
"""simple docstring"""

from typing import List, Optional

import numpy as np

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

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"EncodecFeatureExtractor"
     lowercase		     =		("T5Tokenizer", "T5TokenizerFast")





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      super().__init__(__UpperCAmelCase			,  __UpperCAmelCase   )
      __UpperCamelCase       						= self.feature_extractor
      __UpperCamelCase       						= False





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=True   ):

      '''simple docstring'''




      return self.tokenizer.get_decoder_prompt_ids(task=__UpperCAmelCase			,  language=__UpperCAmelCase			,  no_timestamps=__UpperCAmelCase   )





     def __call__(			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      if self._in_target_context_manager:
       return self.current_processor(*__UpperCAmelCase			,  **__UpperCAmelCase   )

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

      if audio is not None:
       __UpperCamelCase       						= self.feature_extractor(__UpperCAmelCase			,  *__UpperCAmelCase			,  sampling_rate=__UpperCAmelCase			,  **__UpperCAmelCase   )

      if audio is None:
       return inputs

      elif text is None:
       return audio_inputs

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





     def 		UpperCAmelCase      (			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= kwargs.pop('audio'			,  __UpperCAmelCase   )
      __UpperCamelCase       						= kwargs.pop('padding_mask'			,  __UpperCAmelCase   )

      if len(__UpperCAmelCase   ) > 0:
       __UpperCamelCase       						= args[0]
       __UpperCamelCase       						= args[1:]

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





     def 		UpperCAmelCase      (			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




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







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None   ):

      '''simple docstring'''




      __UpperCamelCase       						= to_numpy(__UpperCAmelCase   )
      __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= audio_values.shape

      if padding_mask is None:
       return list(__UpperCAmelCase   )

      __UpperCamelCase       						= to_numpy(__UpperCAmelCase   )

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

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

      return audio_values





"""simple docstring"""

from typing import TYPE_CHECKING

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


UpperCamelCase			:       List[str]									=				{
    "configuration_autoformer": [
        "AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AutoformerConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Dict									=				[
          "AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "AutoformerForPrediction",
          "AutoformerModel",
          "AutoformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_autoformer import (
          AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AutoformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_autoformer import (
                AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                AutoformerForPrediction,
                AutoformerModel,
                AutoformerPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase			:       int									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



