





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	(   UpperCamelCase__						,    unittest.TestCase):
      _lowercase    : Union[str, Any]            =      BarthezTokenizer
      _lowercase    : Any            =      BarthezTokenizerFast
      _lowercase    : Any            =      True
      _lowercase    : Dict            =      True







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        super().setUp()

        a__     : Any  =BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"							)
        tokenizer.save_pretrained(self.tmpdirname							)
        tokenizer.save_pretrained(self.tmpdirname		,				legacy_format=lowerCAmelCase__							)
        a__     : int  =tokenizer







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : List[Any]  ="<pad>"
        a__     : 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        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        a__     : Dict  =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__							)		,				1_0_1_1_2_2							)







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        self.assertEqual(self.get_tokenizer().vocab_size		,				1_0_1_1_2_2							)







      @require_torch
      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        a__     : Dict  =["A long paragraph for summarization.", "Another paragraph for summarization."]
        a__     : Dict  =[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

        a__     : Union[str, Any]  =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							)
        a__     : Optional[Any]  =batch.input_ids.tolist()[0]
        self.assertListEqual(lowerCAmelCase__		,				lowerCAmelCase__							)







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



        '''simple docstring'''




        if not self.test_rust_tokenizer:
          return

        a__     : str  =self.get_tokenizer()
        a__     : Any  =self.get_rust_tokenizer()

        a__     : List[Any]  ="I was born in 92000, and this is falsé."

        a__     : Optional[int]  =tokenizer.tokenize(lowerCAmelCase__							)
        a__     : Union[str, Any]  =rust_tokenizer.tokenize(lowerCAmelCase__							)
        self.assertListEqual(lowerCAmelCase__		,				lowerCAmelCase__							)

        a__     : Optional[int]  =tokenizer.encode(lowerCAmelCase__		,				add_special_tokens=lowerCAmelCase__							)
        a__     : List[str]  =rust_tokenizer.encode(lowerCAmelCase__		,				add_special_tokens=lowerCAmelCase__							)
        self.assertListEqual(lowerCAmelCase__		,				lowerCAmelCase__							)

        a__     : Union[str, Any]  =self.get_rust_tokenizer()
        a__     : str  =tokenizer.encode(lowerCAmelCase__							)
        a__     : int  =rust_tokenizer.encode(lowerCAmelCase__							)
        self.assertListEqual(lowerCAmelCase__		,				lowerCAmelCase__							)







      @slow
      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : Any  ={"input_ids": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 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__     : 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=lowerCAmelCase__		,				model_name="moussaKam/mbarthez"		,				revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"		,				sequences=lowerCAmelCase__		,				)













import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase__)
class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : str            =      field(default="""image-classification"""						,    metadata={"""include_in_asdict_even_if_is_default""": True})
      _lowercase    : ClassVar[Features]            =      Features({"""image""": Image()})
      _lowercase    : ClassVar[Features]            =      Features({"""labels""": ClassLabel})
      _lowercase    : str            =      "image"
      _lowercase    : str            =      "labels"







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						List[Any]:



        '''simple docstring'''




        if self.label_column not in features:
          raise ValueError(F'''Column {self.label_column} is not present in features.'''							)
        if not isinstance(features[self.label_column]		,				lowerCAmelCase__							):
          raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''							)
        a__     : int  =copy.deepcopy(self							)
        a__     : Dict  =self.label_schema.copy()
        a__     : int  =features[self.label_column]
        a__     : str  =label_schema
        return task_template







      @property
      def        _lowercase  (   self							)       ->						Dict[str, str]:



        '''simple docstring'''




        return {
            self.image_column: "image",
            self.label_column: "labels",
        }






