


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


lowerCAmelCase__  :List[Any]             =							get_logger(__name__)


lowerCAmelCase__  :List[str]             =							R'''
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

'''

class 		__a				:
		@add_start_docstrings(_SCREAMING_SNAKE_CASE						)
		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									raise NotImplementedError(
									    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''						)



class 		__a				:
		@add_start_docstrings(_SCREAMING_SNAKE_CASE						)
		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									raise NotImplementedError(
									    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''						)



class 		__a				(    UpperCAmelCase      ):
		@add_start_docstrings(_SCREAMING_SNAKE_CASE						)
		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									for processor in self:
																_UpperCAmelCase					=      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.'''						)
																							_UpperCAmelCase					=      processor(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
																else:
																							_UpperCAmelCase					=      processor(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									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}'''						)

									_UpperCAmelCase					=      temperature






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      scores / self.temperature
									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = -float('Inf'						)      ,   _SCREAMING_SNAKE_CASE = 1						)			->	List[Any]:




									"""simple docstring"""





									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}'''						)

									_UpperCAmelCase					=      top_p
									_UpperCAmelCase					=      filter_value
									_UpperCAmelCase					=      min_tokens_to_keep






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      lax.top_k(_SCREAMING_SNAKE_CASE      ,   scores.shape[-1]						)

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

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

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

									_UpperCAmelCase					=      jnp.where(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      jax.lax.sort_key_val(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)[-1]

									return next_scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = -float('Inf'						)      ,   _SCREAMING_SNAKE_CASE = 1						)			->	int:




									"""simple docstring"""





									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}'''						)

									_UpperCAmelCase					=      max(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      filter_value






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      scores.shape
									_UpperCAmelCase					=      jnp.full(batch_size * vocab_size      ,   self.filter_value						)

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

									_UpperCAmelCase					=      next_scores_flat.at[topk_indices_flat].set(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      next_scores_flat.reshape(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									return next_scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      bos_token_id






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      jnp.full(scores.shape      ,   -float('inf'						)						)

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

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

									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      max_length
									_UpperCAmelCase					=      eos_token_id






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      jnp.full(scores.shape      ,   -float('inf'						)						)

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

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

									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									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}'''						)

									_UpperCAmelCase					=      min_length
									_UpperCAmelCase					=      eos_token_id






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      1 - jnp.clip(cur_len - self.min_length      ,   0      ,   1						)

									_UpperCAmelCase					=      jnp.where(_SCREAMING_SNAKE_CASE      ,   scores.at[:, self.eos_token_id].set(-float('inf'						)						)      ,   _SCREAMING_SNAKE_CASE						)

									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      list(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      begin_index






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      1 - jnp.bool_(cur_len - self.begin_index						)

									_UpperCAmelCase					=      jnp.where(_SCREAMING_SNAKE_CASE      ,   scores.at[:, self.begin_suppress_tokens].set(-float('inf'						)						)      ,   _SCREAMING_SNAKE_CASE						)

									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      list(_SCREAMING_SNAKE_CASE						)






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      scores.at[..., self.suppress_tokens].set(-float('inf'						)						)

									return scores



class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      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.
									_UpperCAmelCase					=      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:
																							_UpperCAmelCase					=      force_token_array.at[index].set(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      jnp.intaa(_SCREAMING_SNAKE_CASE						)






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	jnp.ndarray:




									"""simple docstring"""





									def _force_token(_SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase					=      scores.shape[0]
																_UpperCAmelCase					=      self.force_token_array[generation_idx]

																_UpperCAmelCase					=      jnp.ones_like(_SCREAMING_SNAKE_CASE      ,   dtype=scores.dtype						) * -float('inf'						)
																_UpperCAmelCase					=      jnp.zeros((batch_size, 1)      ,   dtype=scores.dtype						)
																_UpperCAmelCase					=      lax.dynamic_update_slice(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   (0, current_token)						)
																return new_scores

									_UpperCAmelCase					=      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 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      generate_config.eos_token_id
									_UpperCAmelCase					=      generate_config.no_timestamps_token_id
									_UpperCAmelCase					=      generate_config.no_timestamps_token_id + 1

									_UpperCAmelCase					=      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'						):
																_UpperCAmelCase					=      generate_config.max_initial_timestamp_index
									else:
																_UpperCAmelCase					=      model_config.vocab_size
									if self.max_initial_timestamp_index is None:
																_UpperCAmelCase					=      model_config.vocab_size






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      scores.at[:, self.no_timestamps_token_id].set(-float('inf'						)						)

									def handle_pairs(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase					=      jnp.where((cur_len - self.begin_index) >= 1      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      jnp.where(
																    input_ids_k[cur_len - 1] >= self.timestamp_begin      ,   True and last_was_timestamp      ,   _SCREAMING_SNAKE_CASE      ,   )

																_UpperCAmelCase					=      jnp.where((cur_len - self.begin_index) < 2      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      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      ,   )

									_UpperCAmelCase					=      jax.vmap(_SCREAMING_SNAKE_CASE						)(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

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

									_UpperCAmelCase					=      self.timestamp_begin + self.max_initial_timestamp_index

									_UpperCAmelCase					=      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
									_UpperCAmelCase					=      jax.nn.log_softmax(_SCREAMING_SNAKE_CASE      ,   axis=-1						)

									def handle_cumulative_probs(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase					=      jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]      ,   axis=-1						)
																_UpperCAmelCase					=      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      ,   )

									_UpperCAmelCase					=      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 		__a				(    UpperCAmelCase      ):
		_a     :  Optional[int]           =     'MCTCTFeatureExtractor'
		_a     :  int           =     'AutoTokenizer'
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




									"""simple docstring"""





									super().__init__(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.feature_extractor
									_UpperCAmelCase					=      False
		def __call__(      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""simple docstring"""





									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.'						)
																_UpperCAmelCase					=      kwargs.pop('raw_speech'						)
									else:
																_UpperCAmelCase					=      kwargs.pop('audio'      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      kwargs.pop('sampling_rate'      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      kwargs.pop('text'      ,   _SCREAMING_SNAKE_CASE						)
									if len(_SCREAMING_SNAKE_CASE						) > 0:
																_UpperCAmelCase					=      args[0]
																_UpperCAmelCase					=      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:
																_UpperCAmelCase					=      self.feature_extractor(_SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE      ,   sampling_rate=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
									if text is not None:
																_UpperCAmelCase					=      self.tokenizer(_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)

									if text is None:
																return inputs
									elif audio is None:
																return encodings
									else:
																_UpperCAmelCase					=      encodings['input_ids']
																return inputs
		def      UpperCAmelCase__    (      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									if self._in_target_context_manager:
																return self.current_processor.pad(*_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      kwargs.pop('input_features'      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      kwargs.pop('labels'      ,   _SCREAMING_SNAKE_CASE						)
									if len(_SCREAMING_SNAKE_CASE						) > 0:
																_UpperCAmelCase					=      args[0]
																_UpperCAmelCase					=      args[1:]

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

									if labels is None:
																return input_features
									elif input_features is None:
																return labels
									else:
																_UpperCAmelCase					=      labels['input_ids']
																return input_features
		def      UpperCAmelCase__    (      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	int:




									"""simple docstring"""





									return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)






		@contextmanager
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									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.'						)
									_UpperCAmelCase					=      True
									_UpperCAmelCase					=      self.tokenizer
									yield
									_UpperCAmelCase					=      self.feature_extractor
									_UpperCAmelCase					=      False
