




'''simple docstring'''







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
A									=data_utils.TransfoXLTokenizer
A									=data_utils.TransfoXLCorpus
A									=data_utils
A									=data_utils





def 		snake_case_							(_a			:							Tuple					,     _a			:							Union[str, Any]					,     _a			:							str					,     _a			:							List[str]  ):




    if transfo_xl_dataset_file:
        # Convert a pre-processed corpus (see original TensorFlow repo)
        with open(_lowerCAmelCase					,     '''rb'''  ) as fp:
            UpperCAmelCase     =	pickle.load(_lowerCAmelCase					,     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(_lowerCAmelCase					,     _lowerCAmelCase  )

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

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

        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(_lowerCAmelCase  )
        print(F"Building PyTorch model from configuration: {config}"  )
        UpperCAmelCase     =	TransfoXLLMHeadModel(_lowerCAmelCase  )

        UpperCAmelCase     =	load_tf_weights_in_transfo_xl(_lowerCAmelCase					,     _lowerCAmelCase					,     _lowerCAmelCase  )
        # Save pytorch-model
        UpperCAmelCase     =	os.path.join(_lowerCAmelCase					,     _lowerCAmelCase  )
        UpperCAmelCase     =	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__":
   A									=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.',
   )
   A									=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,
   )










'''simple docstring'''







import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class     _a						(			__a							,							unittest.TestCase				):
       __a       :  Any            =      MgpstrTokenizer
       __a       :  Optional[Any]            =      False
       __a       :  str            =      {}
       __a       :  Optional[int]            =      False
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           super().setUp()

           # fmt: off
           UpperCAmelCase     =	['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
           # fmt: on
           UpperCAmelCase     =	dict(zip(lowercase     ,       range(len(lowercase       )       )       )       )

           UpperCAmelCase     =	os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''vocab_file''']       )
           with open(self.vocab_file     ,       '''w'''     ,       encoding='''utf-8'''       ) as fp:
               fp.write(json.dumps(lowercase       ) + '''\n'''       )
       def 					A   (			self		:			int     ,       **lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,       **lowercase       )
       def 					A   (			self		:			int     ,       lowercase		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''tester'''
           UpperCAmelCase     =	'''tester'''
           return input_text, output_text
       @unittest.skip('''MGP-STR always lower cases letters.'''       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           pass
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_tokenizers(do_lower_case=lowercase       )
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                   UpperCAmelCase     =	'''[SPECIAL_TOKEN]'''

                   tokenizer.add_special_tokens({'''cls_token''': special_token}       )
                   UpperCAmelCase     =	tokenizer.encode([special_token]     ,       add_special_tokens=lowercase       )
                   self.assertEqual(len(lowercase       )     ,       1       )

                   UpperCAmelCase     =	tokenizer.decode(lowercase     ,       skip_special_tokens=lowercase       )
                   self.assertTrue(special_token not in decoded       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                   UpperCAmelCase ,  UpperCAmelCase     =	self.get_input_output_texts(lowercase       )

                   UpperCAmelCase     =	tokenizer.tokenize(lowercase       )
                   UpperCAmelCase     =	tokenizer.convert_tokens_to_ids(lowercase       )
                   UpperCAmelCase     =	tokenizer.encode(lowercase     ,       add_special_tokens=lowercase       )
                   self.assertListEqual(lowercase     ,       lowercase       )

                   UpperCAmelCase     =	tokenizer.convert_ids_to_tokens(lowercase       )
                   self.assertNotEqual(len(lowercase       )     ,       0       )
                   UpperCAmelCase     =	tokenizer.decode(lowercase       )
                   self.assertIsInstance(lowercase     ,       lowercase       )

                   self.assertEqual(text_a.replace(''' '''     ,       ''''''       )     ,       lowercase       )
       @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           pass







       @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''       )
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           pass




