


"""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  (					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Dict    =		BarthezTokenizer
     A_       :	str    =		BarthezTokenizerFast
     A_       :	int    =		True
     A_       :	Union[str, Any]    =		True


     def 							_UpperCAmelCase      (      self							)       ->							str:
          super().setUp()

          _a    	=				BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''							)
          tokenizer.save_pretrained(self.tmpdirname							)
          tokenizer.save_pretrained(self.tmpdirname    ,				legacy_format=__UpperCAmelCase							)
          _a    	=				tokenizer


     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a    	=				'''<pad>'''
          _a    	=				1

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


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a    	=				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(__UpperCAmelCase							)    ,				101122							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          self.assertEqual(self.get_tokenizer().vocab_size    ,				101122							)


     @require_torch
     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a    	=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          _a    	=				[0, 57, 3018, 70307, 91, 2]

          _a    	=				self.tokenizer(
              __UpperCAmelCase    ,				max_length=len(__UpperCAmelCase							)    ,				padding=__UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				return_tensors='''pt'''							)
          self.assertIsInstance(__UpperCAmelCase    ,				__UpperCAmelCase							)

          self.assertEqual((2, 6)    ,				batch.input_ids.shape							)
          self.assertEqual((2, 6)    ,				batch.attention_mask.shape							)
          _a    	=				batch.input_ids.tolist()[0]
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          if not self.test_rust_tokenizer:
               return

          _a    	=				self.get_tokenizer()
          _a    	=				self.get_rust_tokenizer()

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

          _a    	=				tokenizer.tokenize(__UpperCAmelCase							)
          _a    	=				rust_tokenizer.tokenize(__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)

          _a    	=				tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase							)
          _a    	=				rust_tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)

          _a    	=				self.get_rust_tokenizer()
          _a    	=				tokenizer.encode(__UpperCAmelCase							)
          _a    	=				rust_tokenizer.encode(__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          # fmt: off
          _a    	=				{'''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.
          _a    	=				[
              '''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=__UpperCAmelCase    ,				model_name='''moussaKam/mbarthez'''    ,				revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''    ,				sequences=__UpperCAmelCase    ,				)



"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def       A_   (   _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

     _a    	=				[
         '''encoder.version''',
         '''decoder.version''',
         '''model.encoder.version''',
         '''model.decoder.version''',
         '''_float_tensor''',
         '''decoder.output_projection.weight''',
     ]
     for k in ignore_keys:
          state_dict.pop(_lowerCAmelCase,     _lowerCAmelCase       )
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

     _a	,      _a    	=				emb.weight.shape
     _a    	=				nn.Linear(_lowerCAmelCase,     _lowerCAmelCase,     bias=_lowerCAmelCase       )
     _a    	=				emb.weight.data
     return lin_layer
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					Union[str, Any]="facebook/mbart-large-en-ro",     _lowerCAmelCase     :					Any=False,     _lowerCAmelCase     :					int=False       ):






     """simple docstring"""

     _a    	=				torch.load(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']
     remove_ignore_keys_(_lowerCAmelCase       )
     _a    	=				state_dict['''encoder.embed_tokens.weight'''].shape[0]

     _a    	=				MBartConfig.from_pretrained(_lowerCAmelCase,     vocab_size=_lowerCAmelCase       )
     if mbart_aa and finetuned:
          _a    	=				'''relu'''

     _a    	=				state_dict['''decoder.embed_tokens.weight''']
     _a    	=				MBartForConditionalGeneration(_lowerCAmelCase       )
     model.model.load_state_dict(_lowerCAmelCase       )

     if finetuned:
          _a    	=				make_linear_from_emb(model.model.shared       )

     return model


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
   )
   parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument(
       '''--hf_config''',
       default='''facebook/mbart-large-cc25''',
       type=str,
       help='''Which huggingface architecture to use: mbart-large''',
   )
   parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
   parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
   __snake_case		=       parser.parse_args()
   __snake_case		=       convert_fairseq_mbart_checkpoint_from_disk(
       args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
   )
   model.save_pretrained(args.pytorch_dump_folder_path)