code: 
  
  
  """simple docstring"""
  import os
  import pickle
  import tempfile
  import unittest
  from typing import Callable, Optional
  
  import numpy as np
  
  from transformers import (
      BatchEncoding,
      BertTokenizer,
      BertTokenizerFast,
      PreTrainedTokenizer,
      PreTrainedTokenizerFast,
      TensorType,
      TokenSpan,
      is_tokenizers_available,
  )
  from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
  from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow
  
  
  if is_tokenizers_available():
        from tokenizers import Tokenizer
        from tokenizers.models import WordPiece
  
  
  
  
  class   lowercase__							(				unittest.TestCase							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     List[Any]:
        _lowerCamelCase  :				str      =		list(tokenizer_class.max_model_input_sizes.keys())
        for model_name in sa_models[:1]:
           _lowerCamelCase  :				List[Any]      =		tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE)
           self.assertIsInstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
           self.assertIsInstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
           for special_tok in tokenizer.all_special_tokens:
              self.assertIsInstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
              _lowerCamelCase  :				List[str]      =		tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE)
              self.assertIsInstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     Union[str, Any]:
        _lowerCamelCase  :				Optional[int]      =		pickle.dumps(SCREAMING_SNAKE_CASE)
        self.assertIsNotNone(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				int      =		pickle.loads(SCREAMING_SNAKE_CASE)
  
        # Ensure is_fast is correctly restored
        self.assertEqual(be_restored.is_fast			,      be_original.is_fast)
  
        # Ensure encodings are potentially correctly restored
        if be_original.is_fast:
           self.assertIsNotNone(be_restored.encodings)
        else:
           self.assertIsNone(be_restored.encodings)
  
        # Ensure the keys are the same
        for original_v, restored_v in zip(be_original.values()			,      be_restored.values()):
           if equal_op:
              self.assertTrue(equal_op(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE))
           else:
              self.assertEqual(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     @slow
     def 		UpperCamelCase_     (       self)     ->     Dict:
        self.check_tokenizer_from_pretrained(SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        self.assertEqual(TensorType("""tf""")			,      TensorType.TENSORFLOW)
        self.assertEqual(TensorType("""pt""")			,      TensorType.PYTORCH)
        self.assertEqual(TensorType("""np""")			,      TensorType.NUMPY)
  
  
  
  
  
     @require_tokenizers
     def 		UpperCamelCase_     (       self)     ->     str:
        import numpy as np
  
        _lowerCamelCase  :				List[Any]      =		BertTokenizer.from_pretrained("""bert-base-cased""")
        _lowerCamelCase  :				List[Any]      =		BertTokenizerFast.from_pretrained("""bert-base-cased""")
  
        # Python no tensor
        with self.subTest("""BatchEncoding (Python, return_tensors=None)"""):
           self.assert_dump_and_restore(tokenizer_p("""Small example to encode"""))
  
        with self.subTest("""BatchEncoding (Python, return_tensors=NUMPY)"""):
           self.assert_dump_and_restore(
               tokenizer_p("""Small example to encode"""			,      return_tensors=TensorType.NUMPY)			,      np.array_equal)
  
        with self.subTest("""BatchEncoding (Rust, return_tensors=None)"""):
           self.assert_dump_and_restore(tokenizer_r("""Small example to encode"""))
  
        with self.subTest("""BatchEncoding (Rust, return_tensors=NUMPY)"""):
           self.assert_dump_and_restore(
               tokenizer_r("""Small example to encode"""			,      return_tensors=TensorType.NUMPY)			,      np.array_equal)
  
  
  
  
  
     @require_tf
     @require_tokenizers
     def 		UpperCamelCase_     (       self)     ->     Any:
        import tensorflow as tf
  
        def tf_array_equals(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
           return tf.reduce_all(tf.equal(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE))
  
