


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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__=1_0_2_4						,							UpperCAmelCase__=1_0_2_4						,							UpperCAmelCase__=False						,							**UpperCAmelCase__  ):
					lowercase_  							=			AutoTokenizer.from_pretrained(UpperCAmelCase__  )
					lowercase_  							=			SeqaSeqDataset(UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							type_path="""train"""						,							**UpperCAmelCase__  )
					lowercase_  							=			tok.pad_token_id

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

					lowercase_  							=			get_lens(UpperCAmelCase__  )
					lowercase_  							=			SeqaSeqDataset(UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							type_path="""val"""						,							**UpperCAmelCase__  )
					lowercase_  							=			get_lens(UpperCAmelCase__  )
					pickle_save(UpperCAmelCase__						,							train_ds.len_file  )
					pickle_save(UpperCAmelCase__						,							val_ds.len_file  )


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






import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
							a					  =     {
							    'linear': PIL.Image.Resampling.BILINEAR,
							    'bilinear': PIL.Image.Resampling.BILINEAR,
							    'bicubic': PIL.Image.Resampling.BICUBIC,
							    'lanczos': PIL.Image.Resampling.LANCZOS,
							    'nearest': PIL.Image.Resampling.NEAREST,
							}
else:
							a					  =     {
							    'linear': PIL.Image.LINEAR,
							    'bilinear': PIL.Image.BILINEAR,
							    'bicubic': PIL.Image.BICUBIC,
							    'lanczos': PIL.Image.LANCZOS,
							    'nearest': PIL.Image.NEAREST,
							}


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			(images / 2 + 0.5).clamp(0						,							1  )
					lowercase_  							=			images.cpu().permute(0						,							2						,							3						,							1  ).float().numpy()
					lowercase_  							=			numpy_to_pil(UpperCAmelCase__  )
					return images



def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					if images.ndim == 3:
										lowercase_  							=			images[None, ...]
					lowercase_  							=			(images * 2_5_5).round().astype("""uint8"""  )
					if images.shape[-1] == 1:
										# special case for grayscale (single channel) images
										lowercase_  							=			[Image.fromarray(image.squeeze()						,							mode="""L"""  ) for image in images]
					else:
										lowercase_  							=			[Image.fromarray(UpperCAmelCase__  ) for image in images]

					return pil_images


