

import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 	UpperCAmelCase__		(	A__						,					unittest.TestCase	):






  """simple docstring"""


  a									= OpenAIGPTTokenizer
  a									= OpenAIGPTTokenizerFast
  a									= True
  a									= False






  def 				lowercase_		(							self						:		Optional[int] )		->							Any:
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       SCREAMING_SNAKE_CASE__   =   [
           '''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>''',
       ]
       SCREAMING_SNAKE_CASE__   =   dict(zip(__lowerCamelCase     ,       range(len(__lowerCamelCase ) ) ) )
       SCREAMING_SNAKE_CASE__   =   ['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

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






  def 				lowercase_		(							self						:		Optional[int]     ,       __lowerCamelCase						:		int )		->							str:
       return "lower newer", "lower newer"






  def 				lowercase_		(							self						:		List[Any] )		->							Any:
       SCREAMING_SNAKE_CASE__   =   OpenAIGPTTokenizer(self.vocab_file     ,       self.merges_file )

       SCREAMING_SNAKE_CASE__   =   '''lower'''
       SCREAMING_SNAKE_CASE__   =   ['''low''', '''er</w>''']
       SCREAMING_SNAKE_CASE__   =   tokenizer.tokenize(__lowerCamelCase )
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   tokens + ['''<unk>''']
       SCREAMING_SNAKE_CASE__   =   [14, 15, 20]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase )     ,       __lowerCamelCase )






  def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Dict=15 )		->							str:
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
                 SCREAMING_SNAKE_CASE__   =   self.rust_tokenizer_class.from_pretrained(__lowerCamelCase     ,       **__lowerCamelCase )

                 # Simple input
                 SCREAMING_SNAKE_CASE__   =   '''This is a simple input'''
                 SCREAMING_SNAKE_CASE__   =   ['''This is a simple input 1''', '''This is a simple input 2''']
                 SCREAMING_SNAKE_CASE__   =   ('''This is a simple input''', '''This is a pair''')
                 SCREAMING_SNAKE_CASE__   =   [
                     ('''This is a simple input 1''', '''This is a simple input 2'''),
                     ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                 ]

                 # Simple input tests
                 self.assertRaises(__lowerCamelCase     ,       tokenizer_r.encode     ,       __lowerCamelCase     ,       max_length=__lowerCamelCase     ,       padding='''max_length''' )

                 # Simple input
                 self.assertRaises(__lowerCamelCase     ,       tokenizer_r.encode_plus     ,       __lowerCamelCase     ,       max_length=__lowerCamelCase     ,       padding='''max_length''' )

                 # Simple input
                 self.assertRaises(
                     __lowerCamelCase     ,       tokenizer_r.batch_encode_plus     ,       __lowerCamelCase     ,       max_length=__lowerCamelCase     ,       padding='''max_length'''     ,       )

                 # Pair input
                 self.assertRaises(__lowerCamelCase     ,       tokenizer_r.encode     ,       __lowerCamelCase     ,       max_length=__lowerCamelCase     ,       padding='''max_length''' )

                 # Pair input
                 self.assertRaises(__lowerCamelCase     ,       tokenizer_r.encode_plus     ,       __lowerCamelCase     ,       max_length=__lowerCamelCase     ,       padding='''max_length''' )

                 # Pair input
                 self.assertRaises(
                     __lowerCamelCase     ,       tokenizer_r.batch_encode_plus     ,       __lowerCamelCase     ,       max_length=__lowerCamelCase     ,       padding='''max_length'''     ,       )







  def 				lowercase_		(							self						:		int )		->							Tuple:
       pass






@require_ftfy
@require_spacy
@require_tokenizers
class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  pass





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
_SCREAMING_SNAKE_CASE					:  Tuple             =      data_utils.TransfoXLTokenizer
_SCREAMING_SNAKE_CASE					:  Dict             =      data_utils.TransfoXLCorpus
_SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      data_utils
_SCREAMING_SNAKE_CASE					:  Any             =      data_utils
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




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

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

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

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

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


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Any             =      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.''',
       )
       _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      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,
       )


