


import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
       from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
       __UpperCamelCase       :       List[str]						       =				get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
       from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__UpperCamelCase       :       Union[str, Any]						       =				12_8022
__UpperCamelCase       :       Any						       =				12_8028

@require_sentencepiece
class       __magic_name__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		List[str]			=      MaMaaaTokenizer
     A:		Tuple			=      False
     A:		Optional[int]			=      False
     A:		int			=      True


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Dict:





      '''simple docstring'''






      super().setUp()

      UpperCamelCase__					:    Optional[Any]          =   ['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
      UpperCamelCase__					:    List[str]          =   dict(zip(lowerCamelCase__							,	range(len(lowerCamelCase__			)			)			)			)
      UpperCamelCase__					:    Any          =   Path(self.tmpdirname			)
      save_json(lowerCamelCase__							,	save_dir / VOCAB_FILES_NAMES['''vocab_file''']			)
      if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
       copyfile(lowerCamelCase__							,	save_dir / VOCAB_FILES_NAMES['''spm_file''']			)

      UpperCamelCase__					:    Dict          =   MaMaaaTokenizer.from_pretrained(self.tmpdirname			)
      tokenizer.save_pretrained(self.tmpdirname			)


     def      UpperCAmelCase__	(      self							:     Any							,	**lowerCamelCase__							:     Optional[Any]			)  ->      int:





      '''simple docstring'''






      return MaMaaaTokenizer.from_pretrained(self.tmpdirname							,	**lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     Dict			)  ->      Optional[Any]:





      '''simple docstring'''






      return (
          "This is a test",
          "This is a test",
      )


     def      UpperCAmelCase__	(      self							:     str			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   '''</s>'''
      UpperCamelCase__					:    List[Any]          =   0

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__			)							,	lowerCamelCase__			)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__			)							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   self.get_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   list(tokenizer.get_vocab().keys()			)

      self.assertEqual(vocab_keys[0]							,	'''</s>'''			)
      self.assertEqual(vocab_keys[1]							,	'''<unk>'''			)
      self.assertEqual(vocab_keys[-1]							,	'''<s>'''			)
      self.assertEqual(len(lowerCamelCase__			)							,	tokenizer.vocab_size + len(tokenizer.get_added_vocab()			)			)


     @unittest.skip('''Skip this test while all models are still to be uploaded.'''			)
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      str:





      '''simple docstring'''






      pass


     def      UpperCAmelCase__	(      self							:     int			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   self.get_tokenizer()

      UpperCamelCase__					:    Union[str, Any]          =   tokenizer.tokenize('''This is a test'''			)
      self.assertListEqual(lowerCamelCase__							,	['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']			)

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(lowerCamelCase__			)							,	[2, 3, 4, 5, 6]							,	)

      UpperCamelCase__					:    List[Any]          =   tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]			)
      self.assertListEqual(lowerCamelCase__							,	['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']			)

      UpperCamelCase__					:    List[Any]          =   tokenizer.convert_tokens_to_string(lowerCamelCase__			)
      self.assertEqual(lowerCamelCase__							,	'''This is a test'''			)


     @slow
     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   {'''input_ids''': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=lowerCamelCase__							,	model_name='''facebook/m2m100_418M'''							,	revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e'''							,	)



@require_torch
@require_sentencepiece
@require_tokenizers
class       __magic_name__	(     unittest.TestCase):
     A:		Dict			=      "facebook/m2m100_418M"
     A:		Any			=      [
         "In my opinion, there are two levels of response from the French government.",
         "NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
     ]
     A:		Optional[Any]			=      [
         "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
         "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
     ]

     # fmt: off
     A:		List[Any]			=      [EN_CODE, 5_9_3, 1_9_4_9, 1_1_5_7_8_1, 4, 7_1_5_8_6, 4_2_3_4, 6_0_6_3_3, 1_2_6_2_3_3, 4_3_2, 1_2_3_8_0_8, 1_5_5_9_2, 1_1_9_7, 1_1_7_1_3_2, 1_2_0_6_1_8, 5, 2]


     @classmethod
     def      UpperCAmelCase__	(      cls							:     Tuple			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    MaMaaaTokenizer          =   MaMaaaTokenizer.from_pretrained(
          cls.checkpoint_name							,	src_lang='''en'''							,	tgt_lang='''fr'''			)
      UpperCamelCase__					:    int          =   1
      return cls


     def      UpperCAmelCase__	(      self							:     str			)  ->      Optional[int]:





