





'''simple docstring'''







from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter





def 				lowerCamelCase_					(     SCREAMING_SNAKE_CASE__					:	str,	SCREAMING_SNAKE_CASE__					:	str,	SCREAMING_SNAKE_CASE__					:	Dict = 1 / sqrt(2      )      )       ->		IIRFilter:
						UpperCAmelCase_	:					Optional[Any]     =     tau * frequency / samplerate
						UpperCAmelCase_	:					Tuple     =     sin(a__      )
						UpperCAmelCase_	:					Union[str, Any]     =     cos(a__      )
						UpperCAmelCase_	:					int     =     _sin / (2 * q_factor)

						UpperCAmelCase_	:					str     =     (1 - _cos) / 2
						UpperCAmelCase_	:					List[str]     =     1 - _cos

						UpperCAmelCase_	:					Tuple     =     1 + alpha
						UpperCAmelCase_	:					int     =     -2 * _cos
						UpperCAmelCase_	:					str     =     1 - alpha

						UpperCAmelCase_	:					Union[str, Any]     =     IIRFilter(2      )
						filt.set_coefficients([aa, aa, aa],	[ba, ba, ba]      )
						return filt





def 				lowerCamelCase_					(     SCREAMING_SNAKE_CASE__					:	Tuple,	SCREAMING_SNAKE_CASE__					:	Optional[Any],	SCREAMING_SNAKE_CASE__					:	Union[str, Any] = 1 / sqrt(2      )      )       ->		IIRFilter:
						UpperCAmelCase_	:					Any     =     tau * frequency / samplerate
						UpperCAmelCase_	:					Tuple     =     sin(a__      )
						UpperCAmelCase_	:					int     =     cos(a__      )
						UpperCAmelCase_	:					Any     =     _sin / (2 * q_factor)

						UpperCAmelCase_	:					Optional[Any]     =     (1 + _cos) / 2
						UpperCAmelCase_	:					str     =     -1 - _cos

						UpperCAmelCase_	:					Tuple     =     1 + alpha
						UpperCAmelCase_	:					Optional[int]     =     -2 * _cos
						UpperCAmelCase_	:					Any     =     1 - alpha

						UpperCAmelCase_	:					Dict     =     IIRFilter(2      )
						filt.set_coefficients([aa, aa, aa],	[ba, ba, ba]      )
						return filt





def 				lowerCamelCase_					(     SCREAMING_SNAKE_CASE__					:	int,	SCREAMING_SNAKE_CASE__					:	str,	SCREAMING_SNAKE_CASE__					:	Dict = 1 / sqrt(2      )      )       ->		IIRFilter:
						UpperCAmelCase_	:					str     =     tau * frequency / samplerate
						UpperCAmelCase_	:					Tuple     =     sin(a__      )
						UpperCAmelCase_	:					Tuple     =     cos(a__      )
						UpperCAmelCase_	:					Optional[Any]     =     _sin / (2 * q_factor)

						UpperCAmelCase_	:					List[Any]     =     _sin / 2
						UpperCAmelCase_	:					Optional[int]     =     0
						UpperCAmelCase_	:					Any     =     -ba

						UpperCAmelCase_	:					Optional[Any]     =     1 + alpha
						UpperCAmelCase_	:					List[str]     =     -2 * _cos
						UpperCAmelCase_	:					int     =     1 - alpha

						UpperCAmelCase_	:					str     =     IIRFilter(2      )
						filt.set_coefficients([aa, aa, aa],	[ba, ba, ba]      )
						return filt





def 				lowerCamelCase_					(     SCREAMING_SNAKE_CASE__					:	Tuple,	SCREAMING_SNAKE_CASE__					:	str,	SCREAMING_SNAKE_CASE__					:	Dict = 1 / sqrt(2      )      )       ->		IIRFilter:
						UpperCAmelCase_	:					Dict     =     tau * frequency / samplerate
						UpperCAmelCase_	:					int     =     sin(a__      )
						UpperCAmelCase_	:					int     =     cos(a__      )
						UpperCAmelCase_	:					Union[str, Any]     =     _sin / (2 * q_factor)

						UpperCAmelCase_	:					List[str]     =     1 - alpha
						UpperCAmelCase_	:					Dict     =     -2 * _cos
						UpperCAmelCase_	:					Any     =     1 + alpha

						UpperCAmelCase_	:					Tuple     =     IIRFilter(2      )
						filt.set_coefficients([ba, ba, ba],	[ba, ba, ba]      )
						return filt





def 				lowerCamelCase_					(     SCREAMING_SNAKE_CASE__					:	Dict,	SCREAMING_SNAKE_CASE__					:	Tuple,	SCREAMING_SNAKE_CASE__					:	Union[str, Any],	SCREAMING_SNAKE_CASE__					:	List[str] = 1 / sqrt(2      ),	)       ->		IIRFilter:
						UpperCAmelCase_	:					Optional[Any]     =     tau * frequency / samplerate
						UpperCAmelCase_	:					Tuple     =     sin(a__      )
						UpperCAmelCase_	:					List[Any]     =     cos(a__      )
						UpperCAmelCase_	:					List[Any]     =     _sin / (2 * q_factor)
						UpperCAmelCase_	:					int     =     10 ** (gain_db / 40)

						UpperCAmelCase_	:					List[str]     =     1 + alpha * big_a
						UpperCAmelCase_	:					str     =     -2 * _cos
						UpperCAmelCase_	:					Optional[int]     =     1 - alpha * big_a
						UpperCAmelCase_	:					Optional[int]     =     1 + alpha / big_a
						UpperCAmelCase_	:					Optional[Any]     =     -2 * _cos
						UpperCAmelCase_	:					List[Any]     =     1 - alpha / big_a

