


'''simple docstring'''






from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""EncodecFeatureExtractor"""
 _lowerCAmelCase 					=	("""T5Tokenizer""", """T5TokenizerFast""")
 def __init__( self      ,				__magic_name__      ,				__magic_name__						)					->							Optional[Any]:
      super().__init__(__magic_name__      ,				__magic_name__						)
      _a			      =							self.feature_extractor
      _a			      =							False
 def 							__UpperCAmelCase		( self      ,				__magic_name__=None      ,				__magic_name__=None      ,				__magic_name__=True						)					->							Tuple:
      return self.tokenizer.get_decoder_prompt_ids(task=__magic_name__      ,				language=__magic_name__      ,				no_timestamps=__magic_name__						)
 def __call__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							List[str]:
      # For backward compatibility
      if self._in_target_context_manager:
           return self.current_processor(*__magic_name__      ,				**__magic_name__						)

      _a			      =							kwargs.pop('audio'      ,				__magic_name__						)
      _a			      =							kwargs.pop('sampling_rate'      ,				__magic_name__						)
      _a			      =							kwargs.pop('text'      ,				__magic_name__						)
      if len(__magic_name__						) > 0:
           _a			      =							args[0]
           _a			      =							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:
           _a			      =							self.tokenizer(__magic_name__      ,				**__magic_name__						)

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

      if audio is None:
           return inputs

      elif text is None:
           return audio_inputs

      else:
           _a			      =							audio_inputs['input_values']
           if "padding_mask" in audio_inputs:
                _a			      =							audio_inputs['padding_mask']
           return inputs
 def 							__UpperCAmelCase		( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Tuple:
      _a			      =							kwargs.pop('audio'      ,				__magic_name__						)
      _a			      =							kwargs.pop('padding_mask'      ,				__magic_name__						)

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

      if audio_values is not None:
           return self._decode_audio(__magic_name__      ,				padding_mask=__magic_name__						)
      else:
           return self.tokenizer.batch_decode(*__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				*__magic_name__      ,				**__magic_name__						)					->							List[Any]:
      return self.tokenizer.decode(*__magic_name__      ,				**__magic_name__						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None						)					->							List[np.ndarray]:
      _a			      =							to_numpy(__magic_name__						)
      _a      ,   _a      ,   _a			      =							audio_values.shape

      if padding_mask is None:
           return list(__magic_name__						)

      _a			      =							to_numpy(__magic_name__						)

      # 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)
      _a			      =							seq_len - padding_mask.shape[-1]
      _a			      =							1 - self.feature_extractor.padding_value
      _a			      =							np.pad(__magic_name__      ,				((0, 0), (0, difference))      ,				'constant'      ,				constant_values=__magic_name__						)

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

      return audio_values





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a_					:    str          =    {
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[str]          =    [
     "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecAudioForAudioFrameClassification",
     "Data2VecAudioForCTC",
     "Data2VecAudioForSequenceClassification",
     "Data2VecAudioForXVector",
     "Data2VecAudioModel",
     "Data2VecAudioPreTrainedModel",
 ]
 a_					:    str          =    [
     "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecTextForCausalLM",
     "Data2VecTextForMaskedLM",
     "Data2VecTextForMultipleChoice",
     "Data2VecTextForQuestionAnswering",
     "Data2VecTextForSequenceClassification",
     "Data2VecTextForTokenClassification",
     "Data2VecTextModel",
     "Data2VecTextPreTrainedModel",
 ]
 a_					:    Tuple          =    [
     "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecVisionForImageClassification",
     "Data2VecVisionForMaskedImageModeling",
     "Data2VecVisionForSemanticSegmentation",
     "Data2VecVisionModel",
     "Data2VecVisionPreTrainedModel",
 ]

if is_tf_available():
 a_					:    Dict          =    [
     "TFData2VecVisionForImageClassification",
     "TFData2VecVisionForSemanticSegmentation",
     "TFData2VecVisionModel",
     "TFData2VecVisionPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
 from .configuration_dataavec_text import (
     DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecTextConfig,
     DataaVecTextOnnxConfig,
 )
 from .configuration_dataavec_vision import (
     DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecVisionConfig,
     DataaVecVisionOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_dataavec_audio import (
      DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecAudioForAudioFrameClassification,
      DataaVecAudioForCTC,
      DataaVecAudioForSequenceClassification,
      DataaVecAudioForXVector,
      DataaVecAudioModel,
      DataaVecAudioPreTrainedModel,
  )
  from .modeling_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecTextForCausalLM,
      DataaVecTextForMaskedLM,
      DataaVecTextForMultipleChoice,
      DataaVecTextForQuestionAnswering,
      DataaVecTextForSequenceClassification,
      DataaVecTextForTokenClassification,
      DataaVecTextModel,
      DataaVecTextPreTrainedModel,
  )
  from .modeling_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecVisionForImageClassification,
      DataaVecVisionForMaskedImageModeling,
      DataaVecVisionForSemanticSegmentation,
      DataaVecVisionModel,
      DataaVecVisionPreTrainedModel,
  )
 if is_tf_available():
  from .modeling_tf_dataavec_vision import (
      TFDataaVecVisionForImageClassification,
      TFDataaVecVisionForSemanticSegmentation,
      TFDataaVecVisionModel,
      TFDataaVecVisionPreTrainedModel,
  )

else:
 import sys

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

