



from typing import List, Optional

import numpy as np

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







class 	_snake_case							(			lowercase_      ):






   '''simple docstring'''






   A__	:      Dict    =		"EncodecFeatureExtractor"
   A__	:      Any    =		("T5Tokenizer", "T5TokenizerFast")


   def __init__(  self:			Tuple   ,lowerCamelCase_:			Optional[Any]   ,lowerCamelCase_:			Tuple				)     ->	int:
       super().__init__(__UpperCamelCase   ,__UpperCamelCase				)
       UpperCAmelCase_			:    Any   						= self.feature_extractor
       UpperCAmelCase_			:    str   						= False


   def 	A__  (  self:			Dict   ,lowerCamelCase_:			Union[str, Any]=None   ,lowerCamelCase_:			str=None   ,lowerCamelCase_:			Optional[int]=True				)     ->	int:
       return self.tokenizer.get_decoder_prompt_ids(task=__UpperCamelCase   ,language=__UpperCamelCase   ,no_timestamps=__UpperCamelCase				)


   def __call__(  self:			Optional[int]   ,*lowerCamelCase_:			Tuple   ,**lowerCamelCase_:			Optional[int]				)     ->	Dict:


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

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

       if audio is not None:
           UpperCAmelCase_			:    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:
           UpperCAmelCase_			:    Optional[int]   						= audio_inputs['input_values']
           if "padding_mask" in audio_inputs:
               UpperCAmelCase_			:    int   						= audio_inputs['padding_mask']
           return inputs


   def 	A__  (  self:			Optional[int]   ,*lowerCamelCase_:			List[str]   ,**lowerCamelCase_:			int				)     ->	str:
       UpperCAmelCase_			:    Optional[int]   						= kwargs.pop("""audio"""   ,__UpperCamelCase				)
       UpperCAmelCase_			:    Union[str, Any]   						= kwargs.pop("""padding_mask"""   ,__UpperCamelCase				)

       if len(__UpperCamelCase				) > 0:
           UpperCAmelCase_			:    List[Any]   						= args[0]
           UpperCAmelCase_			:    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 	A__  (  self:			Optional[int]   ,*lowerCamelCase_:			List[str]   ,**lowerCamelCase_:			Optional[int]				)     ->	Tuple:
       return self.tokenizer.decode(*__UpperCamelCase   ,**__UpperCamelCase				)






   def 	A__  (  self:			Dict   ,lowerCamelCase_:			int   ,lowerCamelCase_:			List[Any] = None				)     ->	List[np.ndarray]:
       UpperCAmelCase_			:    Dict   						= to_numpy(__UpperCamelCase				)
       UpperCAmelCase_			:    int   						= audio_values.shape

       if padding_mask is None:
           return list(__UpperCamelCase				)

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

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

       return audio_values

"""simple docstring"""







__SCREAMING_SNAKE_CASE    	=[
    "DownloadConfig",
    "DownloadManager",
    "DownloadMode",
    "StreamingDownloadManager",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager

