





'''simple docstring'''

from typing import List, Optional

import numpy as np

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







class __a (   _snake_case			):
   __UpperCamelCase   : Optional[Any]	      =	'EncodecFeatureExtractor'
   __UpperCamelCase   : Dict	      =	('T5Tokenizer', 'T5TokenizerFast')





   def __init__(					self				:			Any				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			List[str]    ):

      '''simple docstring'''

      super().__init__(lowerCamelCase				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.feature_extractor
      __SCREAMING_SNAKE_CASE      							=       False





   def      UpperCAmelCase__     (					self				:			List[Any]				,lowerCamelCase				:			Dict=None				,lowerCamelCase				:			int=None				,lowerCamelCase				:			str=True    ):

      '''simple docstring'''

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





   def __call__(					self				:			Any				,*lowerCamelCase				:			Tuple				,**lowerCamelCase				:			List[str]    ):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE      							=       kwargs.pop("""audio"""				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       kwargs.pop("""sampling_rate"""				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       kwargs.pop("""text"""				,lowerCamelCase    )
      if len(lowerCamelCase    ) > 0:
         __SCREAMING_SNAKE_CASE      							=       args[0]
         __SCREAMING_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:
         __SCREAMING_SNAKE_CASE      							=       self.tokenizer(lowerCamelCase				,**lowerCamelCase    )

      if audio is not None:
         __SCREAMING_SNAKE_CASE      							=       self.feature_extractor(lowerCamelCase				,*lowerCamelCase				,sampling_rate=lowerCamelCase				,**lowerCamelCase    )

      if audio is None:
         return inputs

      elif text is None:
         return audio_inputs

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





   def      UpperCAmelCase__     (					self				:			Optional[int]				,*lowerCamelCase				:			str				,**lowerCamelCase				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       kwargs.pop("""audio"""				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       kwargs.pop("""padding_mask"""				,lowerCamelCase    )

      if len(lowerCamelCase    ) > 0:
         __SCREAMING_SNAKE_CASE      							=       args[0]
         __SCREAMING_SNAKE_CASE      							=       args[1:]

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





   def      UpperCAmelCase__     (					self				:			Any				,*lowerCamelCase				:			Any				,**lowerCamelCase				:			Dict    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Tuple				,lowerCamelCase				:			List[str]				,lowerCamelCase				:			Optional = None    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       to_numpy(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       audio_values.shape

      if padding_mask is None:
         return list(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       to_numpy(lowerCamelCase    )

      # 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)
      __SCREAMING_SNAKE_CASE      							=       seq_len - padding_mask.shape[-1]
      __SCREAMING_SNAKE_CASE      							=       1 - self.feature_extractor.padding_value
      __SCREAMING_SNAKE_CASE      							=       np.pad(lowerCamelCase				,((0, 0), (0, difference))				,"""constant"""				,constant_values=lowerCamelCase    )

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

      return audio_values












'''simple docstring'''

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

a        =						[
    "python",
    "tqdm",
    "regex",
    "requests",
    "packaging",
    "filelock",
    "numpy",
    "tokenizers",
    "huggingface-hub",
    "safetensors",
    "accelerate",
    "pyyaml",
]

for pkg in pkgs_to_check_at_runtime:
    if pkg in deps:
        if pkg == "tokenizers":
            # must be loaded here, or else tqdm check may fail
            from .utils import is_tokenizers_available

            if not is_tokenizers_available():
                continue  # not required, check version only if installed
        elif pkg == "accelerate":
            # must be loaded here, or else tqdm check may fail
            from .utils import is_accelerate_available

            # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
            # Transformers with PyTorch
            if not is_accelerate_available():
                continue  # not required, check version only if installed

        require_version_core(deps[pkg])
    else:
        raise ValueError(F'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=None      )						->					Optional[Any]:

   '''simple docstring'''







   require_version(deps[pkg]							,	__UpperCAmelCase      )





