





import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_UpperCAmelCase        =				data_utils.TransfoXLTokenizer
_UpperCAmelCase        =				data_utils.TransfoXLCorpus
_UpperCAmelCase        =				data_utils
_UpperCAmelCase        =				data_utils




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		str:
       if transfo_xl_dataset_file:
              # Convert a pre-processed corpus (see original TensorFlow repo)
              with open(UpperCamelCase_					,		"rb"  ) as fp:
                     UpperCamelCase_          =       pickle.load(UpperCamelCase_					,		encoding="latin1"  )
              # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
              UpperCamelCase_          =       pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
              print(F'''Save vocabulary to {pytorch_vocab_dump_path}'''  )
              UpperCamelCase_          =       corpus.vocab.__dict__
              torch.save(UpperCamelCase_					,		UpperCamelCase_  )

              UpperCamelCase_          =       corpus.__dict__
              corpus_dict_no_vocab.pop("vocab"					,		UpperCamelCase_  )
              UpperCamelCase_          =       pytorch_dump_folder_path + "/" + CORPUS_NAME
              print(F'''Save dataset to {pytorch_dataset_dump_path}'''  )
              torch.save(UpperCamelCase_					,		UpperCamelCase_  )

       if tf_checkpoint_path:
              # Convert a pre-trained TensorFlow model
              UpperCamelCase_          =       os.path.abspath(UpperCamelCase_  )
              UpperCamelCase_          =       os.path.abspath(UpperCamelCase_  )

              print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''  )
              # Initialise PyTorch model
              if transfo_xl_config_file == "":
                     UpperCamelCase_          =       TransfoXLConfig()
              else:
                     UpperCamelCase_          =       TransfoXLConfig.from_json_file(UpperCamelCase_  )
              print(F'''Building PyTorch model from configuration: {config}'''  )
              UpperCamelCase_          =       TransfoXLLMHeadModel(UpperCamelCase_  )

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


if __name__ == "__main__":
     _UpperCAmelCase        =				argparse.ArgumentParser()
     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(
         '--tf_checkpoint_path',
         default='',
         type=str,
         help='An optional path to a TensorFlow checkpoint path to be converted.',
     )
     parser.add_argument(
         '--transfo_xl_config_file',
         default='',
         type=str,
         help=(
             'An optional config json file corresponding to the pre-trained BERT model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--transfo_xl_dataset_file',
         default='',
         type=str,
         help='An optional dataset file to be converted in a vocabulary.',
     )
     _UpperCAmelCase        =				parser.parse_args()
     convert_transfo_xl_checkpoint_to_pytorch(
         args.tf_checkpoint_path,
         args.transfo_xl_config_file,
         args.pytorch_dump_folder_path,
         args.transfo_xl_dataset_file,
     )








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 							_UpperCamelCase					(	lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Union[str, Any]         =   XLMTokenizer
       _UpperCamelCase				:					Optional[int]         =   False

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




              """simple docstring"""


              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              UpperCamelCase_          =  [
                  "l",
                  "o",
                  "w",
                  "e",
                  "r",
                  "s",
                  "t",
                  "i",
                  "d",
                  "n",
                  "w</w>",
                  "r</w>",
                  "t</w>",
                  "lo",
                  "low",
                  "er</w>",
                  "low</w>",
                  "lowest</w>",
                  "newer</w>",
                  "wider</w>",
                  "<unk>",
              ]
              UpperCamelCase_          =  dict(zip(_SCREAMING_SNAKE_CASE					,   range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
              UpperCamelCase_          =  ["l o 123", "lo w 1456", "e r</w> 1789", ""]

              UpperCamelCase_          =  os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["vocab_file"]		)
              UpperCamelCase_          =  os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["merges_file"]		)
              with open(self.vocab_file					,   "w"		) as fp:
                     fp.write(json.dumps(_SCREAMING_SNAKE_CASE		)		)
              with open(self.merges_file					,   "w"		) as fp:
                     fp.write("\n".join(_SCREAMING_SNAKE_CASE		)		)

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  "lower newer"
              UpperCamelCase_          =  "lower newer"
              return input_text, output_text

       def lowercase (		self: int		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  XLMTokenizer(self.vocab_file					,   self.merges_file		)

              UpperCamelCase_          =  "lower"
              UpperCamelCase_          =  ["low", "er</w>"]
              UpperCamelCase_          =  tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  tokens + ["<unk>"]
              UpperCamelCase_          =  [14, 15, 20]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)



       @slow
       def lowercase (		self: Optional[int]		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  XLMTokenizer.from_pretrained("xlm-mlm-en-2048"		)

              UpperCamelCase_          =  tokenizer.encode("sequence builders"					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer.encode("multi-sequence build"					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              assert encoded_sentence == [0] + text + [1]
              assert encoded_pair == [0] + text + [1] + text_a + [1]

