


from typing import List, Optional

import numpy as np

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






class    UpperCAmelCase__	( _UpperCAmelCase		):



				"""simple docstring"""






				UpperCAmelCase__						:		Union[str, Any]              =  'EncodecFeatureExtractor'
				UpperCAmelCase__						:		List[Any]              =  ('T5Tokenizer', 'T5TokenizerFast')






				def __init__(      self ,       A_ ,       A_   )  ->	str:
									super().__init__(SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_   )
									__UpperCamelCase   							=self.feature_extractor
									__UpperCamelCase   							=False






				def _a (      self ,       A_=None ,       A_=None ,       A_=True   )  ->	Optional[Any]:
									return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE_ ,       language=SCREAMING_SNAKE_CASE_ ,       no_timestamps=SCREAMING_SNAKE_CASE_   )






				def __call__(      self ,       *A_ ,       **A_   )  ->	List[Any]:
									# For backward compatibility
									if self._in_target_context_manager:
														return self.current_processor(*SCREAMING_SNAKE_CASE_ ,       **SCREAMING_SNAKE_CASE_   )

									__UpperCamelCase   							=kwargs.pop('audio' ,       SCREAMING_SNAKE_CASE_   )
									__UpperCamelCase   							=kwargs.pop('sampling_rate' ,       SCREAMING_SNAKE_CASE_   )
									__UpperCamelCase   							=kwargs.pop('text' ,       SCREAMING_SNAKE_CASE_   )
									if len(SCREAMING_SNAKE_CASE_   ) > 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(SCREAMING_SNAKE_CASE_ ,       **SCREAMING_SNAKE_CASE_   )

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

									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 _a (      self ,       *A_ ,       **A_   )  ->	Optional[Any]:
									__UpperCamelCase   							=kwargs.pop('audio' ,       SCREAMING_SNAKE_CASE_   )
									__UpperCamelCase   							=kwargs.pop('padding_mask' ,       SCREAMING_SNAKE_CASE_   )

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

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






				def _a (      self ,       *A_ ,       **A_   )  ->	Optional[Any]:
									return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ ,       **SCREAMING_SNAKE_CASE_   )






				def _a (      self ,       A_ ,       A_ = None   )  ->	Union[str, Any]:
									__UpperCamelCase   							=to_numpy(SCREAMING_SNAKE_CASE_   )
									__UpperCamelCase   							=audio_values.shape

									if padding_mask is None:
														return list(SCREAMING_SNAKE_CASE_   )

									__UpperCamelCase   							=to_numpy(SCREAMING_SNAKE_CASE_   )

									# 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(SCREAMING_SNAKE_CASE_ ,       ((0, 0), (0, difference)) ,       'constant' ,       constant_values=SCREAMING_SNAKE_CASE_   )

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

									return audio_values









import sys

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'.split()
if sys.version_info < (3, 7):
   pkgs_to_check_at_runtime.append('dataclasses')
if sys.version_info < (3, 8):
   pkgs_to_check_at_runtime.append('importlib_metadata')

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

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : Optional[int]=None    ):
     require_version(deps[pkg]      ,					SCREAMING_SNAKE_CASE__    )





