_CASE__ ):
            A__        =  deprecated_class_or_function.__init__
            A__        =  deprecated_class_or_function.__name__
        else:
            A__        =  deprecated_class_or_function
            A__        =  deprecated_function.__name__
            # Support deprecating __init__ class method: class name instead
            A__        =  name if name != '__init__' else deprecated_function.__qualname__.split('.' )[-2]

        A__        =  (
            f'{name} is deprecated and will be removed in the next major version of datasets.' + f' {help_message}'
            if help_message
            else ''
        )

        @wraps(SCREAMING_SNAKE_CASE__ )
        def wrapper(*SCREAMING_SNAKE_CASE__     :      Tuple       ,     **SCREAMING_SNAKE_CASE__     :      Any ):
            A__        =  hash(SCREAMING_SNAKE_CASE__ )
            if func_hash not in _emitted_deprecation_warnings:
                warnings.warn(SCREAMING_SNAKE_CASE__       ,     category=SCREAMING_SNAKE_CASE__       ,     stacklevel=2 )
                _emitted_deprecation_warnings.add(SCREAMING_SNAKE_CASE__ )
            return deprecated_function(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__ )

        A__        =  'deprecated'

        if inspect.isclass(SCREAMING_SNAKE_CASE__ ):
            A__        =  wrapper
            return deprecated_class_or_function
        else:
            return wrapper

    return decorator


class 					A		(				enum.EnumMeta					):







    """simple docstring"""





    def __getattribute__( cls					:			Optional[Any],lowercase_					:			int						)->       Optional[Any]:





        '''simple docstring'''







        A__        =  super().__getattribute__(lowercase_						)
        if isinstance(lowercase_,enum.Enum						) and obj._on_access:
            obj._on_access()
        return obj


    def __getitem__( cls					:			int,lowercase_					:			Any						)->       Tuple:





        '''simple docstring'''







        A__        =  super().__getitem__(lowercase_						)
        if member._on_access:
            member._on_access()
        return member


    def __call__( cls					:			Optional[int],lowercase_					:			Tuple,lowercase_					:			List[Any]=None,*, lowercase_					:			Any=None,lowercase_					:			Optional[Any]=None,lowercase_					:			Any=None,lowercase_					:			Optional[int]=1						)->       List[str]:





        '''simple docstring'''







        A__        =  super().__call__(lowercase_,lowercase_,module=lowercase_,qualname=lowercase_,type=lowercase_,start=lowercase_						)
        if isinstance(lowercase_,enum.Enum						) and obj._on_access:
            obj._on_access()
        return obj




class 					A		(				enum.Enum       ,				metaclass=_UpperCAmelCase					):







    """simple docstring"""





    def __new__( cls					:			Any,lowercase_					:			str						)->       Any:





        '''simple docstring'''







        A__        =  object.__new__(cls						)
        A__        =  value
        A__        =  member.deprecate
        return member


    @property
    def 		snake_case__	( self					:			List[Any]						)->       List[Any]:





        '''simple docstring'''







        return ""


    def 		snake_case__	( self					:			List[str]						)->       Dict:





        '''simple docstring'''







        A__        =  F' {self.help_message}' if self.help_message else ''
        warnings.warn(
            F'\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'
            + help_message,lowercase_,stacklevel=3,)

