import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







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







  def __init__(							self       :				Tuple     ,   __magic_name__       :				str     ,   __magic_name__       :				Dict      )     ->		List[str]:
        super().__init__(__magic_name__     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.feature_extractor
        SCREAMING_SNAKE_CASE_      =     False







  @classmethod
  def       __A   (							cls       :				int     ,   __magic_name__       :				Union[str, Any]     ,   **__magic_name__       :				Optional[int]      )     ->		Any:
        try:
              return super().from_pretrained(__magic_name__     ,   **__magic_name__      )
        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: "     ,   __magic_name__     ,   )

              SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(__magic_name__     ,   **__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     WavaVecaCTCTokenizer.from_pretrained(__magic_name__     ,   **__magic_name__      )

              return cls(feature_extractor=__magic_name__     ,   tokenizer=__magic_name__      )







  def __call__(							self       :				Union[str, Any]     ,   *__magic_name__       :				Optional[Any]     ,   **__magic_name__       :				Union[str, Any]      )     ->		Tuple:
        # For backward compatibility
        if self._in_target_context_manager:
              return self.current_processor(*__magic_name__     ,   **__magic_name__      )

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

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







  def       __A   (							self       :				Optional[int]     ,   *__magic_name__       :				int     ,   **__magic_name__       :				int      )     ->		Tuple:
        # For backward compatibility
        if self._in_target_context_manager:
              return self.current_processor.pad(*__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     kwargs.pop("input_features"     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     kwargs.pop("labels"     ,   __magic_name__      )
        if len(__magic_name__      ) > 0:
              SCREAMING_SNAKE_CASE_      =     args[0]
              SCREAMING_SNAKE_CASE_      =     args[1:]

        if input_features is not None:
              SCREAMING_SNAKE_CASE_      =     self.feature_extractor.pad(__magic_name__     ,   *__magic_name__     ,   **__magic_name__      )
        if labels is not None:
              SCREAMING_SNAKE_CASE_      =     self.tokenizer.pad(__magic_name__     ,   **__magic_name__      )

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







  def       __A   (							self       :				Union[str, Any]     ,   *__magic_name__       :				Union[str, Any]     ,   **__magic_name__       :				List[Any]      )     ->		Dict:
        return self.tokenizer.batch_decode(*__magic_name__     ,   **__magic_name__      )







  def       __A   (							self       :				List[str]     ,   *__magic_name__       :				Tuple     ,   **__magic_name__       :				Dict      )     ->		Dict:
        return self.tokenizer.decode(*__magic_name__     ,   **__magic_name__      )







  @contextmanager
  def       __A   (							self       :				Optional[Any]      )     ->		List[Any]:
        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."      )
        SCREAMING_SNAKE_CASE_      =     True
        SCREAMING_SNAKE_CASE_      =     self.tokenizer
        yield
        SCREAMING_SNAKE_CASE_      =     self.feature_extractor
        SCREAMING_SNAKE_CASE_      =     False



def    a__       (						__UpperCamelCase ):
      if not head:
            return True
      # split the list to two parts
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     head.next, head
      while fast and fast.next:
            SCREAMING_SNAKE_CASE_      =     fast.next.next
            SCREAMING_SNAKE_CASE_      =     slow.next
      SCREAMING_SNAKE_CASE_      =     slow.next
      SCREAMING_SNAKE_CASE_      =     None  # Don't forget here! But forget still works!
      # reverse the second part
      SCREAMING_SNAKE_CASE_      =     None
      while second:
            SCREAMING_SNAKE_CASE_      =     second.next
            SCREAMING_SNAKE_CASE_      =     node
            SCREAMING_SNAKE_CASE_      =     second
            SCREAMING_SNAKE_CASE_      =     nxt
      # compare two parts
      # second part has the same or one less node
      while node:
            if node.val != head.val:
                  return False
            SCREAMING_SNAKE_CASE_      =     node.next
            SCREAMING_SNAKE_CASE_      =     head.next
      return True


def    a__       (						__UpperCamelCase ):
      if not head or not head.next:
            return True

      # 1. Get the midpoint (slow)
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     head
      while fast and fast.next:
            SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     fast.next.next, slow.next

      # 2. Push the second half into the stack
      SCREAMING_SNAKE_CASE_      =     [slow.val]
      while slow.next:
            SCREAMING_SNAKE_CASE_      =     slow.next
            stack.append(slow.val )

      # 3. Comparison
      while stack:
            if stack.pop() != cur.val:
                  return False
            SCREAMING_SNAKE_CASE_      =     cur.next

      return True





def    a__       (						__UpperCamelCase ):
      if not head or not head.next:
            return True
      SCREAMING_SNAKE_CASE_      =     {}
      SCREAMING_SNAKE_CASE_      =     0
      while head:
            if head.val in d:
                  d[head.val].append(__UpperCamelCase )
            else:
                  SCREAMING_SNAKE_CASE_      =     [pos]
            SCREAMING_SNAKE_CASE_      =     head.next
            pos += 1
      SCREAMING_SNAKE_CASE_      =     pos - 1
      SCREAMING_SNAKE_CASE_      =     0
      for v in d.values():
            if len(__UpperCamelCase ) % 2 != 0:
                  middle += 1
            else:
                  SCREAMING_SNAKE_CASE_      =     0
                  for i in range(0   ,					len(__UpperCamelCase ) ):
                        if v[i] + v[len(__UpperCamelCase ) - 1 - step] != checksum:
                              return False
                        step += 1
            if middle > 1:
                  return False
      return True