        _lowerCamelCase  :				List[Any]      =		BertTokenizer.from_pretrained("""bert-base-cased""")
        _lowerCamelCase  :				Any      =		BertTokenizerFast.from_pretrained("""bert-base-cased""")
  
        with self.subTest("""BatchEncoding (Python, return_tensors=TENSORFLOW)"""):
           self.assert_dump_and_restore(
               tokenizer_p("""Small example to encode"""			,      return_tensors=TensorType.TENSORFLOW)			,      SCREAMING_SNAKE_CASE)
  
        with self.subTest("""BatchEncoding (Rust, return_tensors=TENSORFLOW)"""):
           self.assert_dump_and_restore(
               tokenizer_r("""Small example to encode"""			,      return_tensors=TensorType.TENSORFLOW)			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     @require_torch
     @require_tokenizers
     def 		UpperCamelCase_     (       self)     ->     Dict:
        import torch
  
        _lowerCamelCase  :				str      =		BertTokenizer.from_pretrained("""bert-base-cased""")
        _lowerCamelCase  :				Any      =		BertTokenizerFast.from_pretrained("""bert-base-cased""")
  
        with self.subTest("""BatchEncoding (Python, return_tensors=PYTORCH)"""):
           self.assert_dump_and_restore(
               tokenizer_p("""Small example to encode"""			,      return_tensors=TensorType.PYTORCH)			,      torch.equal)
  
        with self.subTest("""BatchEncoding (Rust, return_tensors=PYTORCH)"""):
           self.assert_dump_and_restore(
               tokenizer_r("""Small example to encode"""			,      return_tensors=TensorType.PYTORCH)			,      torch.equal)
  
  
  
  
  
     @require_tokenizers
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        _lowerCamelCase  :				Any      =		BertTokenizer.from_pretrained("""bert-base-cased""")
        _lowerCamelCase  :				Tuple      =		BertTokenizerFast.from_pretrained("""bert-base-cased""")
  
        with self.subTest("""Python Tokenizer"""):
           self.assertFalse(tokenizer_p("""Small example to_encode""").is_fast)
  
        with self.subTest("""Rust Tokenizer"""):
           self.assertTrue(tokenizer_r("""Small example to_encode""").is_fast)
  
  
  
  
  
     @require_tokenizers
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        _lowerCamelCase  :				Optional[int]      =		BertTokenizerFast.from_pretrained("""bert-base-cased""")
        _lowerCamelCase  :				int      =		tokenizer_r(["""Test""", """\xad""", """test"""]			,      is_split_into_words=SCREAMING_SNAKE_CASE)
  
        self.assertEqual(encoded.word_to_tokens(0)			,      TokenSpan(start=1			,      end=2))
        self.assertEqual(encoded.word_to_tokens(1)			,      SCREAMING_SNAKE_CASE)
        self.assertEqual(encoded.word_to_tokens(2)			,      TokenSpan(start=2			,      end=3))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Any:
        _lowerCamelCase  :				Tuple      =		BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]})
        _lowerCamelCase  :				str      =		batch.convert_to_tensors(tensor_type="""np""")
        self.assertEqual(tensor_batch["""inputs"""].shape			,      (2, 3))
        self.assertEqual(tensor_batch["""labels"""].shape			,      (2,))
        # test converting the converted
        with CaptureStderr() as cs:
           _lowerCamelCase  :				List[Any]      =		batch.convert_to_tensors(tensor_type="""np""")
        self.assertFalse(len(cs.err)			,      msg=F'should have no warning, but got {cs.err}')
  
        _lowerCamelCase  :				List[Any]      =		BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0})
        _lowerCamelCase  :				List[Any]      =		batch.convert_to_tensors(tensor_type="""np"""			,      prepend_batch_axis=SCREAMING_SNAKE_CASE)
        self.assertEqual(tensor_batch["""inputs"""].shape			,      (1, 3))
        self.assertEqual(tensor_batch["""labels"""].shape			,      (1,))
  
  
  
  
  
