


'''simple docstring'''




from ...processing_utils import ProcessorMixin




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  str          =	"SpeechT5FeatureExtractor"
   _SCREAMING_SNAKE_CASE  :  int          =	"SpeechT5Tokenizer"
   def __init__(						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]      ) ->							Dict:
        super().__init__(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
   def __call__(						self       :      List[str]       ,   *SCREAMING_SNAKE_CASE_       :      Dict       ,   **SCREAMING_SNAKE_CASE_       :      str      ) ->							Any:
        __snake_case           =						kwargs.pop('audio'       ,   SCREAMING_SNAKE_CASE_      )
        __snake_case           =						kwargs.pop('text'       ,   SCREAMING_SNAKE_CASE_      )
        __snake_case           =						kwargs.pop('text_target'       ,   SCREAMING_SNAKE_CASE_      )
        __snake_case           =						kwargs.pop('audio_target'       ,   SCREAMING_SNAKE_CASE_      )
        __snake_case           =						kwargs.pop('sampling_rate'       ,   SCREAMING_SNAKE_CASE_      )

        if audio is not None and text is not None:
             raise ValueError(
                 'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'      )
        if audio_target is not None and text_target is not None:
             raise ValueError(
                 'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'      )
        if audio is None and audio_target is None and text is None and text_target is None:
             raise ValueError(
                 'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'      )

        if audio is not None:
             __snake_case           =						self.feature_extractor(SCREAMING_SNAKE_CASE_       ,   *SCREAMING_SNAKE_CASE_       ,   sampling_rate=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
        elif text is not None:
             __snake_case           =						self.tokenizer(SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
        else:
             __snake_case           =						None

        if audio_target is not None:
             __snake_case           =						self.feature_extractor(audio_target=SCREAMING_SNAKE_CASE_       ,   *SCREAMING_SNAKE_CASE_       ,   sampling_rate=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
             __snake_case           =						targets['input_values']
        elif text_target is not None:
             __snake_case           =						self.tokenizer(SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
             __snake_case           =						targets['input_ids']
        else:
             __snake_case           =						None

        if inputs is None:
             return targets

        if targets is not None:
             __snake_case           =						labels

             __snake_case           =						targets.get('attention_mask'      )
             if decoder_attention_mask is not None:
                  __snake_case           =						decoder_attention_mask

        return inputs
   def a  (						self       :      List[Any]       ,   *SCREAMING_SNAKE_CASE_       :      Tuple       ,   **SCREAMING_SNAKE_CASE_       :      Dict      ) ->							Any:
        __snake_case           =						kwargs.pop('input_values'       ,   SCREAMING_SNAKE_CASE_      )
        __snake_case           =						kwargs.pop('input_ids'       ,   SCREAMING_SNAKE_CASE_      )
        __snake_case           =						kwargs.pop('labels'       ,   SCREAMING_SNAKE_CASE_      )

        if input_values is not None and input_ids is not None:
             raise ValueError('Cannot process both `input_values` and `input_ids` inputs.'      )
        if input_values is None and input_ids is None and labels is None:
             raise ValueError(
                 'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'      )

        if input_values is not None:
             __snake_case           =						self.feature_extractor.pad(SCREAMING_SNAKE_CASE_       ,   *SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
        elif input_ids is not None:
             __snake_case           =						self.tokenizer.pad(SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
        else:
             __snake_case           =						None

        if labels is not None:
             if "input_ids" in labels or (isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ) and "input_ids" in labels[0]):
                  __snake_case           =						self.tokenizer.pad(SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
                  __snake_case           =						targets['input_ids']
             else:
                  __snake_case           =						self.feature_extractor.feature_size
                  __snake_case           =						self.feature_extractor.num_mel_bins
                  __snake_case           =						self.feature_extractor.pad(SCREAMING_SNAKE_CASE_       ,   *SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
                  __snake_case           =						feature_size_hack
                  __snake_case           =						targets['input_values']
        else:
             __snake_case           =						None

        if inputs is None:
             return targets

        if targets is not None:
             __snake_case           =						labels

             __snake_case           =						targets.get('attention_mask'      )
             if decoder_attention_mask is not None:
                  __snake_case           =						decoder_attention_mask

        return inputs
   def a  (						self       :      Optional[int]       ,   *SCREAMING_SNAKE_CASE_       :      str       ,   **SCREAMING_SNAKE_CASE_       :      Optional[int]      ) ->							Tuple:
        return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )





   def a  (						self       :      List[Any]       ,   *SCREAMING_SNAKE_CASE_       :      str       ,   **SCREAMING_SNAKE_CASE_       :      Any      ) ->							Union[str, Any]:
        return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )








"""simple docstring"""


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case    					=					{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case    					=					[
       'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'InformerForPrediction',
       'InformerModel',
       'InformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_informer import (
          INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          InformerForPrediction,
          InformerModel,
          InformerPreTrainedModel,
      )

else:
   import sys

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