


"""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"""))





"""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
 )

