


"""simple docstring"""






from pathlib import Path

import fire
from tqdm import tqdm


def __lowercase     (      lowerCamelCase_  :      Optional[int]="ro"						,					lowerCamelCase_  :      Tuple="en"						,					lowerCamelCase_  :      Optional[Any]="wmt16"						,					lowerCamelCase_  :      List[str]=None  ):
				try:
								import datasets
				except (ModuleNotFoundError, ImportError):
								raise ImportError("run pip install datasets"  )
				SCREAMING_SNAKE_CASE__     							=				F'''{src_lang}-{tgt_lang}'''
				print(F'''Converting {dataset}-{pair}'''  )
				SCREAMING_SNAKE_CASE__     							=				datasets.load_dataset(lowerCamelCase_						,					lowerCamelCase_  )
				if save_dir is None:
								SCREAMING_SNAKE_CASE__     							=				F'''{dataset}-{pair}'''
				SCREAMING_SNAKE_CASE__     							=				Path(lowerCamelCase_  )
				save_dir.mkdir(exist_ok=lowerCamelCase_  )

				for split in ds.keys():
								print(F'''Splitting {split} with {ds[split].num_rows} records'''  )

								# to save to val.source, val.target like summary datasets
								SCREAMING_SNAKE_CASE__     							=				"val" if split == "validation" else split
								SCREAMING_SNAKE_CASE__     							=				save_dir.joinpath(F'''{fn}.source'''  )
								SCREAMING_SNAKE_CASE__     							=				save_dir.joinpath(F'''{fn}.target'''  )
								SCREAMING_SNAKE_CASE__     							=				src_path.open("w+"  )
								SCREAMING_SNAKE_CASE__     							=				tgt_path.open("w+"  )

								# reader is the bottleneck so writing one record at a time doesn't slow things down
								for x in tqdm(ds[split]  ):
												SCREAMING_SNAKE_CASE__     							=				x["translation"]
												src_fp.write(ex[src_lang] + "\n"  )
												tgt_fp.write(ex[tgt_lang] + "\n"  )

				print(F'''Saved {dataset} dataset to {save_dir}'''  )


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




"""simple docstring"""






import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_lowerCamelCase     =  0b1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
_lowerCamelCase     =  [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]


class 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self     ):
								SCREAMING_SNAKE_CASE__     							=				WATERMARK_BITS
								SCREAMING_SNAKE_CASE__     							=				WatermarkEncoder()

								self.encoder.set_watermark("bits"       ,		self.watermark     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								# can't encode images that are smaller than 256
								if images.shape[-1] < 256:
												return images

								SCREAMING_SNAKE_CASE__     							=				(255 * (images / 2 + 0.5)).cpu().permute(0       ,		2       ,		3       ,		1     ).float().numpy()

								SCREAMING_SNAKE_CASE__     							=				[self.encoder.encode(UpperCAmelCase__       ,		"dwtDct"     ) for image in images]

								SCREAMING_SNAKE_CASE__     							=				torch.from_numpy(np.array(UpperCAmelCase__     )     ).permute(0       ,		3       ,		1       ,		2     )

								SCREAMING_SNAKE_CASE__     							=				torch.clamp(2 * (images / 255 - 0.5)       ,		min=-1.0       ,		max=1.0     )
								return images
