




import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


UpperCamelCase								=					get_logger(__name__)


UpperCamelCase								=					R'\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'





class 			__lowerCamelCase	:




    """simple docstring"""

    @add_start_docstrings(SCREAMING_SNAKE_CASE__       )
    def __call__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       )				->	jnp.ndarray:
      raise NotImplementedError(
          f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'       )






class 			__lowerCamelCase	:




    """simple docstring"""

    @add_start_docstrings(SCREAMING_SNAKE_CASE__       )
    def __call__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       )				->	jnp.ndarray:
      raise NotImplementedError(
          f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'       )






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    @add_start_docstrings(SCREAMING_SNAKE_CASE__       )
    def __call__(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					int       ,     **SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	jnp.ndarray:
      for processor in self:
        lowerCAmelCase__          =			inspect.signature(processor.__call__       ).parameters
        if len(SCREAMING_SNAKE_CASE__       ) > 3:
          if not all(arg in kwargs for arg in list(function_args.keys()       )[2:]       ):
            raise ValueError(
                f'Make sure that all the required parameters: {list(function_args.keys()       )} for '
                f'{processor.__class__} are passed to the logits processor.'       )
          lowerCAmelCase__          =			processor(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )
        else:
          lowerCAmelCase__          =			processor(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
      return scores






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					float       )				->	Any:
      if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) or not (temperature > 0):
        raise ValueError(f'`temperature` has to be a strictly positive float, but is {temperature}'       )

      lowerCAmelCase__          =			temperature


    def __call__(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	jnp.ndarray:
      lowerCAmelCase__          =			scores / self.temperature
      return scores






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					float       ,     SCREAMING_SNAKE_CASE__					:					float = -float("Inf"       )       ,     SCREAMING_SNAKE_CASE__					:					int = 1       )				->	Tuple:
      if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) or (top_p < 0 or top_p > 1.0):
        raise ValueError(f'`top_p` has to be a float > 0 and < 1, but is {top_p}'       )
      if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) or (min_tokens_to_keep < 1):
        raise ValueError(f'`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'       )

      lowerCAmelCase__          =			top_p
      lowerCAmelCase__          =			filter_value
      lowerCAmelCase__          =			min_tokens_to_keep


    def __call__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	jnp.ndarray:
      lowerCAmelCase__     ,		lowerCAmelCase__          =			lax.top_k(SCREAMING_SNAKE_CASE__       ,     scores.shape[-1]       )

      lowerCAmelCase__          =			jnp.full_like(SCREAMING_SNAKE_CASE__       ,     self.filter_value       )
      lowerCAmelCase__          =			jax.nn.softmax(SCREAMING_SNAKE_CASE__       ,     axis=-1       ).cumsum(axis=-1       )
      lowerCAmelCase__          =			cumulative_probs < self.top_p

      # include the token that is higher than top_p as well
      lowerCAmelCase__          =			jnp.roll(SCREAMING_SNAKE_CASE__       ,     1       )
      score_mask |= score_mask.at[:, 0].set(SCREAMING_SNAKE_CASE__       )

      # min tokens to keep
      lowerCAmelCase__          =			score_mask.at[:, : self.min_tokens_to_keep].set(SCREAMING_SNAKE_CASE__       )

      lowerCAmelCase__          =			jnp.where(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
      lowerCAmelCase__          =			jax.lax.sort_key_val(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )[-1]

      return next_scores






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					float = -float("Inf"       )       ,     SCREAMING_SNAKE_CASE__					:					int = 1       )				->	Optional[int]:
      if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) or top_k <= 0:
        raise ValueError(f'`top_k` has to be a strictly positive integer, but is {top_k}'       )

      lowerCAmelCase__          =			max(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
      lowerCAmelCase__          =			filter_value


    def __call__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	jnp.ndarray:
      lowerCAmelCase__     ,		lowerCAmelCase__          =			scores.shape
      lowerCAmelCase__          =			jnp.full(batch_size * vocab_size       ,     self.filter_value       )

      lowerCAmelCase__          =			min(self.top_k       ,     scores.shape[-1]       )  # Safety check
      lowerCAmelCase__     ,		lowerCAmelCase__          =			lax.top_k(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
      lowerCAmelCase__          =			jnp.broadcast_to((jnp.arange(SCREAMING_SNAKE_CASE__       ) * vocab_size)[:, None]       ,     (batch_size, topk)       ).flatten()
      lowerCAmelCase__          =			topk_scores.flatten()
      lowerCAmelCase__          =			topk_indices.flatten() + shift

      lowerCAmelCase__          =			next_scores_flat.at[topk_indices_flat].set(SCREAMING_SNAKE_CASE__       )
      lowerCAmelCase__          =			next_scores_flat.reshape(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
      return next_scores