      '''simple docstring'''






      self.assertEqual(self.tokenizer.get_lang_id('''ar'''			)							,	128006			)
      self.assertEqual(self.tokenizer.get_lang_id('''en'''			)							,	128022			)
      self.assertEqual(self.tokenizer.get_lang_id('''ro'''			)							,	128076			)
      self.assertEqual(self.tokenizer.get_lang_id('''mr'''			)							,	128063			)


     def      UpperCAmelCase__	(      self							:     int			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   self.tokenizer.get_vocab()
      self.assertEqual(len(lowerCamelCase__			)							,	self.tokenizer.vocab_size			)
      self.assertEqual(vocab['''<unk>''']							,	3			)
      self.assertIn(self.tokenizer.get_lang_token('''en'''			)							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Any			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   '''en'''
      UpperCamelCase__					:    Any          =   self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
      self.assertListEqual(self.expected_src_tokens							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Dict:





      '''simple docstring'''






      self.assertIn(lowerCamelCase__							,	self.tokenizer.all_special_ids			)
      # fmt: off
      UpperCamelCase__					:    List[Any]          =   [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
      # fmt: on
      UpperCamelCase__					:    Optional[int]          =   self.tokenizer.decode(lowerCamelCase__							,	skip_special_tokens=lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   self.tokenizer.decode(generated_ids[1:]							,	skip_special_tokens=lowerCamelCase__			)
      self.assertEqual(lowerCamelCase__							,	lowerCamelCase__			)
      self.assertNotIn(self.tokenizer.eos_token							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Any			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   tempfile.mkdtemp()
      UpperCamelCase__					:    Any          =   self.tokenizer.lang_token_to_id
      self.tokenizer.save_pretrained(lowerCamelCase__			)
      UpperCamelCase__					:    List[Any]          =   MaMaaaTokenizer.from_pretrained(lowerCamelCase__			)
      self.assertDictEqual(new_tok.lang_token_to_id							,	lowerCamelCase__			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   '''en'''
      UpperCamelCase__					:    str          =   '''fr'''

      UpperCamelCase__					:    Tuple          =   self.tokenizer(self.src_text							,	text_target=self.tgt_text							,	padding=lowerCamelCase__							,	return_tensors='''pt'''			)

      UpperCamelCase__					:    Any          =   shift_tokens_right(
          batch['''labels''']							,	self.tokenizer.pad_token_id							,	self.tokenizer.eos_token_id			)

      for k in batch:
       UpperCamelCase__					:    List[Any]          =   batch[k].tolist()
      # batch = {k: v.tolist() for k,v in batch.items()}
      # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
      # batch.decoder_inputs_ids[0][0] ==
      assert batch.input_ids[1][0] == EN_CODE
      assert batch.input_ids[1][-1] == 2
      assert batch.labels[1][0] == FR_CODE
      assert batch.labels[1][-1] == 2
      assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]


     @require_torch
     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   '''mr'''
      self.assertListEqual(self.tokenizer.prefix_tokens							,	[self.tokenizer.get_lang_id('''mr'''			)]			)
      self.assertListEqual(self.tokenizer.suffix_tokens							,	[self.tokenizer.eos_token_id]			)

      UpperCamelCase__					:    int          =   '''zh'''
      self.assertListEqual(self.tokenizer.prefix_tokens							,	[self.tokenizer.get_lang_id('''zh'''			)]			)
      self.assertListEqual(self.tokenizer.suffix_tokens							,	[self.tokenizer.eos_token_id]			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   '''mr'''
      self.tokenizer._switch_to_target_mode()
      self.assertListEqual(self.tokenizer.prefix_tokens							,	[self.tokenizer.get_lang_id('''mr'''			)]			)
      self.assertListEqual(self.tokenizer.suffix_tokens							,	[self.tokenizer.eos_token_id]			)
      self.tokenizer._switch_to_input_mode()
      self.assertListEqual(self.tokenizer.prefix_tokens							,	[self.tokenizer.get_lang_id(self.tokenizer.src_lang			)]			)

      UpperCamelCase__					:    List[str]          =   '''zh'''
      self.tokenizer._switch_to_target_mode()
      self.assertListEqual(self.tokenizer.prefix_tokens							,	[self.tokenizer.get_lang_id('''zh'''			)]			)
      self.assertListEqual(self.tokenizer.suffix_tokens							,	[self.tokenizer.eos_token_id]			)
      self.tokenizer._switch_to_input_mode()
      self.assertListEqual(self.tokenizer.prefix_tokens							,	[self.tokenizer.get_lang_id(self.tokenizer.src_lang			)]			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     int			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   self.tokenizer._build_translation_inputs('''A test'''							,	return_tensors='''pt'''							,	src_lang='''en'''							,	tgt_lang='''ar'''			)

      self.assertEqual(
          nested_simplify(lowerCamelCase__			)							,	{
              # en_XX, A, test, EOS
              '''input_ids''': [[128022, 58, 4183, 2]],
              '''attention_mask''': [[1, 1, 1, 1]],
              # ar_AR
              '''forced_bos_token_id''': 128006,
          }							,	)






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def 			_a  (				SCREAMING_SNAKE_CASE	:				Namespace     ):







