

"""simple docstring"""






from typing import List, Optional

import numpy as np

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






class      _UpperCAmelCase      (		__UpperCAmelCase):
			__a			:							Dict					       =				'''EncodecFeatureExtractor'''
			__a			:							Dict					       =				('''T5Tokenizer''', '''T5TokenizerFast''')
			def __init__(		self   ,		_A   ,		_A  )     ->     Tuple:






				'''simple docstring'''







				super().__init__(lowerCamelCase__   ,		lowerCamelCase__  )
				_UpperCAmelCase     :				Optional[Any]          =       self.feature_extractor
				_UpperCAmelCase     :				Optional[int]          =       False
			def 				__snake_case			(		self   ,		_A=None   ,		_A=None   ,		_A=True  )     ->     Any:






				'''simple docstring'''







				return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase__   ,		language=lowerCamelCase__   ,		no_timestamps=lowerCamelCase__  )
			def __call__(		self   ,		*_A   ,		**_A  )     ->     Tuple:






				'''simple docstring'''







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

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

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

				if audio is None:
					return inputs

				elif text is None:
					return audio_inputs

				else:
					_UpperCAmelCase     :				Any          =       audio_inputs["""input_values"""]
					if "padding_mask" in audio_inputs:
						_UpperCAmelCase     :				Optional[int]          =       audio_inputs["""padding_mask"""]
					return inputs
			def 				__snake_case			(		self   ,		*_A   ,		**_A  )     ->     Dict:






				'''simple docstring'''







				_UpperCAmelCase     :				str          =       kwargs.pop("""audio"""   ,		lowerCamelCase__  )
				_UpperCAmelCase     :				Any          =       kwargs.pop("""padding_mask"""   ,		lowerCamelCase__  )

				if len(lowerCamelCase__  ) > 0:
					_UpperCAmelCase     :				Any          =       args[0]
					_UpperCAmelCase     :				Any          =       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   ,		*_A   ,		**_A  )     ->     Union[str, Any]:






				'''simple docstring'''







				return self.tokenizer.decode(*lowerCamelCase__   ,		**lowerCamelCase__  )
			def 				__snake_case			(		self   ,		_A   ,		_A = None  )     ->     List[np.ndarray]:






				'''simple docstring'''







				_UpperCAmelCase     :				Any          =       to_numpy(lowerCamelCase__  )
				_UpperCAmelCase     :				List[str]          =       audio_values.shape

				if padding_mask is None:
					return list(lowerCamelCase__  )

				_UpperCAmelCase     :				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)
				_UpperCAmelCase     :				int          =       seq_len - padding_mask.shape[-1]
				_UpperCAmelCase     :				int          =       1 - self.feature_extractor.padding_value
				_UpperCAmelCase     :				Tuple          =       np.pad(lowerCamelCase__   ,		((0, 0), (0, difference))   ,		"""constant"""   ,		constant_values=lowerCamelCase__  )

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

				return audio_values









"""simple docstring"""


from decimal import Decimal, getcontext
from math import ceil, factorial


def    _lowerCamelCase   (     lowerCamelCase__	:							int  ):

 if not isinstance(lowerCamelCase__       ,		lowerCamelCase__  ):
  raise TypeError("""Undefined for non-integers"""  )
 elif precision < 1:
  raise ValueError("""Undefined for non-natural numbers"""  )

 lowercase__       :       List[str]				=						precision
 lowercase__       :       Optional[int]				=						ceil(precision / 14  )
 lowercase__       :       Union[str, Any]				=						42_68_80 * Decimal(1_00_05  ).sqrt()
 lowercase__       :       Optional[Any]				=						1
 lowercase__       :       Optional[Any]				=						13_59_14_09
 lowercase__       :       List[Any]				=						Decimal(lowerCamelCase__  )
 for k in range(1       ,		lowerCamelCase__  ):
  lowercase__       :       Optional[int]				=						factorial(6 * k  ) // (factorial(3 * k  ) * factorial(lowerCamelCase__  ) ** 3)
  linear_term += 5_45_14_01_34
  exponential_term *= -26_25_37_41_26_40_76_80_00
  partial_sum += Decimal(multinomial_term * linear_term  ) / exponential_term
 return str(constant_term / partial_sum  )[:-1]


if __name__ == "__main__":
   __snake_case    					=					50
   print(F"The first {n} digits of pi is: {pi(n)}")