






# Lint as: python3

import hashlib
from typing import Union





def 						__UpperCAmelCase      (					a_):
							if isinstance(a_							,					a_):
														# Data already in bytes, returns as it as
														return hash_data
							elif isinstance(a_							,					a_):
														# We keep the data as it as for it ot be later encoded to UTF-8
														# However replace `\\` with `/` for Windows compatibility
														snake_case_           =							hash_data.replace('\\'							,					'/')
							elif isinstance(a_							,					a_):
														snake_case_           =							str(a_)
							else:
														# If data is not of the required type, raise error
														raise InvalidKeyError(a_)

							return hash_data.encode('utf-8')




class 			UpperCamelCase_				(      snake_case_    ):



	'''simple docstring'''




	def __init__(	self	,      a )	->   Union[str, Any]:
								snake_case_           =							'\nFAILURE TO GENERATE DATASET: Invalid key type detected'
								snake_case_           =							F'''\nFound Key {hash_data} of type {type(a )}'''
								snake_case_           =							'\nKeys should be either str, int or bytes type'
								super().__init__(F'''{self.prefix}{self.err_msg}{self.suffix}''' )






class 			UpperCamelCase_				(      snake_case_    ):



	'''simple docstring'''




	def __init__(	self	,      a	,      a	,      a="" )	->   Union[str, Any]:
								snake_case_           =							key
								snake_case_           =							duplicate_key_indices
								snake_case_           =							fix_msg
								snake_case_           =							'Found multiple examples generated with the same key'
								if len(a ) <= 20:
															snake_case_           =							F'''\nThe examples at index {", ".join(a )} have the key {key}'''
								else:
															snake_case_           =							F'''\nThe examples at index {", ".join(duplicate_key_indices[:20] )}... ({len(a ) - 20} more) have the key {key}'''
								snake_case_           =							'\n' + fix_msg if fix_msg else ''
								super().__init__(F'''{self.prefix}{self.err_msg}{self.suffix}''' )






class 			UpperCamelCase_				:



	'''simple docstring'''




	def __init__(	self	,      a )	->   Dict:
								snake_case_           =							hashlib.mda(_as_bytes(a ) )






	def 							_UpperCamelCase      (	self	,      a )	->   int:
								snake_case_           =							self._split_mda.copy()
								snake_case_           =							_as_bytes(a )
								mda.update(a )
								# Convert to integer with hexadecimal conversion
								return int(mda.hexdigest()	,      16 )













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 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_							,					a_):
							snake_case_      ,		snake_case_           =							utils.load_model(a_							,					a_							,					a_							,					epoch=a_							,					device=a_)
							snake_case_           =							TrajectoryTransformerModel(gpt.config)

							trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict())
							snake_case_           =							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__":
	lowercase  							= Parser().parse_args("plan")
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)





