




'''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_						(      lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				BarthezTokenizer
  lowercase							=				BarthezTokenizerFast
  lowercase							=				True
  lowercase							=				True

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        super().setUp()

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

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






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

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

  def   A__						(				self				)  ->				Dict:



        """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(_SCREAMING_SNAKE_CASE				)      ,		101122				)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






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

  @require_torch
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






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

        UpperCamelCase      = self.tokenizer(
            _SCREAMING_SNAKE_CASE      ,		max_length=len(_SCREAMING_SNAKE_CASE				)      ,		padding=_SCREAMING_SNAKE_CASE      ,		truncation=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)
        self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_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(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_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(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE				)
        self.assertListEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

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

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


  @slow
  def   A__						(				self				)  ->				Optional[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=_SCREAMING_SNAKE_CASE      ,		model_name="""moussaKam/mbarthez"""      ,		revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""      ,		sequences=_SCREAMING_SNAKE_CASE      ,		)








'''simple docstring'''

import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {'vocab_file': 'vocab.txt'}

SCREAMING_SNAKE_CASE__     =       {
    'vocab_file': {
        'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',
        'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',
    },
}

SCREAMING_SNAKE_CASE__     =       {
    'facebook/esm2_t6_8M_UR50D': 1_0_2_4,
    'facebook/esm2_t12_35M_UR50D': 1_0_2_4,
}







def    lowercase__			(				__UpperCamelCase   )->			Any:
      with open(__UpperCamelCase		,						"""r"""   ) as f:
            UpperCamelCase      = f.read().splitlines()
            return [l.strip() for l in lines]






class     a_						(      lowerCamelCase    ):
  lowercase							=				VOCAB_FILES_NAMES
  lowercase							=				PRETRAINED_VOCAB_FILES_MAP
  lowercase							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase							=				["""input_ids""", """attention_mask"""]

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE="<unk>"      ,		_SCREAMING_SNAKE_CASE="<cls>"      ,		_SCREAMING_SNAKE_CASE="<pad>"      ,		_SCREAMING_SNAKE_CASE="<mask>"      ,		_SCREAMING_SNAKE_CASE="<eos>"      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Any:



        """simple docstring"""






        super().__init__(**_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = load_vocab_file(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = dict(enumerate(self.all_tokens				)				)
        UpperCamelCase      = {tok: ind for ind, tok in enumerate(self.all_tokens				)}
        UpperCamelCase      = unk_token
        UpperCamelCase      = cls_token
        UpperCamelCase      = pad_token
        UpperCamelCase      = mask_token
        UpperCamelCase      = eos_token
        UpperCamelCase      = self.all_tokens
        self._create_trie(self.unique_no_split_tokens				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return self._id_to_token.get(_SCREAMING_SNAKE_CASE      ,		self.unk_token				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        return self._token_to_id.get(_SCREAMING_SNAKE_CASE      ,		self._token_to_id.get(self.unk_token				)				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        return text.split()

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE=False				)  ->				Dict:



        """simple docstring"""






        return len(self._id_to_token				)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return {token: i for i, token in enumerate(self.all_tokens				)}

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        return self._token_to_id.get(_SCREAMING_SNAKE_CASE      ,		self._token_to_id.get(self.unk_token				)				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return self._id_to_token.get(_SCREAMING_SNAKE_CASE      ,		self.unk_token				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None				)  ->				List[int]:



        """simple docstring"""






        UpperCamelCase      = [self.cls_token_id]
        UpperCamelCase      = [self.eos_token_id]  # No sep token in ESM vocabulary
        if token_ids_a is None:
              if self.eos_token_id is None:
                    return cls + token_ids_a
              else:
                    return cls + token_ids_a + sep
        elif self.eos_token_id is None:
              raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!"""				)
        return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = False				)  ->				List[int]:



        """simple docstring"""






        if already_has_special_tokens:
              if token_ids_a is not None:
                    raise ValueError(
                        """You should not supply a second sequence if the provided sequence of """
                        """ids is already formatted with special tokens for the model."""				)

              return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
        UpperCamelCase      = [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]
        if token_ids_a is not None:
              mask += [0] * len(_SCREAMING_SNAKE_CASE				) + [1]
        return mask

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = os.path.join(_SCREAMING_SNAKE_CASE      ,		(filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""				)
        with open(_SCREAMING_SNAKE_CASE      ,		"""w"""				) as f:
              f.write("""\n""".join(self.all_tokens				)				)
        return (vocab_file,)

  @property
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        return self.get_vocab_size(with_added_tokens=_SCREAMING_SNAKE_CASE				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = False				)  ->				int:



        """simple docstring"""






        return super()._add_tokens(_SCREAMING_SNAKE_CASE      ,		special_tokens=_SCREAMING_SNAKE_CASE				)