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	List[str]:
      lowerCAmelCase__          =			bos_token_id


    def __call__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	jnp.ndarray:
      lowerCAmelCase__          =			jnp.full(scores.shape       ,     -float("inf"       )       )

      lowerCAmelCase__          =			1 - jnp.bool_(cur_len - 1       )

      lowerCAmelCase__          =			jnp.where(SCREAMING_SNAKE_CASE__       ,     new_scores.at[:, self.bos_token_id].set(0       )       ,     SCREAMING_SNAKE_CASE__       )

      return scores






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	int:
      lowerCAmelCase__          =			max_length
      lowerCAmelCase__          =			eos_token_id


    def __call__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	jnp.ndarray:
      lowerCAmelCase__          =			jnp.full(scores.shape       ,     -float("inf"       )       )

      lowerCAmelCase__          =			1 - jnp.bool_(cur_len - self.max_length + 1       )

      lowerCAmelCase__          =			jnp.where(SCREAMING_SNAKE_CASE__       ,     new_scores.at[:, self.eos_token_id].set(0       )       ,     SCREAMING_SNAKE_CASE__       )

      return scores






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Optional[int]:
      if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) or min_length < 0:
        raise ValueError(f'`min_length` has to be a positive integer, but is {min_length}'       )

      if not isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) or eos_token_id < 0:
        raise ValueError(f'`eos_token_id` has to be a positive integer, but is {eos_token_id}'       )

      lowerCAmelCase__          =			min_length
      lowerCAmelCase__          =			eos_token_id


    def __call__(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	jnp.ndarray:
      # create boolean flag to decide if min length penalty should be applied
      lowerCAmelCase__          =			1 - jnp.clip(cur_len - self.min_length       ,     0       ,     1       )

      lowerCAmelCase__          =			jnp.where(SCREAMING_SNAKE_CASE__       ,     scores.at[:, self.eos_token_id].set(-float("inf"       )       )       ,     SCREAMING_SNAKE_CASE__       )

      return scores






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Any:
      lowerCAmelCase__          =			list(SCREAMING_SNAKE_CASE__       )
      lowerCAmelCase__          =			begin_index


    def __call__(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Tuple:
      lowerCAmelCase__          =			1 - jnp.bool_(cur_len - self.begin_index       )

      lowerCAmelCase__          =			jnp.where(SCREAMING_SNAKE_CASE__       ,     scores.at[:, self.begin_suppress_tokens].set(-float("inf"       )       )       ,     SCREAMING_SNAKE_CASE__       )

      return scores






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					list       )				->	int:
      lowerCAmelCase__          =			list(SCREAMING_SNAKE_CASE__       )


    def __call__(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	jnp.ndarray:
      lowerCAmelCase__          =			scores.at[..., self.suppress_tokens].set(-float("inf"       )       )

      return scores






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	List[Any]:
      lowerCAmelCase__          =			dict(SCREAMING_SNAKE_CASE__       )
      # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
      # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
      # Indexes without forced tokens will have a negative value.
      lowerCAmelCase__          =			jnp.ones((max(force_token_map.keys()       ) + 1)       ,     dtype=jnp.intaa       ) * -1
      for index, token in force_token_map.items():
        if token is not None:
          lowerCAmelCase__          =			force_token_array.at[index].set(SCREAMING_SNAKE_CASE__       )
      lowerCAmelCase__          =			jnp.intaa(SCREAMING_SNAKE_CASE__       )


    def __call__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					jnp.ndarray       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	jnp.ndarray:
      def _force_token(SCREAMING_SNAKE_CASE__					:					Any       ):
        lowerCAmelCase__          =			scores.shape[0]
        lowerCAmelCase__          =			self.force_token_array[generation_idx]

        lowerCAmelCase__          =			jnp.ones_like(SCREAMING_SNAKE_CASE__       ,     dtype=scores.dtype       ) * -float("inf"       )
        lowerCAmelCase__          =			jnp.zeros((batch_size, 1)       ,     dtype=scores.dtype       )
        lowerCAmelCase__          =			lax.dynamic_update_slice(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     (0, current_token)       )
        return new_scores

      lowerCAmelCase__          =			lax.cond(
          cur_len >= self.force_token_array.shape[0]       ,     lambda: scores       ,     lambda: lax.cond(
              self.force_token_array[cur_len] >= 0       ,     lambda: _force_token(SCREAMING_SNAKE_CASE__       )       ,     lambda: scores       ,     )       ,     )
      return scores






