






'''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        a_		(    __a			, unittest.TestCase       ):
  lowercase  	=   BarthezTokenizer
  lowercase  	=   BarthezTokenizerFast
  lowercase  	=   True
  lowercase  	=   True

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        super().setUp()

        UpperCamelCase       						=      BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""   )
        tokenizer.save_pretrained(self.tmpdirname   )
        tokenizer.save_pretrained(self.tmpdirname    ,	legacy_format=snake_case__   )
        UpperCamelCase       						=      tokenizer

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      """<pad>"""
        UpperCamelCase       						=      1

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

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      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(snake_case__   )    ,	101122   )

  def 						A__		(   self   )						->   int:



        """simple docstring"""






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

  @require_torch
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
        UpperCamelCase       						=      [0, 57, 3018, 70307, 91, 2]

        UpperCamelCase       						=      self.tokenizer(
            snake_case__    ,	max_length=len(snake_case__   )    ,	padding=snake_case__    ,	truncation=snake_case__    ,	return_tensors="""pt"""   )
        self.assertIsInstance(snake_case__    ,	snake_case__   )

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

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        if not self.test_rust_tokenizer:
              return

        UpperCamelCase       						=      self.get_tokenizer()
        UpperCamelCase       						=      self.get_rust_tokenizer()

        UpperCamelCase       						=      """I was born in 92000, and this is falsé."""

        UpperCamelCase       						=      tokenizer.tokenize(snake_case__   )
        UpperCamelCase       						=      rust_tokenizer.tokenize(snake_case__   )
        self.assertListEqual(snake_case__    ,	snake_case__   )

        UpperCamelCase       						=      tokenizer.encode(snake_case__    ,	add_special_tokens=snake_case__   )
        UpperCamelCase       						=      rust_tokenizer.encode(snake_case__    ,	add_special_tokens=snake_case__   )
        self.assertListEqual(snake_case__    ,	snake_case__   )

        UpperCamelCase       						=      self.get_rust_tokenizer()
        UpperCamelCase       						=      tokenizer.encode(snake_case__   )
        UpperCamelCase       						=      rust_tokenizer.encode(snake_case__   )
        self.assertListEqual(snake_case__    ,	snake_case__   )


  @slow
  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      {"""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       						=      [
            """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=snake_case__    ,	model_name="""moussaKam/mbarthez"""    ,	revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""    ,	sequences=snake_case__    ,	)













'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 					lowercase__				(   __UpperCamelCase					)->				Any:
      UpperCamelCase       						=      [
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(__UpperCamelCase	,		__UpperCamelCase					)







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase						,UpperCamelCase       						=      emb.weight.shape
      UpperCamelCase       						=      nn.Linear(__UpperCamelCase	,		__UpperCamelCase	,		bias=__UpperCamelCase					)
      UpperCamelCase       						=      emb.weight.data
      return lin_layer







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase       						=      torch.load(__UpperCamelCase	,		map_location="""cpu"""					)
      UpperCamelCase       						=      mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
      UpperCamelCase       						=      mam_aaa["""model"""]
      remove_ignore_keys_(__UpperCamelCase					)
      UpperCamelCase       						=      state_dict["""encoder.embed_tokens.weight"""].shape[0]

      UpperCamelCase       						=      MaMaaaConfig(
          vocab_size=__UpperCamelCase	,		max_position_embeddings=1024	,		encoder_layers=args.encoder_layers	,		decoder_layers=args.decoder_layers	,		encoder_attention_heads=args.encoder_attention_heads	,		decoder_attention_heads=args.decoder_attention_heads	,		encoder_ffn_dim=args.encoder_ffn_embed_dim	,		decoder_ffn_dim=args.decoder_ffn_embed_dim	,		d_model=args.encoder_embed_dim	,		encoder_layerdrop=args.encoder_layerdrop	,		decoder_layerdrop=args.decoder_layerdrop	,		dropout=args.dropout	,		attention_dropout=args.attention_dropout	,		activation_dropout=args.activation_dropout	,		activation_function="""relu"""	,		)

      UpperCamelCase       						=      state_dict["""decoder.embed_tokens.weight"""]
      UpperCamelCase       						=      MaMaaaForConditionalGeneration(__UpperCamelCase					)
      model.model.load_state_dict(__UpperCamelCase	,		strict=__UpperCamelCase					)
      UpperCamelCase       						=      make_linear_from_emb(model.model.shared					)

      return model


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='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.')
     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     SCREAMING_SNAKE_CASE__											=     convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)
