



"""simple docstring"""




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel






class 			UpperCAmelCase_						(       utils.Parser		):
      __SCREAMING_SNAKE_CASE		:     str				  =				"halfcheetah-medium-expert-v2"
      __SCREAMING_SNAKE_CASE		:     str				  =				"config.offline"







def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		Dict		,						SCREAMING_SNAKE_CASE__			:		Any		,						SCREAMING_SNAKE_CASE__			:		str		,						SCREAMING_SNAKE_CASE__			:		int       )						->     List[str]:





     '''simple docstring'''

     _UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					utils.load_model(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						epoch=SCREAMING_SNAKE_CASE__		,						device=SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			Tuple              =					TrajectoryTransformerModel(gpt.config       )

     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()       )
     _UpperCAmelCase    :			Optional[int]              =					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__":
 _lowerCAmelCase       :      List[str]							=							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"""




import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Tuple		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Dict:





     '''simple docstring'''

     _UpperCAmelCase    :			Any              =					{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
     _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			str              =					MODELS[model_size]  # set model hyperparameters
     _UpperCAmelCase    :			Tuple              =					ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE__		,						n_layer=SCREAMING_SNAKE_CASE__		,						n_head=SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			int              =					ImageGPTForCausalLM(SCREAMING_SNAKE_CASE__       )

     # Load weights from numpy
     load_tf_weights_in_imagegpt(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )

     # Save pytorch-model
     _UpperCAmelCase    :			Union[str, Any]              =					pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     _UpperCAmelCase    :			int              =					pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(f'Save PyTorch model to {pytorch_weights_dump_path}'       )
     torch.save(model.state_dict()		,						SCREAMING_SNAKE_CASE__       )
     print(f'Save configuration file to {pytorch_config_dump_path}'       )
     with open(SCREAMING_SNAKE_CASE__		,						"w"		,						encoding="utf-8"       ) as f:
          f.write(config.to_json_string()       )


if __name__ == "__main__":
 _lowerCAmelCase       :      List[str]							=							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--imagegpt_checkpoint_path",
     default=None,
     type=str,
     required=True,
     help="Path to the TensorFlow checkpoint path.",
 )
 parser.add_argument(
     "--model_size",
     default=None,
     type=str,
     required=True,
     help="Size of the model (can be either 'small', 'medium' or 'large').",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 _lowerCAmelCase       :      Optional[int]							=							parser.parse_args()
 convert_imagegpt_checkpoint_to_pytorch(
     args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
 )
