


from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							int	   =       'EncodecFeatureExtractor'
     __lowercase				:							Dict	   =       ('T5Tokenizer', 'T5TokenizerFast')

     def __init__(					self:Optional[int]     ,		_a:Union[str, Any]     ,		_a:Tuple   ):
       super().__init__(_a     ,		_a   )
       snake_case__						= self.feature_extractor
       snake_case__						= False

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:Optional[int]=None     ,		_a:Tuple=None     ,		_a:List[Any]=True   ):
       return self.tokenizer.get_decoder_prompt_ids(task=_a     ,		language=_a     ,		no_timestamps=_a   )

     def __call__(					self:List[Any]     ,		*_a:Dict     ,		**_a:Union[str, Any]   ):
       # For backward compatibility
       if self._in_target_context_manager:
         return self.current_processor(*_a     ,		**_a   )

       snake_case__						= kwargs.pop('''audio'''     ,		_a   )
       snake_case__						= kwargs.pop('''sampling_rate'''     ,		_a   )
       snake_case__						= kwargs.pop('''text'''     ,		_a   )
       if len(_a   ) > 0:
         snake_case__						= args[0]
         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 text is not None:
         snake_case__						= self.tokenizer(_a     ,		**_a   )

       if audio is not None:
         snake_case__						= self.feature_extractor(_a     ,		*_a     ,		sampling_rate=_a     ,		**_a   )

       if audio is None:
         return inputs

       elif text is None:
         return audio_inputs

       else:
         snake_case__						= audio_inputs['''input_values''']
         if "padding_mask" in audio_inputs:
           snake_case__						= audio_inputs['''padding_mask''']
         return inputs

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		*_a:List[str]     ,		**_a:List[Any]   ):
       snake_case__						= kwargs.pop('''audio'''     ,		_a   )
       snake_case__						= kwargs.pop('''padding_mask'''     ,		_a   )

       if len(_a   ) > 0:
         snake_case__						= args[0]
         snake_case__						= args[1:]

       if audio_values is not None:
         return self._decode_audio(_a     ,		padding_mask=_a   )
       else:
         return self.tokenizer.batch_decode(*_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		*_a:Optional[int]     ,		**_a:Dict   ):
       return self.tokenizer.decode(*_a     ,		**_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:Optional[Any]     ,		_a:Optional = None   ):
       snake_case__						= to_numpy(_a   )
       snake_case__				,				snake_case__				,				snake_case__						= audio_values.shape

       if padding_mask is None:
         return list(_a   )

       snake_case__						= to_numpy(_a   )

       # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
       # token (so that the generated audio values are **not** treated as padded tokens)
       snake_case__						= seq_len - padding_mask.shape[-1]
       snake_case__						= 1 - self.feature_extractor.padding_value
       snake_case__						= np.pad(_a     ,		((0, 0), (0, difference))     ,		'''constant'''     ,		constant_values=_a   )

       snake_case__						= audio_values.tolist()
       for i in range(_a   ):
         snake_case__						= np.asarray(audio_values[i]   )[
             padding_mask[i][None, :] != self.feature_extractor.padding_value
         ]
         snake_case__						= sliced_audio.reshape(_a     ,		-1   )

       return audio_values





from typing import TYPE_CHECKING

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


lowerCamelCase__							:						Tuple							     =   {
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						Tuple							     =   ["""RobertaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						Optional[int]							     =   [
       """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """RobertaForCausalLM""",
       """RobertaForMaskedLM""",
       """RobertaForMultipleChoice""",
       """RobertaForQuestionAnswering""",
       """RobertaForSequenceClassification""",
       """RobertaForTokenClassification""",
       """RobertaModel""",
       """RobertaPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						List[str]							     =   [
       """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFRobertaForCausalLM""",
       """TFRobertaForMaskedLM""",
       """TFRobertaForMultipleChoice""",
       """TFRobertaForQuestionAnswering""",
       """TFRobertaForSequenceClassification""",
       """TFRobertaForTokenClassification""",
       """TFRobertaMainLayer""",
       """TFRobertaModel""",
       """TFRobertaPreTrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						str							     =   [
       """FlaxRobertaForCausalLM""",
       """FlaxRobertaForMaskedLM""",
       """FlaxRobertaForMultipleChoice""",
       """FlaxRobertaForQuestionAnswering""",
       """FlaxRobertaForSequenceClassification""",
       """FlaxRobertaForTokenClassification""",
       """FlaxRobertaModel""",
       """FlaxRobertaPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
   from .tokenization_roberta import RobertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roberta_fast import RobertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roberta import (
          ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          RobertaForCausalLM,
          RobertaForMaskedLM,
          RobertaForMultipleChoice,
          RobertaForQuestionAnswering,
          RobertaForSequenceClassification,
          RobertaForTokenClassification,
          RobertaModel,
          RobertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roberta import (
          TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRobertaForCausalLM,
          TFRobertaForMaskedLM,
          TFRobertaForMultipleChoice,
          TFRobertaForQuestionAnswering,
          TFRobertaForSequenceClassification,
          TFRobertaForTokenClassification,
          TFRobertaMainLayer,
          TFRobertaModel,
          TFRobertaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roberta import (
          FlaxRobertaForCausalLM,
          FlaxRobertaForMaskedLM,
          FlaxRobertaForMultipleChoice,
          FlaxRobertaForQuestionAnswering,
          FlaxRobertaForSequenceClassification,
          FlaxRobertaForTokenClassification,
          FlaxRobertaModel,
          FlaxRobertaPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase__							:						List[Any]							     =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

