






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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def  a__ (__lowercase							:Dict					,  __lowercase							:Any					,  __lowercase							:Optional[int]=1024					,  __lowercase							:Any=1024					,  __lowercase							:Tuple=False					,  **__lowercase							:List[Any]   )		-> Tuple:
				_A	:				Any   =      AutoTokenizer.from_pretrained(__lowercase   )
				_A	:				Optional[Any]   =      SeqaSeqDataset(__lowercase					,  __lowercase					,  __lowercase					,  __lowercase					,  type_path='''train'''					,  **__lowercase   )
				_A	:				Any   =      tok.pad_token_id

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

				_A	:				str   =      get_lens(__lowercase   )
				_A	:				Any   =      SeqaSeqDataset(__lowercase					,  __lowercase					,  __lowercase					,  __lowercase					,  type_path='''val'''					,  **__lowercase   )
				_A	:				Optional[Any]   =      get_lens(__lowercase   )
				pickle_save(__lowercase					,  train_ds.len_file   )
				pickle_save(__lowercase					,  val_ds.len_file   )


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











from PIL import Image



def  a__ (__lowercase							:Image					,  __lowercase							:float   )		-> Image:

				def brightness(__lowercase							:int   ) -> float:
								return 128 + level + (c - 128)

				if not -255.0 <= level <= 255.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''   )
				return img.point(__lowercase   )


if __name__ == "__main__":
	# Load image
	with Image.open('image_data/lena.jpg') as img:
		# Change brightness to 100
		_UpperCamelCase			:	Any								=change_brightness(img, 100)
		brigt_img.save('image_data/lena_brightness.png', format='png')



