


'''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			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: str							  =		DistilBertTokenizer
 __lowerCAmelCase						: Optional[int]							  =		DistilBertTokenizerFast
 __lowerCAmelCase						: Optional[int]							  =		True




 @slow
 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a					:  Optional[Any]						=  DistilBertTokenizer.from_pretrained('distilbert-base-uncased'      )

    _a					:  Optional[int]						=  tokenizer.encode('sequence builders'		,      add_special_tokens=lowerCamelCase_      )
    _a					:  Any						=  tokenizer.encode('multi-sequence build'		,      add_special_tokens=lowerCamelCase_      )

    _a					:  Dict						=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase_      )
    _a					:  Optional[int]						=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase_		,      lowerCamelCase_      )

    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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[int]							  =		"""ClapFeatureExtractor"""
 __lowerCAmelCase						: str							  =		("""RobertaTokenizer""", """RobertaTokenizerFast""")




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    Union[str, Any]:
    super().__init__(lowerCamelCase_		,      lowerCamelCase_      )




 def __call__(    self		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      **lowerCamelCase_      )      ->    List[Any]:
    _a					:  Any						=  kwargs.pop('sampling_rate'		,      lowerCamelCase_      )

    if text is None and audios is None:
       raise ValueError('You have to specify either text or audios. Both cannot be none.'      )

    if text is not None:
       _a					:  Union[str, Any]						=  self.tokenizer(lowerCamelCase_		,      return_tensors=lowerCamelCase_		,      **lowerCamelCase_      )

    if audios is not None:
       _a					:  List[str]						=  self.feature_extractor(
           lowerCamelCase_		,      sampling_rate=lowerCamelCase_		,      return_tensors=lowerCamelCase_		,      **lowerCamelCase_      )

    if text is not None and audios is not None:
       _a					:  List[Any]						=  audio_features.input_features
       return encoding
    elif text is not None:
       return encoding
    else:
       return BatchEncoding(data=dict(**lowerCamelCase_      )		,      tensor_type=lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    Tuple:
    return self.tokenizer.batch_decode(*lowerCamelCase_		,      **lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    List[str]:
    return self.tokenizer.decode(*lowerCamelCase_		,      **lowerCamelCase_      )




 @property
 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  Dict						=  self.tokenizer.model_input_names
    _a					:  Tuple						=  self.feature_extractor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names      )      )


