
from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class 	A__   (						SCREAMING_SNAKE_CASE__				):
							lowerCAmelCase__		:						Union[str, Any]    			=							"EncodecFeatureExtractor"
							lowerCAmelCase__		:						Any    			=							("T5Tokenizer", "T5TokenizerFast")





							def __init__( self  :   Optional[Any]     , _UpperCAmelCase  :   Union[str, Any]     , _UpperCAmelCase  :   Dict				)						->		Optional[Any]:



											"""simple docstring"""


											super().__init__(lowerCamelCase__     , lowerCamelCase__				)
											__lowercase								=					self.feature_extractor
											__lowercase								=					False





							def 			a__ ( self  :   Tuple     , _UpperCAmelCase  :   Tuple=None     , _UpperCAmelCase  :   Dict=None     , _UpperCAmelCase  :   Optional[Any]=True				)						->		int:



											"""simple docstring"""


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





							def __call__( self  :   Union[str, Any]     , *_UpperCAmelCase  :   List[Any]     , **_UpperCAmelCase  :   Optional[Any]				)						->		str:



											"""simple docstring"""


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

											__lowercase								=					kwargs.pop('audio'     , lowerCamelCase__				)
											__lowercase								=					kwargs.pop('sampling_rate'     , lowerCamelCase__				)
											__lowercase								=					kwargs.pop('text'     , lowerCamelCase__				)
											if len(lowerCamelCase__				) > 0:
															__lowercase								=					args[0]
															__lowercase								=					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								=					self.tokenizer(lowerCamelCase__     , **lowerCamelCase__				)

											if audio is not None:
															__lowercase								=					self.feature_extractor(lowerCamelCase__     , *lowerCamelCase__     , sampling_rate=lowerCamelCase__     , **lowerCamelCase__				)

											if audio is None:
															return inputs

											elif text is None:
															return audio_inputs

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





							def 			a__ ( self  :   Dict     , *_UpperCAmelCase  :   Optional[int]     , **_UpperCAmelCase  :   Optional[int]				)						->		Tuple:



											"""simple docstring"""


											__lowercase								=					kwargs.pop('audio'     , lowerCamelCase__				)
											__lowercase								=					kwargs.pop('padding_mask'     , lowerCamelCase__				)

											if len(lowerCamelCase__				) > 0:
															__lowercase								=					args[0]
															__lowercase								=					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 			a__ ( self  :   int     , *_UpperCAmelCase  :   Optional[int]     , **_UpperCAmelCase  :   Any				)						->		Optional[Any]:



											"""simple docstring"""


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





							def 			a__ ( self  :   Union[str, Any]     , _UpperCAmelCase  :   Union[str, Any]     , _UpperCAmelCase  :   Dict = None				)						->		Any:



											"""simple docstring"""


											__lowercase								=					to_numpy(lowerCamelCase__				)
											__lowercase								=					audio_values.shape

											if padding_mask is None:
															return list(lowerCamelCase__				)

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

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

											return audio_values










from ..utils import DummyObject, requires_backends






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: List[str]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Any					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)


