



'''simple docstring'''





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			int		=			DistilBertTokenizer
 __lowerCamelCase       :			Union[str, Any]		=			DistilBertTokenizerFast
 __lowerCamelCase       :			Dict		=			True



 @slow
 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''			)

      _a           =    tokenizer.encode('''sequence builders'''					,  add_special_tokens=A			)
      _a           =    tokenizer.encode('''multi-sequence build'''					,  add_special_tokens=A			)

      _a           =    tokenizer.build_inputs_with_special_tokens(A			)
      _a           =    tokenizer.build_inputs_with_special_tokens(A					,  A			)

      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]





'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=snake_case_)
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							field(default="""language-modeling"""							,       metadata={"""include_in_asdict_even_if_is_default""": True})

				lowerCAmelCase_     			=							Features({"""text""": Value("""string""")})
				lowerCAmelCase_     			=							Features({})
				lowerCAmelCase_     			=							"text"







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







											'''simple docstring'''






											return {self.text_column: "text"}




