




"""simple docstring"""






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class lowerCAmelCase__	(     lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			BarthezTokenizer
			lowerCamelCase__      =			BarthezTokenizerFast
			lowerCamelCase__      =			True
			lowerCamelCase__      =			True
			def 					A_    (  self  ):
							super().setUp()

							_lowerCamelCase     :			Union[str, Any]    =    BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'  )
							tokenizer.save_pretrained(self.tmpdirname  )
							tokenizer.save_pretrained(self.tmpdirname  ,      legacy_format=lowercase  )
							_lowerCamelCase     :			Union[str, Any]    =    tokenizer
			def 					A_    (  self  ):
							_lowerCamelCase     :			Dict    =    '<pad>'
							_lowerCamelCase     :			Tuple    =    1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase  )  ,      lowercase  )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase  )  ,      lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    list(self.get_tokenizer().get_vocab().keys()  )

							self.assertEqual(vocab_keys[0]  ,      '<s>'  )
							self.assertEqual(vocab_keys[1]  ,      '<pad>'  )
							self.assertEqual(vocab_keys[-1]  ,      '<mask>'  )
							self.assertEqual(len(lowercase  )  ,      101122  )
			def 					A_    (  self  ):
							self.assertEqual(self.get_tokenizer().vocab_size  ,      101122  )
			@require_torch
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							_lowerCamelCase     :			List[str]    =    [0, 57, 3018, 70307, 91, 2]

							_lowerCamelCase     :			Tuple    =    self.tokenizer(
							    lowercase  ,      max_length=len(lowercase  )  ,      padding=lowercase  ,      truncation=lowercase  ,      return_tensors='pt'  )
							self.assertIsInstance(lowercase  ,      lowercase  )

							self.assertEqual((2, 6)  ,      batch.input_ids.shape  )
							self.assertEqual((2, 6)  ,      batch.attention_mask.shape  )
							_lowerCamelCase     :			int    =    batch.input_ids.tolist()[0]
							self.assertListEqual(lowercase  ,      lowercase  )
			def 					A_    (  self  ):
							if not self.test_rust_tokenizer:
											return

							_lowerCamelCase     :			str    =    self.get_tokenizer()
							_lowerCamelCase     :			Optional[int]    =    self.get_rust_tokenizer()

							_lowerCamelCase     :			Union[str, Any]    =    'I was born in 92000, and this is falsé.'

							_lowerCamelCase     :			List[Any]    =    tokenizer.tokenize(lowercase  )
							_lowerCamelCase     :			Dict    =    rust_tokenizer.tokenize(lowercase  )
							self.assertListEqual(lowercase  ,      lowercase  )

							_lowerCamelCase     :			Dict    =    tokenizer.encode(lowercase  ,      add_special_tokens=lowercase  )
							_lowerCamelCase     :			Dict    =    rust_tokenizer.encode(lowercase  ,      add_special_tokens=lowercase  )
							self.assertListEqual(lowercase  ,      lowercase  )

							_lowerCamelCase     :			Any    =    self.get_rust_tokenizer()
							_lowerCamelCase     :			List[str]    =    tokenizer.encode(lowercase  )
							_lowerCamelCase     :			List[Any]    =    rust_tokenizer.encode(lowercase  )
							self.assertListEqual(lowercase  ,      lowercase  )






			@slow
			def 					A_    (  self  ):
							# fmt: off
							_lowerCamelCase     :			List[str]    =    {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# moussaKam/mbarthez is a french model. So we also use french texts.
							_lowerCamelCase     :			List[Any]    =    [
							    '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=lowercase  ,      model_name='moussaKam/mbarthez'  ,      revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'  ,      sequences=lowercase  ,      )




'''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
_UpperCamelCase											=						data_utils.TransfoXLTokenizer
_UpperCamelCase											=						data_utils.TransfoXLCorpus
_UpperCamelCase											=						data_utils
_UpperCamelCase											=						data_utils

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

    __lowerCamelCase       :       Optional[Any]           = corpus.__dict__
    corpus_dict_no_vocab.pop('vocab'       ,_lowerCAmelCase       )
    __lowerCamelCase       :       Optional[Any]           = 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
    __lowerCamelCase       :       int           = os.path.abspath(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = 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 == "":
      __lowerCamelCase       :       Optional[int]           = TransfoXLConfig()
    else:
      __lowerCamelCase       :       Optional[int]           = TransfoXLConfig.from_json_file(_lowerCAmelCase       )
    print(F'Building PyTorch model from configuration: {config}'       )
    __lowerCamelCase       :       List[str]           = TransfoXLLMHeadModel(_lowerCAmelCase       )

    __lowerCamelCase       :       Dict           = load_tf_weights_in_transfo_xl(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )
    # Save pytorch-model
    __lowerCamelCase       :       List[str]           = os.path.join(_lowerCAmelCase       ,_lowerCAmelCase       )
    __lowerCamelCase       :       int           = 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__":
   _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,
   )






