


"""simple docstring"""







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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def  a__      (    SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]=1_0_2_4    ,				SCREAMING_SNAKE_CASE   :   List[str]=1_0_2_4    ,				SCREAMING_SNAKE_CASE   :   Dict=False    ,				**SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					Optional[int]								=							SeqaSeqDataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				type_path="train"    ,				**SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					List[Any]								=							tok.pad_token_id

  def get_lens(SCREAMING_SNAKE_CASE   :   Dict    ):
    lowerCAmelCase					:					Optional[Any]								=							tqdm(
        DataLoader(SCREAMING_SNAKE_CASE    ,				batch_size=5_1_2    ,				num_workers=8    ,				shuffle=SCREAMING_SNAKE_CASE    ,				collate_fn=ds.collate_fn    )    ,				desc=str(ds.len_file    )    ,				)
    lowerCAmelCase					:					Tuple								=							[]
    for batch in dl:
      lowerCAmelCase					:					Dict								=							batch["input_ids"].ne(SCREAMING_SNAKE_CASE    ).sum(1    ).tolist()
      lowerCAmelCase					:					Optional[int]								=							batch["labels"].ne(SCREAMING_SNAKE_CASE    ).sum(1    ).tolist()
      if consider_target:
        for src, tgt in zip(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ):
          max_lens.append(max(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )    )
      else:
        max_lens.extend(SCREAMING_SNAKE_CASE    )
    return max_lens

  lowerCAmelCase					:					Tuple								=							get_lens(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					Union[str, Any]								=							SeqaSeqDataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				type_path="val"    ,				**SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					List[str]								=							get_lens(SCREAMING_SNAKE_CASE    )
  pickle_save(SCREAMING_SNAKE_CASE    ,				train_ds.len_file    )
  pickle_save(SCREAMING_SNAKE_CASE    ,				val_ds.len_file    )


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









"""simple docstring"""







from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowerCAmelCase__				    =       1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

lowerCAmelCase__				    =       3e8  # unit of c : m * s^-1
def  a__      (    SCREAMING_SNAKE_CASE   :   float    ,				SCREAMING_SNAKE_CASE   :   float    ,				SCREAMING_SNAKE_CASE   :   float    ):


  '''simple docstring'''







  if (force, area, distance).count(0    ) != 1:
    raise ValueError("One and only one argument must be 0"    )
  if force < 0:
    raise ValueError("Magnitude of force can not be negative"    )
  if distance < 0:
    raise ValueError("Distance can not be negative"    )
  if area < 0:
    raise ValueError("Area can not be negative"    )
  if force == 0:
    lowerCAmelCase					:					Any								=							(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
        2_4_0 * (distance) ** 4
    )
    return {"force": force}
  elif area == 0:
    lowerCAmelCase					:					Any								=							(2_4_0 * force * (distance) ** 4) / (
        REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
    )
    return {"area": area}
  elif distance == 0:
    lowerCAmelCase					:					Dict								=							(
        (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * force)
    ) ** (1 / 4)
    return {"distance": distance}
  raise ValueError("One and only one argument must be 0"    )


# Run doctest
if __name__ == "__main__":
     import doctest

     doctest.testmod()





