"""simple docstring"""




import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin




class 				__lowerCAmelCase					(    a_,		unittest.TestCase  ):



 '''simple docstring'''
 _lowerCamelCase  :		Dict			      =	SpeechaTextaTokenizer
 _lowerCamelCase  :		Dict			      =	False

 def    __snake_case   (     self    :						Tuple					)	->							List[str]:
        super().setUp()

        _UpperCamelCase       :    str					  =					'<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '					)
        _UpperCamelCase       :    List[Any]					  =					['he re</w> 123', 'here a 1456']
        _UpperCamelCase       :    int					  =					dict(zip(lowerCamelCase    ,     range(len(lowerCamelCase					)					)					)					)

        _UpperCamelCase       :    int					  =					{'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

        _UpperCamelCase       :    List[str]					  =					tempfile.mkdtemp()
        _UpperCamelCase       :    List[Any]					  =					os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['vocab_file']					)
        _UpperCamelCase       :    List[Any]					  =					os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['merges_file']					)

        with open(self.vocab_file    ,     'w'    ,     encoding='utf-8'					) as fp:
               fp.write(json.dumps(lowerCamelCase					) + '\n'					)

        with open(self.merges_file    ,     'w'					) as fp:
               fp.write('\n'.join(lowerCamelCase					)					)

 def    __snake_case   (     self    :						Optional[int]					)	->							Union[str, Any]:
        _UpperCamelCase       :    Optional[Any]					  =					list(self.get_tokenizer().get_vocab().keys()					)

        self.assertEqual(vocab_keys[0]    ,     '<s>'					)
        self.assertEqual(vocab_keys[1]    ,     '<pad>'					)
        self.assertEqual(vocab_keys[-1]    ,     'vocab'					)
        self.assertEqual(len(lowerCamelCase					)    ,     1_4					)

 def    __snake_case   (     self    :						Optional[Any]					)	->							Any:
        self.assertEqual(self.get_tokenizer().vocab_size    ,     1_4					)

 def    __snake_case   (     self    :						Optional[int]					)	->							Optional[int]:
        _UpperCamelCase       :    Optional[Any]					  =					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

        # make sure @@ is correctly concatenated
        _UpperCamelCase       :    Optional[Any]					  =					[4, 6, 8, 7, 1_0]  # ["here@@", "couple", "words", "of@@", "the"]
        _UpperCamelCase       :    Dict					  =					tokenizer.decode(lowerCamelCase					)

        self.assertTrue(output_string == 'herecouple words ofthe'					)

 def    __snake_case   (     self    :						Union[str, Any]					)	->							Union[str, Any]:
        _UpperCamelCase       :    Optional[int]					  =					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

        with tempfile.TemporaryDirectory() as tmp_dirname:
               tokenizer.save_pretrained(lowerCamelCase					)
               os.remove(os.path.join(lowerCamelCase    ,     'merges.txt'					)					)

               # load tokenizer without merges file should not throw an error
               _UpperCamelCase       :    Optional[Any]					  =					SpeechaTextaTokenizer.from_pretrained(lowerCamelCase					)

        with tempfile.TemporaryDirectory() as tmp_dirname:
               # save tokenizer and load again
               tokenizer.save_pretrained(lowerCamelCase					)
               _UpperCamelCase       :    int					  =					SpeechaTextaTokenizer.from_pretrained(lowerCamelCase					)

        self.assertIsNotNone(lowerCamelCase					)

 def    __snake_case   (     self    :						Optional[int]					)	->							Tuple:
        if not self.test_slow_tokenizer:
               return

        _UpperCamelCase       :    List[str]					  =					inspect.signature(self.tokenizer_class.__init__					)
        _UpperCamelCase       :    Union[str, Any]					  =					self.get_tokenizer()

        for parameter_name, parameter in signature.parameters.items():
               if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                      self.assertIn(lowerCamelCase    ,     tokenizer.init_kwargs					)




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 				__lowerCAmelCase					(    utils.Parser  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	"halfcheetah-medium-expert-v2"
 _lowerCamelCase  :		str			      =	"config.offline"







def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Tuple						,  lowercase_:		Dict						,  lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]			):
       _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					utils.load_model(lowercase_						,  lowercase_						,  lowercase_						,  epoch=lowercase_						,  device=lowercase_			)
       _UpperCamelCase       :    Tuple					  =					TrajectoryTransformerModel(gpt.config			)

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
       _UpperCamelCase       :    Any					  =					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
 )