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Dict:
      lowerCAmelCase__          =			generate_config.eos_token_id
      lowerCAmelCase__          =			generate_config.no_timestamps_token_id
      lowerCAmelCase__          =			generate_config.no_timestamps_token_id + 1

      lowerCAmelCase__          =			decoder_input_length + 1

      if generate_config.is_multilingual:
        # room for language token and task token
        self.begin_index += 2
      if hasattr(SCREAMING_SNAKE_CASE__       ,     "max_initial_timestamp_index"       ):
        lowerCAmelCase__          =			generate_config.max_initial_timestamp_index
      else:
        lowerCAmelCase__          =			model_config.vocab_size
      if self.max_initial_timestamp_index is None:
        lowerCAmelCase__          =			model_config.vocab_size


    def __call__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Optional[int]:
      # suppress <|notimestamps|> which is handled by without_timestamps
      lowerCAmelCase__          =			scores.at[:, self.no_timestamps_token_id].set(-float("inf"       )       )

      def handle_pairs(SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					int       ):
        lowerCAmelCase__          =			jnp.where((cur_len - self.begin_index) >= 1       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
        lowerCAmelCase__          =			jnp.where(
            input_ids_k[cur_len - 1] >= self.timestamp_begin       ,     True and last_was_timestamp       ,     SCREAMING_SNAKE_CASE__       ,     )

        lowerCAmelCase__          =			jnp.where((cur_len - self.begin_index) < 2       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
        lowerCAmelCase__          =			jnp.where(
            input_ids_k[cur_len - 2] >= self.timestamp_begin       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     )

        return jnp.where(
            SCREAMING_SNAKE_CASE__       ,     jnp.where(
                penultimate_was_timestamp > 0       ,     scores_k.at[self.timestamp_begin :].set(-float("inf"       )       )       ,     scores_k.at[: self.eos_token_id].set(-float("inf"       )       )       ,     )       ,     SCREAMING_SNAKE_CASE__       ,     )

      lowerCAmelCase__          =			jax.vmap(SCREAMING_SNAKE_CASE__       )(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

      lowerCAmelCase__          =			jnp.where(cur_len == self.begin_index       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
      lowerCAmelCase__          =			jnp.where(
          self.max_initial_timestamp_index is not None       ,     True and apply_max_initial_timestamp       ,     SCREAMING_SNAKE_CASE__       ,     )

      lowerCAmelCase__          =			self.timestamp_begin + self.max_initial_timestamp_index

      lowerCAmelCase__          =			jnp.where(
          SCREAMING_SNAKE_CASE__       ,     scores.at[:, last_allowed + 1 :].set(-float("inf"       )       )       ,     SCREAMING_SNAKE_CASE__       ,     )

      # if sum of probability over timestamps is above any other token, sample timestamp
      lowerCAmelCase__          =			jax.nn.log_softmax(SCREAMING_SNAKE_CASE__       ,     axis=-1       )

      def handle_cumulative_probs(SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       ):
        lowerCAmelCase__          =			jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]       ,     axis=-1       )
        lowerCAmelCase__          =			jnp.max(logprobs_k[: self.timestamp_begin]       )
        return jnp.where(
            timestamp_logprob > max_text_token_logprob       ,     scores_k.at[: self.timestamp_begin].set(-float("inf"       )       )       ,     SCREAMING_SNAKE_CASE__       ,     )

      lowerCAmelCase__          =			jax.vmap(SCREAMING_SNAKE_CASE__       )(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

      return scores






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				"Speech2TextFeatureExtractor"
    snake_case__ =				"Speech2TextTokenizer"


    def __init__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Optional[Any]:
         super().__init__(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.feature_extractor
         lowerCAmelCase__          =			False


    def __call__(	self					:					str       ,     *SCREAMING_SNAKE_CASE__					:					int       ,     **SCREAMING_SNAKE_CASE__					:					int       )				->	Optional[Any]:
         # 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."       )
              lowerCAmelCase__          =			kwargs.pop("raw_speech"       )
         else:
              lowerCAmelCase__          =			kwargs.pop("audio"       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			kwargs.pop("sampling_rate"       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			kwargs.pop("text"       ,     SCREAMING_SNAKE_CASE__       )
         if len(SCREAMING_SNAKE_CASE__       ) > 0:
              lowerCAmelCase__          =			args[0]
              lowerCAmelCase__          =			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:
              lowerCAmelCase__          =			self.feature_extractor(SCREAMING_SNAKE_CASE__       ,     *SCREAMING_SNAKE_CASE__       ,     sampling_rate=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )
         if text is not None:
              lowerCAmelCase__          =			self.tokenizer(SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )

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


    def        a				(	self					:					str       ,     *SCREAMING_SNAKE_CASE__					:					Dict       ,     **SCREAMING_SNAKE_CASE__					:					int       )				->	Union[str, Any]:
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Dict       ,     *SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     **SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	Optional[Any]:
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    @contextmanager
    def        a				(	self					:					Union[str, Any]       )				->	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."       )
         lowerCAmelCase__          =			True
         lowerCAmelCase__          =			self.tokenizer
         yield
         lowerCAmelCase__          =			self.feature_extractor
         lowerCAmelCase__          =			False
