



"""simple docstring"""






from typing import List, Optional

import numpy as np

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

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


     '''simple docstring'''





     _a             =							'EncodecFeatureExtractor'
     _a             =							('T5Tokenizer', 'T5TokenizerFast')



     def __init__(	self							:  Any,  lowerCamelCase							:  str,  lowerCamelCase							:  List[str]					)-> str:
        super().__init__(lowerCamelCase,  lowerCamelCase					)
        lowerCamelCase__		:		Union[str, Any]		     =self.feature_extractor
        lowerCamelCase__		:		Tuple		     =False



     def 		snake_case	(	self							:  Any,  lowerCamelCase							:  Dict=None,  lowerCamelCase							:  str=None,  lowerCamelCase							:  List[Any]=True					)-> str:
        return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase,  language=lowerCamelCase,  no_timestamps=lowerCamelCase					)



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

        lowerCamelCase__		:		List[str]		     =kwargs.pop('''audio''',  lowerCamelCase					)
        lowerCamelCase__		:		List[Any]		     =kwargs.pop('''sampling_rate''',  lowerCamelCase					)
        lowerCamelCase__		:		Any		     =kwargs.pop('''text''',  lowerCamelCase					)
        if len(lowerCamelCase					) > 0:
           lowerCamelCase__		:		Union[str, Any]		     =args[0]
           lowerCamelCase__		:		str		     =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:
           lowerCamelCase__		:		Tuple		     =self.tokenizer(lowerCamelCase,  **lowerCamelCase					)

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

        if audio is None:
           return inputs

        elif text is None:
           return audio_inputs

        else:
           lowerCamelCase__		:		Optional[int]		     =audio_inputs['''input_values''']
           if "padding_mask" in audio_inputs:
              lowerCamelCase__		:		Dict		     =audio_inputs['''padding_mask''']
           return inputs



     def 		snake_case	(	self							:  Optional[Any],  *lowerCamelCase							:  Any,  **lowerCamelCase							:  Union[str, Any]					)-> List[Any]:
        lowerCamelCase__		:		Dict		     =kwargs.pop('''audio''',  lowerCamelCase					)
        lowerCamelCase__		:		List[Any]		     =kwargs.pop('''padding_mask''',  lowerCamelCase					)

        if len(lowerCamelCase					) > 0:
           lowerCamelCase__		:		str		     =args[0]
           lowerCamelCase__		:		Dict		     =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 		snake_case	(	self							:  Dict,  *lowerCamelCase							:  List[Any],  **lowerCamelCase							:  Dict					)-> int:
        return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase					)




     def 		snake_case	(	self							:  Any,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Optional = None					)-> List[np.ndarray]:
        lowerCamelCase__		:		List[str]		     =to_numpy(lowerCamelCase					)
        lowerCamelCase__		:		List[str]		     =audio_values.shape

        if padding_mask is None:
           return list(lowerCamelCase					)

        lowerCamelCase__		:		Optional[Any]		     =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)
        lowerCamelCase__		:		Optional[Any]		     =seq_len - padding_mask.shape[-1]
        lowerCamelCase__		:		int		     =1 - self.feature_extractor.padding_value
        lowerCamelCase__		:		str		     =np.pad(lowerCamelCase,  ((0, 0), (0, difference)),  '''constant''',  constant_values=lowerCamelCase					)

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

        return audio_values









"""simple docstring"""






def 					snake_case__       (		__lowerCamelCase    :   int							,		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			return int((input_a, input_a).count(0     ) != 0     )






def 					snake_case__       (		):
			"""simple docstring"""
			assert nand_gate(0							,		0     ) == 1
			assert nand_gate(0							,		1     ) == 1
			assert nand_gate(1							,		0     ) == 1
			assert nand_gate(1							,		1     ) == 0


if __name__ == "__main__":
		print(nand_gate(0, 0))
		print(nand_gate(0, 1))
		print(nand_gate(1, 0))
		print(nand_gate(1, 1))




