"""simple docstring"""






import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


__lowerCAmelCase       :			List[Any]   	={
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()


def      UpperCAmelCase__			(							lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:Any   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:Any=None						)					->       Any:
      '''simple docstring'''


      lowercase								=      XLNetConfig.from_json_file(lowerCAmelCase__						)

      lowercase								=      finetuning_task.lower() if finetuning_task is not None else """"""
      if finetuning_task in GLUE_TASKS_NUM_LABELS:
            print(f'Building PyTorch XLNetForSequenceClassification model from configuration: {config}'						)
            lowercase								=      finetuning_task
            lowercase								=      GLUE_TASKS_NUM_LABELS[finetuning_task]
            lowercase								=      XLNetForSequenceClassification(lowerCAmelCase__						)
      elif "squad" in finetuning_task:
            lowercase								=      finetuning_task
            lowercase								=      XLNetForQuestionAnswering(lowerCAmelCase__						)
      else:
            lowercase								=      XLNetLMHeadModel(lowerCAmelCase__						)

      # Load weights from tf checkpoint
      load_tf_weights_in_xlnet(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)

      # Save pytorch-model
      lowercase								=      os.path.join(lowerCAmelCase__   ,						lowerCAmelCase__						)
      lowercase								=      os.path.join(lowerCAmelCase__   ,						lowerCAmelCase__						)
      print(f'Save PyTorch model to {os.path.abspath(lowerCAmelCase__						)}'						)
      torch.save(model.state_dict()   ,						lowerCAmelCase__						)
      print(f'Save configuration file to {os.path.abspath(lowerCAmelCase__						)}'						)
      with open(lowerCAmelCase__   ,						"""w"""   ,						encoding="""utf-8"""						) as f:
            f.write(config.to_json_string()						)


if __name__ == "__main__":
       __lowerCAmelCase       :			Optional[Any]   	=argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--xlnet_config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained XLNet model. \n"""
               """This specifies the model architecture."""
           ),
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""",
           default=None,
           type=str,
           required=True,
           help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
       )
       parser.add_argument(
           """--finetuning_task""",
           default=None,
           type=str,
           help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
       )
       __lowerCAmelCase       :			Dict   	=parser.parse_args()
       print(args)

       convert_xlnet_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
       )




"""simple docstring"""






import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class 					_A		(			lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  str				    =      TransfoXLTokenizer
      snake_case__     :  Union[str, Any]				    =      False
      snake_case__     :  Union[str, Any]				    =      False

      def 							A__			(		self		):







            """simple docstring"""



            super().setUp()

            lowercase								=      [
                """<unk>""",
                """[CLS]""",
                """[SEP]""",
                """want""",
                """unwanted""",
                """wa""",
                """un""",
                """running""",
                """,""",
                """low""",
                """l""",
            ]
            lowercase								=      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						,			**__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      True
            return TransfoXLTokenizer.from_pretrained(self.tmpdirname						,			**__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      """<unk> UNwanted , running"""
            lowercase								=      """<unk> unwanted, running"""
            return input_text, output_text

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TransfoXLTokenizer(vocab_file=self.vocab_file						,			lower_case=__lowerCAmelCase		)

            lowercase								=      tokenizer.tokenize("""<unk> UNwanted , running"""		)
            self.assertListEqual(__lowerCAmelCase						,			["""<unk>""", """unwanted""", """,""", """running"""]		)

            self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase		)						,			[0, 4, 8, 7]		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TransfoXLTokenizer(lower_case=__lowerCAmelCase		)

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """		)						,			["""hello""", """!""", """how""", """are""", """you""", """?"""]		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TransfoXLTokenizer(lower_case=__lowerCAmelCase		)

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """		)						,			["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TransfoXLTokenizer(lower_case=__lowerCAmelCase		)
            lowercase								=      """Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
            lowercase								=      [
                """Hello""",
                """(""",
                """bracket""",
                """)""",
                """and""",
                """side""",
                """@-@""",
                """scrolled""",
                """[""",
                """and""",
                """]""",
                """Henry""",
                """'s""",
                """$""",
                """5""",
                """@,@""",
                """000""",
                """with""",
                """3""",
                """@.@""",
                """34""",
                """m""",
                """.""",
                """What""",
                """'s""",
                """up""",
                """!""",
                """?""",
            ]

            self.assertListEqual(tokenizer.tokenize(__lowerCAmelCase		)						,			__lowerCAmelCase		)

            self.assertEqual(tokenizer.convert_tokens_to_string(__lowerCAmelCase		)						,			__lowerCAmelCase		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_tokenizer()
            lowercase								=      len(__lowerCAmelCase		)

            tokenizer.add_tokens(["""new1""", """new2"""]		)
            tokenizer.move_added_token("""new1"""						,			1		)

            # Check that moved token is not copied (duplicate)
            self.assertEqual(len(__lowerCAmelCase		)						,			original_len + 2		)
            # Check that token is moved to specified id
            self.assertEqual(tokenizer.encode("""new1"""		)						,			[1]		)
            self.assertEqual(tokenizer.decode([1]		)						,			"""new1"""		)



