


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A			=						{
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
       'RobertaPreLayerNormForCausalLM',
       'RobertaPreLayerNormForMaskedLM',
       'RobertaPreLayerNormForMultipleChoice',
       'RobertaPreLayerNormForQuestionAnswering',
       'RobertaPreLayerNormForSequenceClassification',
       'RobertaPreLayerNormForTokenClassification',
       'RobertaPreLayerNormModel',
       'RobertaPreLayerNormPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFRobertaPreLayerNormForCausalLM',
       'TFRobertaPreLayerNormForMaskedLM',
       'TFRobertaPreLayerNormForMultipleChoice',
       'TFRobertaPreLayerNormForQuestionAnswering',
       'TFRobertaPreLayerNormForSequenceClassification',
       'TFRobertaPreLayerNormForTokenClassification',
       'TFRobertaPreLayerNormMainLayer',
       'TFRobertaPreLayerNormModel',
       'TFRobertaPreLayerNormPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'FlaxRobertaPreLayerNormForCausalLM',
       'FlaxRobertaPreLayerNormForMaskedLM',
       'FlaxRobertaPreLayerNormForMultipleChoice',
       'FlaxRobertaPreLayerNormForQuestionAnswering',
       'FlaxRobertaPreLayerNormForSequenceClassification',
       'FlaxRobertaPreLayerNormForTokenClassification',
       'FlaxRobertaPreLayerNormModel',
       'FlaxRobertaPreLayerNormPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_roberta_prelayernorm import (
       ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
       RobertaPreLayerNormConfig,
       RobertaPreLayerNormOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roberta_prelayernorm import (
          ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
          RobertaPreLayerNormForCausalLM,
          RobertaPreLayerNormForMaskedLM,
          RobertaPreLayerNormForMultipleChoice,
          RobertaPreLayerNormForQuestionAnswering,
          RobertaPreLayerNormForSequenceClassification,
          RobertaPreLayerNormForTokenClassification,
          RobertaPreLayerNormModel,
          RobertaPreLayerNormPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roberta_prelayernorm import (
          TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRobertaPreLayerNormForCausalLM,
          TFRobertaPreLayerNormForMaskedLM,
          TFRobertaPreLayerNormForMultipleChoice,
          TFRobertaPreLayerNormForQuestionAnswering,
          TFRobertaPreLayerNormForSequenceClassification,
          TFRobertaPreLayerNormForTokenClassification,
          TFRobertaPreLayerNormMainLayer,
          TFRobertaPreLayerNormModel,
          TFRobertaPreLayerNormPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roberta_prelayernorm import (
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormModel,
          FlaxRobertaPreLayerNormPreTrainedModel,
      )

else:
   import sys

   _A			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]              =  "MCTCTFeatureExtractor"
    UpperCAmelCase__						:		str              =  "AutoTokenizer"






    def __init__(      self ,       A_ ,       A_   )  ->	Dict:
         super().__init__(A_ ,       A_   )
         __UpperCamelCase   							=self.feature_extractor
         __UpperCamelCase   							=False






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

         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' ,       A_   )
         __UpperCamelCase   							=kwargs.pop('sampling_rate' ,       A_   )
         __UpperCamelCase   							=kwargs.pop('text' ,       A_   )
         if len(A_   ) > 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(A_ ,       *A_ ,       sampling_rate=A_ ,       **A_   )
         if text is not None:
              __UpperCamelCase   							=self.tokenizer(A_ ,       **A_   )

         if text is None:
              return inputs
         elif audio is None:
              return encodings
         else:
              __UpperCamelCase   							=encodings['input_ids']
              return inputs






    def _a (      self ,       *A_ ,       **A_   )  ->	str:
         return self.tokenizer.batch_decode(*A_ ,       **A_   )






    def _a (      self ,       *A_ ,       **A_   )  ->	List[Any]:
         # For backward compatibility
         if self._in_target_context_manager:
              return self.current_processor.pad(*A_ ,       **A_   )

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

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

         if labels is None:
              return input_features
         elif input_features is None:
              return labels
         else:
              __UpperCamelCase   							=labels['input_ids']
              return input_features






    def _a (      self ,       *A_ ,       **A_   )  ->	Optional[int]:
         return self.tokenizer.decode(*A_ ,       **A_   )






    @contextmanager
    def _a (      self   )  ->	str:
         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





