

'''simple docstring'''






from ...processing_utils import ProcessorMixin






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						List[str]           =    'SpeechT5FeatureExtractor'
  SCREAMING_SNAKE_CASE					:						Tuple           =    'SpeechT5Tokenizer'






  def __init__(   self     :  Any      ,lowercase__     :  List[Any]      ,lowercase__     :  Optional[Any]    ):
   super().__init__(lowercase__      ,lowercase__    )






  def __call__(   self     :  List[Any]      ,*lowercase__     :  Optional[int]      ,**lowercase__     :  Union[str, Any]    ):
   __lowercase													=				kwargs.pop('''audio'''      ,lowercase__    )
   __lowercase													=				kwargs.pop('''text'''      ,lowercase__    )
   __lowercase													=				kwargs.pop('''text_target'''      ,lowercase__    )
   __lowercase													=				kwargs.pop('''audio_target'''      ,lowercase__    )
   __lowercase													=				kwargs.pop('''sampling_rate'''      ,lowercase__    )

   if audio is not None and text is not None:
    raise ValueError(
        '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'''    )
   if audio_target is not None and text_target is not None:
    raise ValueError(
        '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'''    )
   if audio is None and audio_target is None and text is None and text_target is None:
    raise ValueError(
        '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'''    )

   if audio is not None:
    __lowercase													=				self.feature_extractor(lowercase__      ,*lowercase__      ,sampling_rate=lowercase__      ,**lowercase__    )
   elif text is not None:
    __lowercase													=				self.tokenizer(lowercase__      ,**lowercase__    )
   else:
    __lowercase													=				None

   if audio_target is not None:
    __lowercase													=				self.feature_extractor(audio_target=lowercase__      ,*lowercase__      ,sampling_rate=lowercase__      ,**lowercase__    )
    __lowercase													=				targets['''input_values''']
   elif text_target is not None:
    __lowercase													=				self.tokenizer(lowercase__      ,**lowercase__    )
    __lowercase													=				targets['''input_ids''']
   else:
    __lowercase													=				None

   if inputs is None:
    return targets

   if targets is not None:
    __lowercase													=				labels

    __lowercase													=				targets.get('''attention_mask'''    )
    if decoder_attention_mask is not None:
     __lowercase													=				decoder_attention_mask

   return inputs






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,*lowercase__     :  int      ,**lowercase__     :  Any    ):
   __lowercase													=				kwargs.pop('''input_values'''      ,lowercase__    )
   __lowercase													=				kwargs.pop('''input_ids'''      ,lowercase__    )
   __lowercase													=				kwargs.pop('''labels'''      ,lowercase__    )

   if input_values is not None and input_ids is not None:
    raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.'''    )
   if input_values is None and input_ids is None and labels is None:
    raise ValueError(
        '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'''    )

   if input_values is not None:
    __lowercase													=				self.feature_extractor.pad(lowercase__      ,*lowercase__      ,**lowercase__    )
   elif input_ids is not None:
    __lowercase													=				self.tokenizer.pad(lowercase__      ,**lowercase__    )
   else:
    __lowercase													=				None

   if labels is not None:
    if "input_ids" in labels or (isinstance(lowercase__      ,lowercase__    ) and "input_ids" in labels[0]):
     __lowercase													=				self.tokenizer.pad(lowercase__      ,**lowercase__    )
     __lowercase													=				targets['''input_ids''']
    else:
     __lowercase													=				self.feature_extractor.feature_size
     __lowercase													=				self.feature_extractor.num_mel_bins
     __lowercase													=				self.feature_extractor.pad(lowercase__      ,*lowercase__      ,**lowercase__    )
     __lowercase													=				feature_size_hack
     __lowercase													=				targets['''input_values''']
   else:
    __lowercase													=				None

   if inputs is None:
    return targets

   if targets is not None:
    __lowercase													=				labels

    __lowercase													=				targets.get('''attention_mask'''    )
    if decoder_attention_mask is not None:
     __lowercase													=				decoder_attention_mask

   return inputs






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,*lowercase__     :  str      ,**lowercase__     :  str    ):
   return self.tokenizer.batch_decode(*lowercase__      ,**lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,*lowercase__     :  Tuple      ,**lowercase__     :  List[Any]    ):
   return self.tokenizer.decode(*lowercase__      ,**lowercase__    )





'''simple docstring'''






import re


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				re.compile(
     R'''^(?:0|94|\+94|0{2}94)''' R'''7(0|1|2|4|5|6|7|8)''' R'''(-| |)''' R'''\d{7}$'''   )

 return bool(re.search(A__  ,       A__   )   )


if __name__ == "__main__":
 lowerCAmelCase__      		=			'''0094702343221'''

 print(is_sri_lankan_phone_number(phone))

