






'''simple docstring'''





import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel





class 		SCREAMING_SNAKE_CASE__   (     utils.Parser    ):
		_UpperCAmelCase         ='''halfcheetah-medium-expert-v2'''
		_UpperCAmelCase         ='''config.offline'''





def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						a_     ,       a_				     =   utils.load_model(lowercase__	,lowercase__	,lowercase__	,epoch=lowercase__	,device=lowercase__				)
						a_				     =   TrajectoryTransformerModel(gpt.config				)

						trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()				)
						a_				     =   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 List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
							import torch

a_      = logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
							from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):



							def 			__UpperCAmelCase				(cls				)		->				Optional[Any]:

													'''simple docstring'''


													return cls



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,lowercase__ = None	,)		->				Union[str, Any]:

						'''simple docstring'''


						a_				     =   device
						a_				     =   shape[0]

						a_				     =   layout or torch.strided
						a_				     =   device or torch.device("cpu"				)

						if generator is not None:
												a_				     =   generator.device.type if not isinstance(lowercase__	,lowercase__				) else generator[0].device.type
												if gen_device_type != device.type and gen_device_type == "cpu":
																		a_				     =   "cpu"
																		if device != "mps":
																								logger.info(
																								    F"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																								    F""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																								    F""" slighly speed up this function by passing a generator that was created on the {device} device."""				)
												elif gen_device_type != device.type and gen_device_type == "cuda":
																		raise ValueError(F"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""				)

						if isinstance(lowercase__	,lowercase__				):
												a_				     =   (1,) + shape[1:]
												a_				     =   [
												    torch.randn(lowercase__	,generator=generator[i]	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				)
												    for i in range(lowercase__				)
												]
												a_				     =   torch.cat(lowercase__	,dim=0				).to(lowercase__				)
						else:
												a_				     =   torch.randn(lowercase__	,generator=lowercase__	,device=lowercase__	,dtype=lowercase__	,layout=lowercase__				).to(lowercase__				)

						return latents



def 			__UpperCAmelCase				(lowercase__				)		->				Tuple:

						'''simple docstring'''


						if is_torch_version("<"	,"2.0.0"				) or not hasattr(lowercase__	,"_dynamo"				):
												return False
						return isinstance(lowercase__	,torch._dynamo.eval_frame.OptimizedModule				)



