


"""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 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 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						List[str]:
			# Construct configuration depending on size
			lowerCAmelCase__ : Tuple   =						{'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__ : Optional[int]   =						MODELS[model_size]  # set model hyperparameters
			lowerCAmelCase__ : Optional[int]   =						ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE_			,				n_layer=SCREAMING_SNAKE_CASE_			,				n_head=SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : List[Any]   =						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
			lowerCAmelCase__ : Optional[int]   =						pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			lowerCAmelCase__ : Optional[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__        =						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__        =						parser.parse_args()
					convert_imagegpt_checkpoint_to_pytorch(
					    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
					)