






import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class 			UpperCamelCase_				(      lowerCAmelCase__	, unittest.TestCase    ):



							'''simple docstring'''




							lowerCAmelCase													=			BertweetTokenizer
							lowerCAmelCase													=			False




							def 							_UpperCamelCase      (	self )	->   Dict:
														super().setUp()

														# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
														snake_case_           =							['''I''', '''m''', '''V@@''', '''R@@''', '''r''', '''e@@''']
														snake_case_           =							dict(zip(_A	,      range(len(_A ) ) ) )
														snake_case_           =							['''#version: 0.2''', '''a m</w>''']
														snake_case_           =							{'''unk_token''': '''<unk>'''}

														snake_case_           =							os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['vocab_file'] )
														snake_case_           =							os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['merges_file'] )
														with open(self.vocab_file	,      'w'	,      encoding='utf-8' ) as fp:
																					for token in vocab_tokens:
																												fp.write(F'''{token} {vocab_tokens[token]}\n''' )
														with open(self.merges_file	,      'w'	,      encoding='utf-8' ) as fp:
																					fp.write('\n'.join(_A ) )




							def 							_UpperCamelCase      (	self	,      **a )	->   List[Any]:
														kwargs.update(self.special_tokens_map )
														return BertweetTokenizer.from_pretrained(self.tmpdirname	,      **_A )




							def 							_UpperCamelCase      (	self	,      a )	->   Optional[int]:
														snake_case_           =							'''I am VinAI Research'''
														snake_case_           =							'''I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
														return input_text, output_text






							def 							_UpperCamelCase      (	self )	->   Union[str, Any]:
														snake_case_           =							BertweetTokenizer(self.vocab_file	,      self.merges_file	,      **self.special_tokens_map )
														snake_case_           =							'''I am VinAI Research'''
														snake_case_           =							'''I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
														snake_case_           =							tokenizer.tokenize(_A )
														self.assertListEqual(_A	,      _A )

														snake_case_           =							tokens + [tokenizer.unk_token]

														snake_case_           =							[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(_A )	,      _A )













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
	)





