
from itertools import product


def     __lowerCAmelCase			(			a__						,    a__  )       ->  list[int]:
				__a										=    sides_number
				__a										=    max_face_number * dice_number
				__a										=    [0] * (max_total + 1)

				__a										=    1
				__a										=    range(a__						,    max_face_number + 1  )
				for dice_numbers in product(a__						,    repeat=a__  ):
								__a										=    sum(a__  )
								totals_frequencies[total] += 1

				return totals_frequencies


def     __lowerCAmelCase			(			)       ->  float:
				__a										=    total_frequency_distribution(
				    sides_number=4						,    dice_number=9  )
				__a										=    total_frequency_distribution(
				    sides_number=6						,    dice_number=6  )

				__a										=    0
				__a										=    9
				__a										=    4 * 9
				__a										=    6
				for peter_total in range(a__						,    max_peter_total + 1  ):
								peter_wins_count += peter_totals_frequencies[peter_total] * sum(
								    colin_totals_frequencies[min_colin_total:peter_total]  )

				__a										=    (4**9) * (6**6)
				__a										=    peter_wins_count / total_games_number

				__a										=    round(a__						,    ndigits=7  )

				return rounded_peter_win_probability


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

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)