




'''simple docstring'''



import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil







@pytest.fixture
def       _UpperCamelCase			(_lowerCamelCase				:  Any						)-> Any:




     '''simple docstring'''





     __snake_case            =	tmp_path / '''file.csv'''
     __snake_case            =	textwrap.dedent(
         '''\
        header1,header2
        1,2
        10,20
        '''						)
     with open(_lowerCamelCase ,						'''w'''						) as f:
          f.write(_lowerCamelCase						)
     return str(_lowerCamelCase						)







@pytest.fixture
def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> int:




     '''simple docstring'''





     __snake_case            =	tmp_path / '''malformed_file.csv'''
     __snake_case            =	textwrap.dedent(
         '''\
        header1,header2
        1,2
        10,20,
        '''						)
     with open(_lowerCamelCase ,						'''w'''						) as f:
          f.write(_lowerCamelCase						)
     return str(_lowerCamelCase						)







@pytest.fixture
def       _UpperCamelCase			(_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  Tuple						)-> List[str]:




     '''simple docstring'''





     __snake_case            =	tmp_path / '''csv_with_image.csv'''
     __snake_case            =	textwrap.dedent(
         f'''\
        image
        {image_file}
        '''						)
     with open(_lowerCamelCase ,						'''w'''						) as f:
          f.write(_lowerCamelCase						)
     return str(_lowerCamelCase						)







@pytest.fixture
def       _UpperCamelCase			(_lowerCamelCase				:  Any						)-> Tuple:




     '''simple docstring'''





     __snake_case            =	tmp_path / '''csv_with_label.csv'''
     __snake_case            =	textwrap.dedent(
         '''\
        label
        good
        bad
        good
        '''						)
     with open(_lowerCamelCase ,						'''w'''						) as f:
          f.write(_lowerCamelCase						)
     return str(_lowerCamelCase						)







@pytest.fixture
def       _UpperCamelCase			(_lowerCamelCase				:  Any						)-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	tmp_path / '''csv_with_int_list.csv'''
     __snake_case            =	textwrap.dedent(
         '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''						)
     with open(_lowerCamelCase ,						'''w'''						) as f:
          f.write(_lowerCamelCase						)
     return str(_lowerCamelCase						)







def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  Any						)-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	Csv()
     __snake_case            =	csv._generate_tables([[csv_file, malformed_csv_file]]						)
     with pytest.raises(_lowerCamelCase ,						match='''Error tokenizing data'''						):
          for _ in generator:
               pass
     assert any(
         record.levelname == '''ERROR'''
         and '''Failed to read file''' in record.message
         and os.path.basename(_lowerCamelCase						) in record.message
         for record in caplog.records						)







@require_pil
def       _UpperCamelCase			(_lowerCamelCase				:  Dict						)-> Optional[Any]:




     '''simple docstring'''





     with open(_lowerCamelCase ,						encoding='''utf-8'''						) as f:
          __snake_case            =	f.read().splitlines()[1]
     __snake_case            =	Csv(encoding='''utf-8''' ,						features=Features({'''image''': Image()}						)						)
     __snake_case            =	csv._generate_tables([[csv_file_with_image]]						)
     __snake_case            =	pa.concat_tables([table for _, table in generator]						)
     assert pa_table.schema.field('''image'''						).type == Image()()
     __snake_case            =	pa_table.to_pydict()['''image''']
     assert generated_content == [{"path": image_file, "bytes": None}]







def       _UpperCamelCase			(_lowerCamelCase				:  Any						)-> int:




     '''simple docstring'''





     with open(_lowerCamelCase ,						encoding='''utf-8'''						) as f:
          __snake_case            =	f.read().splitlines()[1:]
     __snake_case            =	Csv(encoding='''utf-8''' ,						features=Features({'''label''': ClassLabel(names=['''good''', '''bad''']						)}						)						)
     __snake_case            =	csv._generate_tables([[csv_file_with_label]]						)
     __snake_case            =	pa.concat_tables([table for _, table in generator]						)
     assert pa_table.schema.field('''label'''						).type == ClassLabel(names=['''good''', '''bad''']						)()
     __snake_case            =	pa_table.to_pydict()['''label''']
     assert generated_content == [ClassLabel(names=['''good''', '''bad''']						).straint(_lowerCamelCase						) for label in labels]







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple						)-> Any:




     '''simple docstring'''





     __snake_case            =	Csv(encoding='''utf-8''' ,						sep=''',''' ,						converters={'''int_list''': lambda _lowerCamelCase						: [int(_lowerCamelCase						) for i in x.split()]}						)
     __snake_case            =	csv._generate_tables([[csv_file_with_int_list]]						)
     __snake_case            =	pa.concat_tables([table for _, table in generator]						)
     assert pa.types.is_list(pa_table.schema.field('''int_list'''						).type						)
     __snake_case            =	pa_table.to_pydict()['''int_list''']
     assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]












'''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 (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   int										=      BarthezTokenizer
       __lowercase	:   Any										=      BarthezTokenizerFast
       __lowercase	:   Dict										=      True
       __lowercase	:   Optional[int]										=      True


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            super().setUp()

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


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	'''<pad>'''
            __snake_case            =	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    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	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	)     ,	10_1122	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size     ,	10_1122	)


       @require_torch
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
            __snake_case            =	[0, 57, 3018, 7_0307, 91, 2]

            __snake_case            =	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	)
            __snake_case            =	batch.input_ids.tolist()[0]
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)


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



            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_rust_tokenizer()

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

            __snake_case            =	tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	tokenizer.encode(__SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_rust_tokenizer()
            __snake_case            =	tokenizer.encode(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 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.
            __snake_case            =	[
                '''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     ,	)






