





from typing import List, Optional

import numpy as np

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


class       __lowerCAmelCase					(       __a      ):


	'''simple docstring'''





	_A													=				"EncodecFeatureExtractor"
	_A													=				("T5Tokenizer", "T5TokenizerFast")

	def __init__(						self:						Optional[Any],					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						int			):
			super().__init__(lowercase_,					lowercase_			)
			lowercase__					:      List[str]      =		self.feature_extractor
			lowercase__					:      Union[str, Any]      =		False

	def    snake_case__(						self:						Optional[Any],					lowerCamelCase_:						Any=None,					lowerCamelCase_:						Any=None,					lowerCamelCase_:						Optional[Any]=True			):
			return self.tokenizer.get_decoder_prompt_ids(task=lowercase_,					language=lowercase_,					no_timestamps=lowercase_			)

	def __call__(						self:						int,					*lowerCamelCase_:						str,					**lowerCamelCase_:						List[str]			):


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

			lowercase__					:      List[Any]      =		kwargs.pop('audio',					lowercase_			)
			lowercase__					:      Dict      =		kwargs.pop('sampling_rate',					lowercase_			)
			lowercase__					:      Optional[int]      =		kwargs.pop('text',					lowercase_			)
			if len(lowercase_			) > 0:
					lowercase__					:      Optional[int]      =		args[0]
					lowercase__					:      Any      =		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__					:      List[str]      =		self.tokenizer(lowercase_,					**lowercase_			)

			if audio is not None:
					lowercase__					:      Optional[int]      =		self.feature_extractor(lowercase_,					*lowercase_,					sampling_rate=lowercase_,					**lowercase_			)

			if audio is None:
					return inputs

			elif text is None:
					return audio_inputs

			else:
					lowercase__					:      Any      =		audio_inputs['input_values']
					if "padding_mask" in audio_inputs:
							lowercase__					:      Dict      =		audio_inputs['padding_mask']
					return inputs

	def    snake_case__(						self:						Union[str, Any],					*lowerCamelCase_:						Union[str, Any],					**lowerCamelCase_:						List[Any]			):
			lowercase__					:      str      =		kwargs.pop('audio',					lowercase_			)
			lowercase__					:      str      =		kwargs.pop('padding_mask',					lowercase_			)

			if len(lowercase_			) > 0:
					lowercase__					:      Any      =		args[0]
					lowercase__					:      List[Any]      =		args[1:]

			if audio_values is not None:
					return self._decode_audio(lowercase_,					padding_mask=lowercase_			)
			else:
					return self.tokenizer.batch_decode(*lowercase_,					**lowercase_			)

	def    snake_case__(						self:						Optional[Any],					*lowerCamelCase_:						Optional[Any],					**lowerCamelCase_:						List[Any]			):
			return self.tokenizer.decode(*lowercase_,					**lowercase_			)





	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						Optional = None			):
			lowercase__					:      List[str]      =		to_numpy(lowercase_			)
			lowercase__   ,				lowercase__   ,				lowercase__					:      Dict      =		audio_values.shape

			if padding_mask is None:
					return list(lowercase_			)

			lowercase__					:      Union[str, Any]      =		to_numpy(lowercase_			)

			# 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__					:      Optional[Any]      =		seq_len - padding_mask.shape[-1]
			lowercase__					:      Union[str, Any]      =		1 - self.feature_extractor.padding_value
			lowercase__					:      Optional[int]      =		np.pad(lowercase_,					((0, 0), (0, difference)),					'constant',					constant_values=lowercase_			)

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

			return audio_values



'''simple docstring'''


import math
import sys

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
  if number != int(SCREAMING_SNAKE_CASE_						):
    raise ValueError("""the value of input must be a natural number"""						)
  if number < 0:
    raise ValueError("""the value of input must not be a negative number"""						)
  if number == 0:
    return 1
  lowercase_     =       [-1] * (number + 1)
  lowercase_     =       0
  for i in range(1 ,	number + 1						):
    lowercase_     =       sys.maxsize
    lowercase_     =       int(math.sqrt(SCREAMING_SNAKE_CASE_						)						)
    for j in range(1 ,	root + 1						):
      lowercase_     =       1 + answers[i - (j**2)]
      lowercase_     =       min(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    lowercase_     =       answer
  return answers[number]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

