





from dataclasses import dataclass
from typing import Callable, Optional

import datasets







@dataclass
class     lowerCamelCase(	datasets.BuilderConfig  ):
					'''simple docstring'''


					__magic_name__        = None
					__magic_name__        = None
					__magic_name__        = None




					def 					lowerCAmelCase__		(						self       ):
									assert self.generator is not None, "generator must be specified"

									if self.gen_kwargs is None:
													_A    =       {}







class     lowerCamelCase(	datasets.GeneratorBasedBuilder  ):
					'''simple docstring'''


					__magic_name__        = GeneratorConfig




					def 					lowerCAmelCase__		(						self       ):
									return datasets.DatasetInfo(features=self.config.features       )




					def 					lowerCAmelCase__		(						self							,    snake_case_       ):
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,    gen_kwargs=self.config.gen_kwargs       )]




					def 					lowerCAmelCase__		(						self							,    **snake_case_       ):
									for idx, ex in enumerate(self.config.generator(**snake_case_       )       ):
													yield idx, ex












from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class     lowerCamelCase(	__snake_case  ):
     '''simple docstring'''


     __magic_name__        = SqueezeBertTokenizer
     __magic_name__        = SqueezeBertTokenizerFast
     __magic_name__        = True




     def 					lowerCAmelCase__		(						self							,    **snake_case_       ):
         return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname							,    **snake_case_       )




     @slow
     def 					lowerCAmelCase__		(						self       ):
         _A    =       SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'       )

         _A    =       tokenizer.encode('sequence builders'							,    add_special_tokens=snake_case_       )
         _A    =       tokenizer.encode('multi-sequence build'							,    add_special_tokens=snake_case_       )

         _A    =       tokenizer.build_inputs_with_special_tokens(snake_case_       )
         _A    =       tokenizer.build_inputs_with_special_tokens(snake_case_							,    snake_case_       )

         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
         ]





