
"""simple docstring"""







from typing import List, Optional

import numpy as np

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




class  A__       (	_lowerCamelCase):
		A_						:       str				   =	'EncodecFeatureExtractor'
		A_						:       Any				   =	('T5Tokenizer', 'T5TokenizerFast')

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			super().__init__(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						List[str]									=      self.feature_extractor
			__lowerCAmelCase			:						List[str]									=      False

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=True ):
			return self.tokenizer.get_decoder_prompt_ids(task=_SCREAMING_SNAKE_CASE       ,		language=_SCREAMING_SNAKE_CASE       ,		no_timestamps=_SCREAMING_SNAKE_CASE )

		def __call__(      self       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			# For backward compatibility
			if self._in_target_context_manager:
				return self.current_processor(*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Optional[Any]									=      kwargs.pop('audio'       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Dict									=      kwargs.pop('sampling_rate'       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						List[Any]									=      kwargs.pop('text'       ,		_SCREAMING_SNAKE_CASE )
			if len(_SCREAMING_SNAKE_CASE ) > 0:
				__lowerCAmelCase			:						str									=      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			:						List[str]									=      self.tokenizer(_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

			if audio is not None:
				__lowerCAmelCase			:						List[str]									=      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:
				__lowerCAmelCase			:						List[str]									=      audio_inputs['input_values']
				if "padding_mask" in audio_inputs:
					__lowerCAmelCase			:						int									=      audio_inputs['padding_mask']
				return inputs

		def 							__lowerCamelCase							(      self       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Optional[int]									=      kwargs.pop('audio'       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Union[str, Any]									=      kwargs.pop('padding_mask'       ,		_SCREAMING_SNAKE_CASE )

			if len(_SCREAMING_SNAKE_CASE ) > 0:
				__lowerCAmelCase			:						Optional[Any]									=      args[0]
				__lowerCAmelCase			:						Optional[int]									=      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 							__lowerCamelCase							(      self       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )







		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None ):
			__lowerCAmelCase			:						int									=      to_numpy(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						int									=      audio_values.shape

			if padding_mask is None:
				return list(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Union[str, Any]									=      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)
			__lowerCAmelCase			:						str									=      seq_len - padding_mask.shape[-1]
			__lowerCAmelCase			:						Union[str, Any]									=      1 - self.feature_extractor.padding_value
			__lowerCAmelCase			:						Dict									=      np.pad(_SCREAMING_SNAKE_CASE       ,		((0, 0), (0, difference))       ,		'constant'       ,		constant_values=_SCREAMING_SNAKE_CASE )

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

			return audio_values






from itertools import permutations



def _a				(	lowerCamelCase:   tuple )	->						bool:







      '''simple docstring'''






      if num[3] % 2 != 0:
            return False

      if (num[2] + num[3] + num[4]) % 3 != 0:
            return False

      if num[5] % 5 != 0:
            return False

      __A          =				[7, 11, 13, 17]
      for i, test in enumerate(lowerCamelCase ):
            if (num[i + 4] * 1_00 + num[i + 5] * 10 + num[i + 6]) % test != 0:
                  return False
      return True



def _a				(	lowerCamelCase:   int = 10 )	->						int:







      '''simple docstring'''






      return sum(
          int(''''''.join(map(lowerCamelCase					,				lowerCamelCase ) ) )
          for num in permutations(range(lowerCamelCase ) )
          if is_substring_divisible(lowerCamelCase ) )


if __name__ == "__main__":
       print(f'{solution() = }')





