


from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class       __magic_name__	:
     A:		str			=      field(
         metadata={"help": "The output directory where the model will be written."}					,				)
     A:		str			=      field(
         metadata={
             "help": (
                 "The encoder model checkpoint for weights initialization."
                 "Don't set if you want to train an encoder model from scratch."
             )
         }					,				)
     A:		str			=      field(
         metadata={
             "help": (
                 "The decoder model checkpoint for weights initialization."
                 "Don't set if you want to train a decoder model from scratch."
             )
         }					,				)
     A:		Optional[str]			=      field(
         default=__lowerCAmelCase					,				metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"})
     A:		Optional[str]			=      field(
         default=__lowerCAmelCase					,				metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"})



def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   HfArgumentParser((ModelArguments,)     )
 ((UpperCamelCase__)       ,				)					:    int          =   parser.parse_args_into_dataclasses()

 # Load pretrained model and tokenizer

 # Use explicit specified encoder config
 if model_args.encoder_config_name:
  UpperCamelCase__					:    Tuple          =   AutoConfig.from_pretrained(model_args.encoder_config_name     )
 # Use pretrained encoder model's config
 else:
  UpperCamelCase__					:    Tuple          =   AutoConfig.from_pretrained(model_args.encoder_model_name_or_path     )

 # Use explicit specified decoder config
 if model_args.decoder_config_name:
  UpperCamelCase__					:    Union[str, Any]          =   AutoConfig.from_pretrained(model_args.decoder_config_name     )
 # Use pretrained decoder model's config
 else:
  UpperCamelCase__					:    Dict          =   AutoConfig.from_pretrained(model_args.decoder_model_name_or_path     )

 # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
 UpperCamelCase__					:    Optional[Any]          =   True
 UpperCamelCase__					:    Optional[Any]          =   True

 UpperCamelCase__					:    Any          =   FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
     encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path    , decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path    , encoder_config=SCREAMING_SNAKE_CASE    , decoder_config=SCREAMING_SNAKE_CASE    , )

 # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
 UpperCamelCase__					:    List[str]          =   decoder_config.decoder_start_token_id
 UpperCamelCase__					:    List[str]          =   decoder_config.pad_token_id
 if decoder_start_token_id is None:
  UpperCamelCase__					:    Optional[Any]          =   decoder_config.bos_token_id
 if pad_token_id is None:
  UpperCamelCase__					:    Union[str, Any]          =   decoder_config.eos_token_id

 # This is necessary to make Flax's generate() work
 UpperCamelCase__					:    Union[str, Any]          =   decoder_config.eos_token_id
 UpperCamelCase__					:    Optional[Any]          =   decoder_start_token_id
 UpperCamelCase__					:    int          =   pad_token_id

 UpperCamelCase__					:    int          =   AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path     )

 UpperCamelCase__					:    List[Any]          =   AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path     )
 UpperCamelCase__					:    Optional[int]          =   tokenizer.convert_ids_to_tokens(model.config.pad_token_id     )

 model.save_pretrained(model_args.output_dir     )
 image_processor.save_pretrained(model_args.output_dir     )
 tokenizer.save_pretrained(model_args.output_dir     )


if __name__ == "__main__":
       main()






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       __magic_name__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		Optional[int]			=      BarthezTokenizer
     A:		Optional[int]			=      BarthezTokenizerFast
     A:		str			=      True
     A:		Optional[int]			=      True


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      str:





      '''simple docstring'''






      super().setUp()

      UpperCamelCase__					:    Optional[Any]          =   BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''			)
      tokenizer.save_pretrained(self.tmpdirname			)
      tokenizer.save_pretrained(self.tmpdirname							,	legacy_format=lowerCamelCase__			)
      UpperCamelCase__					:    int          =   tokenizer


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





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   '''<pad>'''
      UpperCamelCase__					:    List[str]          =   1

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


     def      UpperCAmelCase__	(      self							:     int			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   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(lowerCamelCase__			)							,	101122			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      str:





      '''simple docstring'''






      self.assertEqual(self.get_tokenizer().vocab_size							,	101122			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      UpperCamelCase__					:    Any          =   [0, 57, 3018, 70307, 91, 2]

      UpperCamelCase__					:    Optional[int]          =   self.tokenizer(
          lowerCamelCase__							,	max_length=len(lowerCamelCase__			)							,	padding=lowerCamelCase__							,	truncation=lowerCamelCase__							,	return_tensors='''pt'''			)
      self.assertIsInstance(lowerCamelCase__							,	lowerCamelCase__			)

      self.assertEqual((2, 6)							,	batch.input_ids.shape			)
      self.assertEqual((2, 6)							,	batch.attention_mask.shape			)
      UpperCamelCase__					:    List[Any]          =   batch.input_ids.tolist()[0]
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      List[Any]:





      '''simple docstring'''






      if not self.test_rust_tokenizer:
       return

      UpperCamelCase__					:    List[Any]          =   self.get_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   self.get_rust_tokenizer()

      UpperCamelCase__					:    Any          =   '''I was born in 92000, and this is falsé.'''

      UpperCamelCase__					:    List[str]          =   tokenizer.tokenize(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   rust_tokenizer.tokenize(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   self.get_rust_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   tokenizer.encode(lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     @slow
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   {'''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.
      UpperCamelCase__					:    int          =   [
          '''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='''moussaKam/mbarthez'''							,	revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''							,	sequences=lowerCamelCase__							,	)