     @require_torch
     def 		UpperCamelCase_     (       self)     ->     List[str]:
        _lowerCamelCase  :				int      =		BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]})
        _lowerCamelCase  :				str      =		batch.convert_to_tensors(tensor_type="""pt""")
        self.assertEqual(tensor_batch["""inputs"""].shape			,      (2, 3))
        self.assertEqual(tensor_batch["""labels"""].shape			,      (2,))
        # test converting the converted
        with CaptureStderr() as cs:
           _lowerCamelCase  :				Tuple      =		batch.convert_to_tensors(tensor_type="""pt""")
        self.assertFalse(len(cs.err)			,      msg=F'should have no warning, but got {cs.err}')
  
        _lowerCamelCase  :				List[Any]      =		BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0})
        _lowerCamelCase  :				List[Any]      =		batch.convert_to_tensors(tensor_type="""pt"""			,      prepend_batch_axis=SCREAMING_SNAKE_CASE)
        self.assertEqual(tensor_batch["""inputs"""].shape			,      (1, 3))
        self.assertEqual(tensor_batch["""labels"""].shape			,      (1,))
  
  
  
  
  
     @require_tf
     def 		UpperCamelCase_     (       self)     ->     str:
        _lowerCamelCase  :				Dict      =		BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]})
        _lowerCamelCase  :				Any      =		batch.convert_to_tensors(tensor_type="""tf""")
        self.assertEqual(tensor_batch["""inputs"""].shape			,      (2, 3))
        self.assertEqual(tensor_batch["""labels"""].shape			,      (2,))
        # test converting the converted
        with CaptureStderr() as cs:
           _lowerCamelCase  :				Optional[Any]      =		batch.convert_to_tensors(tensor_type="""tf""")
        self.assertFalse(len(cs.err)			,      msg=F'should have no warning, but got {cs.err}')
  
        _lowerCamelCase  :				List[str]      =		BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0})
        _lowerCamelCase  :				str      =		batch.convert_to_tensors(tensor_type="""tf"""			,      prepend_batch_axis=SCREAMING_SNAKE_CASE)
        self.assertEqual(tensor_batch["""inputs"""].shape			,      (1, 3))
        self.assertEqual(tensor_batch["""labels"""].shape			,      (1,))
  
  
  
  
  
     @require_flax
     def 		UpperCamelCase_     (       self)     ->     Any:
        _lowerCamelCase  :				Union[str, Any]      =		BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]})
        _lowerCamelCase  :				Optional[int]      =		batch.convert_to_tensors(tensor_type="""jax""")
        self.assertEqual(tensor_batch["""inputs"""].shape			,      (2, 3))
        self.assertEqual(tensor_batch["""labels"""].shape			,      (2,))
        # test converting the converted
        with CaptureStderr() as cs:
           _lowerCamelCase  :				Optional[int]      =		batch.convert_to_tensors(tensor_type="""jax""")
        self.assertFalse(len(cs.err)			,      msg=F'should have no warning, but got {cs.err}')
  
        _lowerCamelCase  :				Dict      =		BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0})
        _lowerCamelCase  :				Dict      =		batch.convert_to_tensors(tensor_type="""jax"""			,      prepend_batch_axis=SCREAMING_SNAKE_CASE)
        self.assertEqual(tensor_batch["""inputs"""].shape			,      (1, 3))
        self.assertEqual(tensor_batch["""labels"""].shape			,      (1,))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        _lowerCamelCase  :				Union[str, Any]      =		[{"""input_ids""": np.array([0, 1, 2])}, {"""input_ids""": np.array([0, 1, 2, 3])}]
        _lowerCamelCase  :				Dict      =		BertTokenizer.from_pretrained("""bert-base-cased""")
  
        _lowerCamelCase  :				Tuple      =		tokenizer.pad(SCREAMING_SNAKE_CASE			,      padding=SCREAMING_SNAKE_CASE)
        self.assertTrue(isinstance(batch["""input_ids"""]			,      np.ndarray))
        self.assertEqual(batch["""input_ids"""].tolist()			,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
        _lowerCamelCase  :				Union[str, Any]      =		tokenizer.pad(SCREAMING_SNAKE_CASE			,      padding=SCREAMING_SNAKE_CASE			,      return_tensors="""np""")
        self.assertTrue(isinstance(batch["""input_ids"""]			,      np.ndarray))
        self.assertEqual(batch["""input_ids"""].tolist()			,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
  
