


'''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       a				(      __a       )  ->     Optional[int]:



					'''simple docstring'''






					UpperCamelCase__       :List[str]							 =			tmp_path / '''file.csv'''
					UpperCamelCase__       :Union[str, Any]							 =			textwrap.dedent(
					    '''\
        header1,header2
        1,2
        10,20
        '''       )
					with open(__a			, '''w'''       ) as f:
										f.write(__a       )
					return str(__a       )


@pytest.fixture
def       a				(      __a       )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :Any							 =			tmp_path / '''malformed_file.csv'''
					UpperCamelCase__       :Optional[Any]							 =			textwrap.dedent(
					    '''\
        header1,header2
        1,2
        10,20,
        '''       )
					with open(__a			, '''w'''       ) as f:
										f.write(__a       )
					return str(__a       )


@pytest.fixture
def       a				(      __a			, __a       )  ->     List[str]:



					'''simple docstring'''






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


@pytest.fixture
def       a				(      __a       )  ->     str:



					'''simple docstring'''






					UpperCamelCase__       :str							 =			tmp_path / '''csv_with_label.csv'''
					UpperCamelCase__       :List[str]							 =			textwrap.dedent(
					    '''\
        label
        good
        bad
        good
        '''       )
					with open(__a			, '''w'''       ) as f:
										f.write(__a       )
					return str(__a       )


@pytest.fixture
def       a				(      __a       )  ->     Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__       :Dict							 =			tmp_path / '''csv_with_int_list.csv'''
					UpperCamelCase__       :List[Any]							 =			textwrap.dedent(
					    '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''       )
					with open(__a			, '''w'''       ) as f:
										f.write(__a       )
					return str(__a       )


def       a				(      __a			, __a			, __a       )  ->     List[str]:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			Csv()
					UpperCamelCase__       :Tuple							 =			csv._generate_tables([[csv_file, malformed_csv_file]]       )
					with pytest.raises(__a			, 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(__a       ) in record.message
					    for record in caplog.records       )


@require_pil
def       a				(      __a       )  ->     Optional[Any]:



					'''simple docstring'''






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


def       a				(      __a       )  ->     str:



					'''simple docstring'''






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







def       a				(      __a       )  ->     List[str]:



					'''simple docstring'''






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



'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


__snake_case											=					logging.get_logger(__name__)

__snake_case											=					{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case											=					{
    '''vocab_file''': {
        '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
        '''distilbert-base-uncased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
        ),
        '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
        '''distilbert-base-cased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
        ),
        '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
        '''distilbert-base-multilingual-cased''': (
            '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
        '''distilbert-base-uncased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
        '''distilbert-base-cased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-german-cased''': (
            '''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-multilingual-cased''': (
            '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
        ),
    },
}

__snake_case											=					{
    '''distilbert-base-uncased''': 512,
    '''distilbert-base-uncased-distilled-squad''': 512,
    '''distilbert-base-cased''': 512,
    '''distilbert-base-cased-distilled-squad''': 512,
    '''distilbert-base-german-cased''': 512,
    '''distilbert-base-multilingual-cased''': 512,
}


__snake_case											=					{
    '''distilbert-base-uncased''': {'''do_lower_case''': True},
    '''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
    '''distilbert-base-cased''': {'''do_lower_case''': False},
    '''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
    '''distilbert-base-german-cased''': {'''do_lower_case''': False},
    '''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    VOCAB_FILES_NAMES
		_a   			=    PRETRAINED_VOCAB_FILES_MAP
		_a   			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a   			=    PRETRAINED_INIT_CONFIGURATION
		_a   			=    ['input_ids', 'attention_mask']
		_a   			=    DistilBertTokenizer




		def __init__(		self   ,       UpperCamelCase_=None   ,       UpperCamelCase_=None   ,       UpperCamelCase_=True   ,       UpperCamelCase_="[UNK]"   ,       UpperCamelCase_="[SEP]"   ,       UpperCamelCase_="[PAD]"   ,       UpperCamelCase_="[CLS]"   ,       UpperCamelCase_="[MASK]"   ,       UpperCamelCase_=True   ,       UpperCamelCase_=None   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							super().__init__(
							    UpperCamelCase_   ,       tokenizer_file=UpperCamelCase_   ,       do_lower_case=UpperCamelCase_   ,       unk_token=UpperCamelCase_   ,       sep_token=UpperCamelCase_   ,       pad_token=UpperCamelCase_   ,       cls_token=UpperCamelCase_   ,       mask_token=UpperCamelCase_   ,       tokenize_chinese_chars=UpperCamelCase_   ,       strip_accents=UpperCamelCase_   ,       **UpperCamelCase_   ,       )

							UpperCamelCase__       :int							 =			json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
							if (
							    normalizer_state.get('''lowercase'''   ,       UpperCamelCase_							) != do_lower_case
							    or normalizer_state.get('''strip_accents'''   ,       UpperCamelCase_							) != strip_accents
							    or normalizer_state.get('''handle_chinese_chars'''   ,       UpperCamelCase_							) != tokenize_chinese_chars
							):
												UpperCamelCase__       :int							 =			getattr(UpperCamelCase_   ,       normalizer_state.pop('''type'''							)							)
												UpperCamelCase__       :Optional[Any]							 =			do_lower_case
												UpperCamelCase__       :Optional[Any]							 =			strip_accents
												UpperCamelCase__       :List[Any]							 =			tokenize_chinese_chars
												UpperCamelCase__       :Any							 =			normalizer_class(**UpperCamelCase_							)

							UpperCamelCase__       :int							 =			do_lower_case




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

							return output




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			[self.sep_token_id]
							UpperCamelCase__       :List[str]							 =			[self.cls_token_id]
							if token_ids_a is None:
												return len(cls + token_ids_a + sep							) * [0]
							return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			self._tokenizer.model.save(UpperCamelCase_   ,       name=UpperCamelCase_							)
							return tuple(UpperCamelCase_							)