

import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE					:  Tuple             =      get_tests_dir('''fixtures/test_sentencepiece.model''')
_SCREAMING_SNAKE_CASE					:  List[str]             =      get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

_SCREAMING_SNAKE_CASE					:  Dict             =      '''pt''' if is_torch_available() else '''tf'''






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






  """simple docstring"""


  a									= CamembertTokenizer
  a									= CamembertTokenizerFast
  a									= True
  a									= True






  def 				lowercase_		(							self						:		str )		->							Tuple:
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE__   =   CamembertTokenizer(__lowerCamelCase )
       tokenizer.save_pretrained(self.tmpdirname )






  def 				lowercase_		(							self						:		List[Any] )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   '''<pad>'''
       SCREAMING_SNAKE_CASE__   =   1

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






  def 				lowercase_		(							self						:		int )		->							int:
       SCREAMING_SNAKE_CASE__   =   list(self.get_tokenizer().get_vocab().keys() )

       self.assertEqual(vocab_keys[0]     ,       '''<s>NOTUSED''' )
       self.assertEqual(vocab_keys[1]     ,       '''<pad>''' )
       self.assertEqual(vocab_keys[-1]     ,       '''<mask>''' )
       self.assertEqual(len(__lowerCamelCase )     ,       1004 )






  def 				lowercase_		(							self						:		Dict )		->							Tuple:
       self.assertEqual(self.get_tokenizer().vocab_size     ,       1005 )






  def 				lowercase_		(							self						:		str )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   CamembertTokenizer(__lowerCamelCase )
       tokenizer.save_pretrained(self.tmpdirname )
       SCREAMING_SNAKE_CASE__   =   CamembertTokenizerFast.from_pretrained(self.tmpdirname )

       SCREAMING_SNAKE_CASE__   =   '''I was born in 92000, and this is falsé.'''

       SCREAMING_SNAKE_CASE__   =   tokenizer.encode(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   rust_tokenizer.encode(__lowerCamelCase )
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   tokenizer.encode(__lowerCamelCase     ,       add_special_tokens=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   rust_tokenizer.encode(__lowerCamelCase     ,       add_special_tokens=__lowerCamelCase )
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )

       # <unk> tokens are not the same for `rust` than for `slow`.
       # Because spm gives back raw token instead of `unk` in EncodeAsPieces
       # tokens = tokenizer.tokenize(sequence)
       SCREAMING_SNAKE_CASE__   =   tokenizer.convert_ids_to_tokens(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   rust_tokenizer.tokenize(__lowerCamelCase )
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )






  def 				lowercase_		(							self						:		Dict )		->							int:
       if not self.test_rust_tokenizer:
            return

       SCREAMING_SNAKE_CASE__   =   self.get_tokenizer()
       SCREAMING_SNAKE_CASE__   =   self.get_rust_tokenizer()

       SCREAMING_SNAKE_CASE__   =   '''I was born in 92000, and this is falsé.'''

       SCREAMING_SNAKE_CASE__   =   tokenizer.tokenize(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   rust_tokenizer.tokenize(__lowerCamelCase )
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   tokenizer.encode(__lowerCamelCase     ,       add_special_tokens=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   rust_tokenizer.encode(__lowerCamelCase     ,       add_special_tokens=__lowerCamelCase )
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   self.get_rust_tokenizer()
       SCREAMING_SNAKE_CASE__   =   tokenizer.encode(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   rust_tokenizer.encode(__lowerCamelCase )
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )







  @slow
  def 				lowercase_		(							self						:		List[str] )		->							List[str]:
       # fmt: off
       SCREAMING_SNAKE_CASE__   =   {'''input_ids''': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '''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, 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, 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]]}  # noqa: E501
       # fmt: on

       # camembert is a french model. So we also use french texts.
       SCREAMING_SNAKE_CASE__   =   [
           '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
           '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
           '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
           '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
           '''telles que la traduction et la synthèse de texte.''',
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=__lowerCamelCase     ,       model_name='''camembert-base'''     ,       revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''     ,       sequences=__lowerCamelCase     ,       )





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,
       )