  
  
  
  
     @require_torch
     def 		UpperCamelCase_     (       self)     ->     Any:
        import torch
  
        _lowerCamelCase  :				Any      =		[{"""input_ids""": torch.tensor([0, 1, 2])}, {"""input_ids""": torch.tensor([0, 1, 2, 3])}]
        _lowerCamelCase  :				List[str]      =		BertTokenizer.from_pretrained("""bert-base-cased""")
  
        _lowerCamelCase  :				Optional[Any]      =		tokenizer.pad(SCREAMING_SNAKE_CASE			,      padding=SCREAMING_SNAKE_CASE)
        self.assertTrue(isinstance(batch["""input_ids"""]			,      torch.Tensor))
        self.assertEqual(batch["""input_ids"""].tolist()			,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
        _lowerCamelCase  :				List[Any]      =		tokenizer.pad(SCREAMING_SNAKE_CASE			,      padding=SCREAMING_SNAKE_CASE			,      return_tensors="""pt""")
        self.assertTrue(isinstance(batch["""input_ids"""]			,      torch.Tensor))
        self.assertEqual(batch["""input_ids"""].tolist()			,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
  
  
  
  
  
     @require_tf
     def 		UpperCamelCase_     (       self)     ->     List[Any]:
        import tensorflow as tf
  
        _lowerCamelCase  :				Optional[Any]      =		[{"""input_ids""": tf.constant([0, 1, 2])}, {"""input_ids""": tf.constant([0, 1, 2, 3])}]
        _lowerCamelCase  :				Optional[Any]      =		BertTokenizer.from_pretrained("""bert-base-cased""")
  
        _lowerCamelCase  :				Dict      =		tokenizer.pad(SCREAMING_SNAKE_CASE			,      padding=SCREAMING_SNAKE_CASE)
        self.assertTrue(isinstance(batch["""input_ids"""]			,      tf.Tensor))
        self.assertEqual(batch["""input_ids"""].numpy().tolist()			,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
        _lowerCamelCase  :				Union[str, Any]      =		tokenizer.pad(SCREAMING_SNAKE_CASE			,      padding=SCREAMING_SNAKE_CASE			,      return_tensors="""tf""")
        self.assertTrue(isinstance(batch["""input_ids"""]			,      tf.Tensor))
        self.assertEqual(batch["""input_ids"""].numpy().tolist()			,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
  
  
  
  
  
     @require_tokenizers
     def 		UpperCamelCase_     (       self)     ->     Dict:
        _lowerCamelCase  :				int      =		Tokenizer(WordPiece(unk_token="""[UNK]"""))
        PreTrainedTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE)
  
  
  
  
  
     @require_tokenizers
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        _lowerCamelCase  :				List[Any]      =		Tokenizer(WordPiece(unk_token="""[UNK]"""))
        with tempfile.TemporaryDirectory() as tmpdirname:
           bert_tokenizer.save(os.path.join(SCREAMING_SNAKE_CASE			,      """tokenizer.json"""))
           PreTrainedTokenizerFast(tokenizer_file=os.path.join(SCREAMING_SNAKE_CASE			,      """tokenizer.json"""))
  
  
style_context: 
  
  
  """simple docstring"""
  import torch
  import trajectory.utils as utils
  
  from transformers import TrajectoryTransformerModel
  
  
  
  
  class   lowercase__							(				utils.Parser							):
     __UpperCAmelCase       					=				"halfcheetah-medium-expert-v2"
     __UpperCAmelCase       					=				"config.offline"
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Optional[Any]	,   __snake_case       :			Optional[int]	,   __snake_case       :			List[str]	,   __snake_case       :			Union[str, Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase      ,						_lowerCamelCase  :				int      =		utils.load_model(__snake_case	,   __snake_case	,   __snake_case	,   epoch=__snake_case	,   device=__snake_case      )
     _lowerCamelCase  :				List[str]      =		TrajectoryTransformerModel(gpt.config      )
  
     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()      )
     _lowerCamelCase  :				str      =		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
   )
  
  