						UpperCAmelCase_	:					Any     =     IIRFilter(2      )
						filt.set_coefficients([aa, aa, aa],	[ba, ba, ba]      )
						return filt





def 				lowerCamelCase_					(     SCREAMING_SNAKE_CASE__					:	Optional[Any],	SCREAMING_SNAKE_CASE__					:	str,	SCREAMING_SNAKE_CASE__					:	int,	SCREAMING_SNAKE_CASE__					:	int = 1 / sqrt(2      ),	)       ->		IIRFilter:
						UpperCAmelCase_	:					List[Any]     =     tau * frequency / samplerate
						UpperCAmelCase_	:					int     =     sin(a__      )
						UpperCAmelCase_	:					str     =     cos(a__      )
						UpperCAmelCase_	:					Dict     =     _sin / (2 * q_factor)
						UpperCAmelCase_	:					int     =     10 ** (gain_db / 40)
						UpperCAmelCase_	:					str     =     (big_a + 1) - (big_a - 1) * _cos
						UpperCAmelCase_	:					Any     =     (big_a + 1) + (big_a - 1) * _cos
						UpperCAmelCase_	:					int     =     (big_a - 1) - (big_a + 1) * _cos
						UpperCAmelCase_	:					Tuple     =     (big_a - 1) + (big_a + 1) * _cos
						UpperCAmelCase_	:					Union[str, Any]     =     2 * sqrt(a__      ) * alpha

						UpperCAmelCase_	:					Tuple     =     big_a * (pmc + aaa)
						UpperCAmelCase_	:					List[Any]     =     2 * big_a * mpc
						UpperCAmelCase_	:					Any     =     big_a * (pmc - aaa)
						UpperCAmelCase_	:					Any     =     ppmc + aaa
						UpperCAmelCase_	:					Optional[int]     =     -2 * pmpc
						UpperCAmelCase_	:					Optional[int]     =     ppmc - aaa

						UpperCAmelCase_	:					List[str]     =     IIRFilter(2      )
						filt.set_coefficients([aa, aa, aa],	[ba, ba, ba]      )
						return filt





def 				lowerCamelCase_					(     SCREAMING_SNAKE_CASE__					:	Any,	SCREAMING_SNAKE_CASE__					:	List[str],	SCREAMING_SNAKE_CASE__					:	List[str],	SCREAMING_SNAKE_CASE__					:	Dict = 1 / sqrt(2      ),	)       ->		IIRFilter:
						UpperCAmelCase_	:					int     =     tau * frequency / samplerate
						UpperCAmelCase_	:					Dict     =     sin(a__      )
						UpperCAmelCase_	:					List[Any]     =     cos(a__      )
						UpperCAmelCase_	:					Tuple     =     _sin / (2 * q_factor)
						UpperCAmelCase_	:					str     =     10 ** (gain_db / 40)
						UpperCAmelCase_	:					Union[str, Any]     =     (big_a + 1) - (big_a - 1) * _cos
						UpperCAmelCase_	:					Tuple     =     (big_a + 1) + (big_a - 1) * _cos
						UpperCAmelCase_	:					Tuple     =     (big_a - 1) - (big_a + 1) * _cos
						UpperCAmelCase_	:					Union[str, Any]     =     (big_a - 1) + (big_a + 1) * _cos
						UpperCAmelCase_	:					List[Any]     =     2 * sqrt(a__      ) * alpha

						UpperCAmelCase_	:					Optional[int]     =     big_a * (ppmc + aaa)
						UpperCAmelCase_	:					Optional[int]     =     -2 * big_a * pmpc
						UpperCAmelCase_	:					str     =     big_a * (ppmc - aaa)
						UpperCAmelCase_	:					Optional[int]     =     pmc + aaa
						UpperCAmelCase_	:					str     =     2 * mpc
						UpperCAmelCase_	:					List[str]     =     pmc - aaa

						UpperCAmelCase_	:					Union[str, Any]     =     IIRFilter(2      )
						filt.set_coefficients([aa, aa, aa],	[ba, ba, ba]      )
						return filt







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def     __lowerCAmelCase			(			a__						,    a__						,    a__=1024						,    a__=1024						,    a__=False						,    **a__  )       ->  Optional[Any]:
				__a										=    AutoTokenizer.from_pretrained(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''train'''						,    **a__  )
				__a										=    tok.pad_token_id

				def get_lens(a__  ):
								__a										=    tqdm(
								    DataLoader(a__						,    batch_size=512						,    num_workers=8						,    shuffle=a__						,    collate_fn=ds.collate_fn  )						,    desc=str(ds.len_file  )						,    )
								__a										=    []
								for batch in dl:
												__a										=    batch['''input_ids'''].ne(a__  ).sum(1  ).tolist()
												__a										=    batch['''labels'''].ne(a__  ).sum(1  ).tolist()
												if consider_target:
																for src, tgt in zip(a__						,    a__  ):
																				max_lens.append(max(a__						,    a__  )  )
												else:
																max_lens.extend(a__  )
								return max_lens

				__a										=    get_lens(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''val'''						,    **a__  )
				__a										=    get_lens(a__  )
				pickle_save(a__						,    train_ds.len_file  )
				pickle_save(a__						,    val_ds.len_file  )


if __name__ == "__main__":
					fire.Fire(save_len_file)