"""simple docstring"""







from typing import List, Optional

import numpy as np

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







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  'EncodecFeatureExtractor'
   lowercase							=  ('T5Tokenizer', 'T5TokenizerFast')
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      int:


        '''simple docstring'''


        super().__init__(__UpperCamelCase     ,__UpperCamelCase			)
        lowercase_	:				Any													=	self.feature_extractor
        lowercase_	:				str													=	False
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=True			)	->      int:


        '''simple docstring'''


        return self.tokenizer.get_decoder_prompt_ids(task=__UpperCamelCase     ,language=__UpperCamelCase     ,no_timestamps=__UpperCamelCase			)
   def __call__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


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

        lowercase_	:				Tuple													=	kwargs.pop('audio'     ,__UpperCamelCase			)
        lowercase_	:				Tuple													=	kwargs.pop('sampling_rate'     ,__UpperCamelCase			)
        lowercase_	:				Any													=	kwargs.pop('text'     ,__UpperCamelCase			)
        if len(__UpperCamelCase			) > 0:
             lowercase_	:				str													=	args[0]
             lowercase_	:				Optional[int]													=	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:
             lowercase_	:				Dict													=	self.tokenizer(__UpperCamelCase     ,**__UpperCamelCase			)

        if audio is not None:
             lowercase_	:				List[Any]													=	self.feature_extractor(__UpperCamelCase     ,*__UpperCamelCase     ,sampling_rate=__UpperCamelCase     ,**__UpperCamelCase			)

        if audio is None:
             return inputs

        elif text is None:
             return audio_inputs

        else:
             lowercase_	:				Optional[int]													=	audio_inputs['input_values']
             if "padding_mask" in audio_inputs:
                  lowercase_	:				int													=	audio_inputs['padding_mask']
             return inputs
   def 			_UpperCAmelCase   (			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	kwargs.pop('audio'     ,__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	kwargs.pop('padding_mask'     ,__UpperCamelCase			)

        if len(__UpperCamelCase			) > 0:
             lowercase_	:				List[Any]													=	args[0]
             lowercase_	:				Optional[int]													=	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			)	->      Tuple:


        '''simple docstring'''


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







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      List[np.ndarray]:


        '''simple docstring'''


        lowercase_	:				Dict													=	to_numpy(__UpperCamelCase			)
        lowercase_							,  lowercase_							,  lowercase_	:				int													=	audio_values.shape

        if padding_mask is None:
             return list(__UpperCamelCase			)

        lowercase_	:				Any													=	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)
        lowercase_	:				List[Any]													=	seq_len - padding_mask.shape[-1]
        lowercase_	:				Any													=	1 - self.feature_extractor.padding_value
        lowercase_	:				Optional[Any]													=	np.pad(__UpperCamelCase     ,((0, 0), (0, difference))     ,'constant'     ,constant_values=__UpperCamelCase			)

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

        return audio_values


"""simple docstring"""







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

__SCREAMING_SNAKE_CASE    	="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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None      ):
     require_version(deps[pkg]     ,			__SCREAMING_SNAKE_CASE      )

