






"""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						(  __snake_case							):





							"""simple docstring"""





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




							def __init__(   self:     List[Any]				,		__A:     Any				,		__A:     Dict    ):

												'''simple docstring'''

												super().__init__(__A				,		__A    )
												a__          =  self.feature_extractor
												a__          =  False




							def  lowercase		(   self:     Union[str, Any]				,		__A:     List[Any]=None				,		__A:     Optional[Any]=None				,		__A:     List[Any]=True    ):

												'''simple docstring'''

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




							def __call__(   self:     Union[str, Any]				,		*__A:     int				,		**__A:     Dict    ):

												'''simple docstring'''

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

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

												if audio is not None:
																	a__          =  self.feature_extractor(__A				,		*__A				,		sampling_rate=__A				,		**__A    )

												if audio is None:
																	return inputs

												elif text is None:
																	return audio_inputs

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




							def  lowercase		(   self:     Union[str, Any]				,		*__A:     List[str]				,		**__A:     Tuple    ):

												'''simple docstring'''

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''padding_mask'''				,		__A    )

												if len(__A    ) > 0:
																	a__          =  args[0]
																	a__          =  args[1:]

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




							def  lowercase		(   self:     Union[str, Any]				,		*__A:     Optional[int]				,		**__A:     Optional[Any]    ):

												'''simple docstring'''

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





							def  lowercase		(   self:     Union[str, Any]				,		__A:     Dict				,		__A:     Optional = None    ):

												'''simple docstring'''

												a__          =  to_numpy(__A    )
												a__       ,a__       ,a__          =  audio_values.shape

												if padding_mask is None:
																	return list(__A    )

												a__          =  to_numpy(__A    )

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

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

												return audio_values










"""simple docstring"""







__a       :	Union[str, Any]             = range(2, 20 + 1)
__a       :	Any             = [10**k for k in range(ks[-1] + 1)]
__a       :	dict[int, dict[int, list[list[int]]]]             = {}





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  sum(a_i[j] for j in range(lowerCamelCase_ ,     len(lowerCamelCase_)))
					a__          =  sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase_) ,     lowerCamelCase_)))

					a__       ,a__          =  0, 0
					a__          =  n - i

					a__          =  memo.get(lowerCamelCase_)

					if sub_memo is not None:
										a__          =  sub_memo.get(lowerCamelCase_)

										if jumps is not None and len(lowerCamelCase_) > 0:
															# find and make the largest jump without going over
															a__          =  -1
															for _k in range(len(lowerCamelCase_) - 1 ,     -1 ,     -1):
																				if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																									a__          =  _k
																									break

															if max_jump >= 0:
																				a__       ,a__       ,a__          =  jumps[max_jump]
																				# since the difference between jumps is cached, add c
																				a__          =  diff + c
																				for j in range(min(lowerCamelCase_ ,     len(lowerCamelCase_))):
																									a__       ,a__          =  divmod(lowerCamelCase_ ,     10)
																				if new_c > 0:
																									add(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)

										else:
															a__          =  []
					else:
										a__          =  {c: []}
										a__          =  sub_memo

					if dn >= max_dn or c + diff >= base[k]:
										return diff, dn

					if k > ks[0]:
										while True:
															# keep doing smaller jumps
															a__       ,a__          =  next_term(lowerCamelCase_ ,     k - 1 ,     i + dn ,     lowerCamelCase_)
															diff += _diff
															dn += terms_jumped

															if dn >= max_dn or c + diff >= base[k]:
																				break
					else:
										# would be too small a jump, just compute sequential terms instead
										a__       ,a__          =  compute(lowerCamelCase_ ,     lowerCamelCase_ ,     i + dn ,     lowerCamelCase_)
										diff += _diff
										dn += terms_jumped

					a__          =  sub_memo[c]

					# keep jumps sorted by # of terms skipped
					a__          =  0
					while j < len(lowerCamelCase_):
										if jumps[j][1] > dn:
															break
										j += 1

					# cache the jump for this value digitsum(b) and c
					sub_memo[c].insert(lowerCamelCase_ ,     (diff, dn, k))
					return (diff, dn)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					if i >= n:
										return 0, i
					if k > len(lowerCamelCase_):
										a_i.extend([0 for _ in range(k - len(lowerCamelCase_))])

					# note: a_i -> b * 10^k + c
					# ds_b -> digitsum(b)
					# ds_c -> digitsum(c)
					a__          =  i
					a__       ,a__       ,a__          =  0, 0, 0
					for j in range(len(lowerCamelCase_)):
										if j >= k:
															ds_b += a_i[j]
										else:
															ds_c += a_i[j]

					while i < n:
										i += 1
										a__          =  ds_c + ds_b
										diff += addend
										a__          =  0
										for j in range(lowerCamelCase_):
															a__          =  a_i[j] + addend
															a__       ,a__          =  divmod(lowerCamelCase_ ,     10)

															ds_c += a_i[j]

										if addend > 0:
															break

					if addend > 0:
										add(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					return diff, i - start_i





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					for j in range(lowerCamelCase_ ,     len(lowerCamelCase_)):
										a__          =  digits[j] + addend
										if s >= 10:
															a__       ,a__          =  divmod(lowerCamelCase_ ,     10)
															a__          =  addend // 10 + quotient
										else:
															a__          =  s
															a__          =  addend // 10

										if addend == 0:
															break

					while addend > 0:
										a__       ,a__          =  divmod(lowerCamelCase_ ,     10)
										digits.append(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ = 10**15):
					a__          =  [1]
					a__          =  1
					a__          =  0
					while True:
										a__       ,a__          =  next_term(lowerCamelCase_ ,     20 ,     i + dn ,     lowerCamelCase_)
										dn += terms_jumped
										if dn == n - i:
															break

					a__          =  0
					for j in range(len(lowerCamelCase_)):
										a_n += digits[j] * 10**j
					return a_n


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