 """simple docstring"""



 return ConvertCommand(
     args.model_type    , args.tf_checkpoint    , args.pytorch_dump_output    , args.config    , args.finetuning_task_name     )


__UpperCamelCase       :       List[Any]						       =				"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"






class       __magic_name__	(     __lowerCAmelCase):


     @staticmethod
     def      UpperCAmelCase__	(      lowerCamelCase__							:     ArgumentParser			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   parser.add_parser(
          '''convert'''							,	help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'''							,	)
      train_parser.add_argument('''--model_type'''							,	type=lowerCamelCase__							,	required=lowerCamelCase__							,	help='''Model\'s type.'''			)
      train_parser.add_argument(
          '''--tf_checkpoint'''							,	type=lowerCamelCase__							,	required=lowerCamelCase__							,	help='''TensorFlow checkpoint path or folder.'''			)
      train_parser.add_argument(
          '''--pytorch_dump_output'''							,	type=lowerCamelCase__							,	required=lowerCamelCase__							,	help='''Path to the PyTorch saved model output.'''			)
      train_parser.add_argument('''--config'''							,	type=lowerCamelCase__							,	default=''''''							,	help='''Configuration file path or folder.'''			)
      train_parser.add_argument(
          '''--finetuning_task_name'''							,	type=lowerCamelCase__							,	default=lowerCamelCase__							,	help='''Optional fine-tuning task name if the TF model was a finetuned model.'''							,	)
      train_parser.set_defaults(func=lowerCamelCase__			)


     def __init__(      self							:     Optional[Any]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	*lowerCamelCase__							:     Optional[int]							,	)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   logging.get_logger('''transformers-cli/converting'''			)

      self._logger.info(F"Loading model {model_type}"			)
      UpperCamelCase__					:    List[str]          =   model_type
      UpperCamelCase__					:    Optional[int]          =   tf_checkpoint
      UpperCamelCase__					:    List[Any]          =   pytorch_dump_output
      UpperCamelCase__					:    List[Any]          =   config
      UpperCamelCase__					:    Any          =   finetuning_task_name


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Optional[Any]:





      '''simple docstring'''






      if self._model_type == "albert":
       try:
        from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
            convert_tf_checkpoint_to_pytorch,
        )
       except ImportError:
        raise ImportError(lowerCamelCase__			)

       convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output			)
      elif self._model_type == "bert":
       try:
        from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
            convert_tf_checkpoint_to_pytorch,
        )
       except ImportError:
        raise ImportError(lowerCamelCase__			)

       convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output			)
      elif self._model_type == "funnel":
       try:
        from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
            convert_tf_checkpoint_to_pytorch,
        )
       except ImportError:
        raise ImportError(lowerCamelCase__			)

       convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output			)
      elif self._model_type == "t5":
       try:
        from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
       except ImportError:
        raise ImportError(lowerCamelCase__			)

       convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output			)
      elif self._model_type == "gpt":
       from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
           convert_openai_checkpoint_to_pytorch,
       )

       convert_openai_checkpoint_to_pytorch(self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output			)
      elif self._model_type == "transfo_xl":
       try:
        from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
            convert_transfo_xl_checkpoint_to_pytorch,
        )
       except ImportError:
        raise ImportError(lowerCamelCase__			)

       if "ckpt" in self._tf_checkpoint.lower():
        UpperCamelCase__					:    str          =   self._tf_checkpoint
        UpperCamelCase__					:    List[Any]          =   ''''''
       else:
        UpperCamelCase__					:    Any          =   self._tf_checkpoint
        UpperCamelCase__					:    List[Any]          =   ''''''
       convert_transfo_xl_checkpoint_to_pytorch(
           lowerCamelCase__							,	self._config							,	self._pytorch_dump_output							,	lowerCamelCase__			)
      elif self._model_type == "gpt2":
       try:
        from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
            convert_gpta_checkpoint_to_pytorch,
        )
       except ImportError:
        raise ImportError(lowerCamelCase__			)

       convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output			)
      elif self._model_type == "xlnet":
       try:
        from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
            convert_xlnet_checkpoint_to_pytorch,
        )
       except ImportError:
        raise ImportError(lowerCamelCase__			)

       convert_xlnet_checkpoint_to_pytorch(
           self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output							,	self._finetuning_task_name			)
      elif self._model_type == "xlm":
       from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
           convert_xlm_checkpoint_to_pytorch,
       )

       convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint							,	self._pytorch_dump_output			)
      elif self._model_type == "lxmert":
       from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
           convert_lxmert_checkpoint_to_pytorch,
       )

       convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint							,	self._pytorch_dump_output			)
      elif self._model_type == "rembert":
       from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
           convert_rembert_tf_checkpoint_to_pytorch,
       )

       convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output			)
      else:
       raise ValueError(
           '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'''			)


