




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel





class  __lowerCamelCase       (    utils.Parser    ):




    """simple docstring"""

    snake_case__					=      "halfcheetah-medium-expert-v2"
    snake_case__					=      "config.offline"






def 							_A   (						lowerCAmelCase_     :					Tuple   ,				lowerCAmelCase_     :					Optional[Any]   ,				lowerCAmelCase_     :					Tuple   ,				lowerCAmelCase_     :					Tuple   ,				lowerCAmelCase_     :					List[Any]			):
     """simple docstring"""

     lowerCAmelCase__	,		lowerCAmelCase__		= utils.load_model(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				epoch=lowerCAmelCase_   ,				device=lowerCAmelCase_			)
     lowerCAmelCase__		= TrajectoryTransformerModel(gpt.config			)

     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
     lowerCAmelCase__		= 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__":
     UpperCamelCase								=     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 os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





@require_tokenizers
class  __lowerCamelCase       (    unittest.TestCase    ):




    """simple docstring"""

    snake_case__					=      EsmTokenizer


    def    a      (       self     :     str   )      ->       Tuple:
         super().setUp()
         lowerCAmelCase__		= tempfile.mkdtemp()
         # fmt: off
         lowerCAmelCase__		= ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
         # fmt: on
         lowerCAmelCase__		= os.path.join(self.tmpdirname   ,	VOCAB_FILES_NAMES["vocab_file"]   )
         with open(self.vocab_file   ,	"w"   ,	encoding="utf-8"   ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )


    def    a      (       self     :     Dict   ,	**SCREAMING_SNAKE_CASE__     :     Optional[Any]   )      ->       List[PreTrainedTokenizerBase]:
         return [self.get_tokenizer(**SCREAMING_SNAKE_CASE__   )]


    def    a      (       self     :     List[str]   ,	**SCREAMING_SNAKE_CASE__     :     str   )      ->       PreTrainedTokenizer:
         return self.tokenizer_class.from_pretrained(self.tmpdirname   ,	**SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     str   )      ->       Tuple:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= tokenizer.tokenize("LAGVS"   )
         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	["L", "A", "G", "V", "S"]   )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__   )   ,	[4, 5, 6, 7, 8]   )


    def    a      (       self     :     int   )      ->       Union[str, Any]:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= "LAGVS"
         self.assertListEqual(tokenizer.encode(SCREAMING_SNAKE_CASE__   )   ,	[0, 4, 5, 6, 7, 8, 2]   )


    def    a      (       self     :     Union[str, Any]   )      ->       str:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= ["LAGVS", "WCB"]
         lowerCAmelCase__		= tokenizer(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_SNAKE_CASE__   )["input_ids"]

         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]   )


    def    a      (       self     :     Any   )      ->       Union[str, Any]:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= ["LAGVS", "WCB"]
         lowerCAmelCase__		= tokenizer(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_SNAKE_CASE__   )["input_ids"]

         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]   )


    def    a      (       self     :     str   )      ->       Optional[Any]:
         lowerCAmelCase__		= self.get_tokenizers(fast=SCREAMING_SNAKE_CASE__   )
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                   lowerCAmelCase__		= "<unk>"
                   lowerCAmelCase__		= "<mask>"

                   lowerCAmelCase__		= tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
                   lowerCAmelCase__		= tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )

                   self.assertEqual(len(SCREAMING_SNAKE_CASE__   )   ,	1   )
                   self.assertEqual(len(SCREAMING_SNAKE_CASE__   )   ,	1   )
                   self.assertEqual(token_a[0]   ,	SCREAMING_SNAKE_CASE__   )
                   self.assertEqual(token_a[0]   ,	SCREAMING_SNAKE_CASE__   )
