





'''simple docstring'''






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   lowerCamelCase__    							=      '''Wav2Vec2FeatureExtractor'''
   lowerCamelCase__    							=      '''AutoTokenizer'''

   def __init__(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    super().__init__(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Optional[int]    =  self.feature_extractor
    snake_case__     :					Optional[Any]    =  False

   @classmethod
   def 		__UpperCamelCase						(							cls	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ):
    try:
     return super().from_pretrained(__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
    except OSError:
     warnings.warn(
         f"Loading a tokenizer inside {cls.__name__} from a config that does not"
         """ include a `tokenizer_class` attribute is deprecated and will be """
         """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
         """ attribute to either your `config.json` or `tokenizer_config.json` """
         """file to suppress this warning: """	,		__SCREAMING_SNAKE_CASE	,		)

     snake_case__     :					List[str]    =  WavaVecaFeatureExtractor.from_pretrained(__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
     snake_case__     :					Optional[Any]    =  WavaVecaCTCTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )

     return cls(feature_extractor=__SCREAMING_SNAKE_CASE	,		tokenizer=__SCREAMING_SNAKE_CASE       )

   def __call__(							self	,		*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ):
    # For backward compatibility
    if self._in_target_context_manager:
     return self.current_processor(*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )

    if "raw_speech" in kwargs:
     warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""       )
     snake_case__     :					Optional[Any]    =  kwargs.pop("""raw_speech"""       )
    else:
     snake_case__     :					Union[str, Any]    =  kwargs.pop("""audio"""	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Union[str, Any]    =  kwargs.pop("""sampling_rate"""	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Dict    =  kwargs.pop("""text"""	,		__SCREAMING_SNAKE_CASE       )
    if len(__SCREAMING_SNAKE_CASE       ) > 0:
     snake_case__     :					Optional[Any]    =  args[0]
     snake_case__     :					Dict    =  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 audio is not None:
     snake_case__     :					Optional[int]    =  self.feature_extractor(__SCREAMING_SNAKE_CASE	,		*__SCREAMING_SNAKE_CASE	,		sampling_rate=__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
    if text is not None:
     snake_case__     :					Any    =  self.tokenizer(__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )

    if text is None:
     return inputs
    elif audio is None:
     return encodings
    else:
     snake_case__     :					List[Any]    =  encodings["""input_ids"""]
     return inputs

   def 		__UpperCamelCase						(							self	,		*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ):
    # For backward compatibility
    if self._in_target_context_manager:
     return self.current_processor.pad(*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Any    =  kwargs.pop("""input_features"""	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					str    =  kwargs.pop("""labels"""	,		__SCREAMING_SNAKE_CASE       )
    if len(__SCREAMING_SNAKE_CASE       ) > 0:
     snake_case__     :					Dict    =  args[0]
     snake_case__     :					int    =  args[1:]

    if input_features is not None:
     snake_case__     :					Any    =  self.feature_extractor.pad(__SCREAMING_SNAKE_CASE	,		*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
    if labels is not None:
     snake_case__     :					List[Any]    =  self.tokenizer.pad(__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )

    if labels is None:
     return input_features
    elif input_features is None:
     return labels
    else:
     snake_case__     :					Tuple    =  labels["""input_ids"""]
     return input_features

   def 		__UpperCamelCase						(							self	,		*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ):
    return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ):
    return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )


   @contextmanager
   def 		__UpperCamelCase						(							self       ):
    warnings.warn(
        """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
        """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
        """your audio inputs, or in a separate call."""       )
    snake_case__     :					List[str]    =  True
    snake_case__     :					Any    =  self.tokenizer
    yield
    snake_case__     :					str    =  self.feature_extractor
    snake_case__     :					str    =  False







"""simple docstring"""

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


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






