
from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				"EncodecFeatureExtractor"
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				("T5Tokenizer", "T5TokenizerFast")
   def __init__(self			: Union[str, Any] ,    snake_case_			: int ,    snake_case_			: int       ):
    super().__init__(snake_case_ ,    snake_case_       )
    __a			:  int		    =  self.feature_extractor
    __a			:  Tuple		    =  False
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Any=None ,    snake_case_			: List[str]=None ,    snake_case_			: Dict=True       ):
    return self.tokenizer.get_decoder_prompt_ids(task=snake_case_ ,    language=snake_case_ ,    no_timestamps=snake_case_       )
   def __call__(self			: Optional[int] ,    *snake_case_			: List[str] ,    **snake_case_			: str       ):
    # For backward compatibility
    if self._in_target_context_manager:
     return self.current_processor(*snake_case_ ,    **snake_case_       )

    __a			:  List[str]		    =  kwargs.pop('''audio''' ,    snake_case_       )
    __a			:  str		    =  kwargs.pop('''sampling_rate''' ,    snake_case_       )
    __a			:  int		    =  kwargs.pop('''text''' ,    snake_case_       )
    if len(snake_case_       ) > 0:
     __a			:  Optional[int]		    =  args[0]
     __a			:  List[Any]		    =  args[1:]

    if audio is None and text is None:
     raise ValueError('''You need to specify either an `audio` or `text` input to process.'''       )

    if text is not None:
     __a			:  Optional[Any]		    =  self.tokenizer(snake_case_ ,    **snake_case_       )

    if audio is not None:
     __a			:  Tuple		    =  self.feature_extractor(snake_case_ ,    *snake_case_ ,    sampling_rate=snake_case_ ,    **snake_case_       )

    if audio is None:
     return inputs

    elif text is None:
     return audio_inputs

    else:
     __a			:  Tuple		    =  audio_inputs['''input_values''']
     if "padding_mask" in audio_inputs:
      __a			:  Optional[int]		    =  audio_inputs['''padding_mask''']
     return inputs
   def 				lowerCAmelCase      (self			: int ,    *snake_case_			: Dict ,    **snake_case_			: Any       ):
    __a			:  int		    =  kwargs.pop('''audio''' ,    snake_case_       )
    __a			:  int		    =  kwargs.pop('''padding_mask''' ,    snake_case_       )

    if len(snake_case_       ) > 0:
     __a			:  Any		    =  args[0]
     __a			:  str		    =  args[1:]

    if audio_values is not None:
     return self._decode_audio(snake_case_ ,    padding_mask=snake_case_       )
    else:
     return self.tokenizer.batch_decode(*snake_case_ ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: Optional[int] ,    *snake_case_			: Optional[Any] ,    **snake_case_			: Any       ):
    return self.tokenizer.decode(*snake_case_ ,    **snake_case_       )







   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: str ,    snake_case_			: Optional = None       ):
    __a			:  Optional[Any]		    =  to_numpy(snake_case_       )
    __a    ,	__a    ,	__a			:  Any		    =  audio_values.shape

    if padding_mask is None:
     return list(snake_case_       )

    __a			:  List[Any]		    =  to_numpy(snake_case_       )

    # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
    # token (so that the generated audio values are **not** treated as padded tokens)
    __a			:  int		    =  seq_len - padding_mask.shape[-1]
    __a			:  Tuple		    =  1 - self.feature_extractor.padding_value
    __a			:  Optional[Any]		    =  np.pad(snake_case_ ,    ((0, 0), (0, difference)) ,    '''constant''' ,    constant_values=snake_case_       )

    __a			:  List[Any]		    =  audio_values.tolist()
    for i in range(snake_case_       ):
     __a			:  str		    =  np.asarray(audio_values[i]       )[
         padding_mask[i][None, :] != self.feature_extractor.padding_value
     ]
     __a			:  str		    =  sliced_audio.reshape(snake_case_ ,    -1       )

    return audio_values


def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    str  ):
 __a			:  Any		    =  len(lowerCAmelCase__  )
 __a			:  Union[str, Any]		    =  []
 for i in range(len(lowerCAmelCase__  ) - pat_len + 1  ):
  __a			:  List[Any]		    =  True
  for j in range(lowerCAmelCase__  ):
   if s[i + j] != pattern[j]:
    __a			:  Union[str, Any]		    =  False
    break
  if match_found:
   position.append(lowerCAmelCase__  )
 return position


if __name__ == "__main__":
      assert naive_pattern_search('ABCDEFG', 'DE') == [3]
      print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))
