


'''simple docstring'''
import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class    lowercase	:

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=sys.maxsize							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= '''bilinear'''
							UpperCamelCase__   :Any												= max_size
							UpperCamelCase__   :str												= short_edge_length




		def __call__(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= []
							for img in imgs:
												UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= img.shape[:2]
												# later: provide list and randomly choose index for resize
												UpperCamelCase__   :int												= np.random.randint(self.short_edge_length[0]    ,   self.short_edge_length[1] + 1							)
												if size == 0:
																	return img
												UpperCamelCase__   :Optional[int]												= size * 1.0 / min(UpperCamelCase_    ,   UpperCamelCase_							)
												if h < w:
																	UpperCamelCase__					,				UpperCamelCase__   :Any												= size, scale * w
												else:
																	UpperCamelCase__					,				UpperCamelCase__   :Tuple												= scale * h, size
												if max(UpperCamelCase_    ,   UpperCamelCase_							) > self.max_size:
																	UpperCamelCase__   :Union[str, Any]												= self.max_size * 1.0 / max(UpperCamelCase_    ,   UpperCamelCase_							)
																	UpperCamelCase__   :Any												= newh * scale
																	UpperCamelCase__   :str												= neww * scale
												UpperCamelCase__   :Union[str, Any]												= int(neww + 0.5							)
												UpperCamelCase__   :Any												= int(newh + 0.5							)

												if img.dtype == np.uinta:
																	UpperCamelCase__   :str												= Image.fromarray(UpperCamelCase_							)
																	UpperCamelCase__   :Optional[Any]												= pil_image.resize((neww, newh)    ,   PILImageResampling.BILINEAR							)
																	UpperCamelCase__   :List[str]												= np.asarray(UpperCamelCase_							)
												else:
																	UpperCamelCase__   :Optional[int]												= img.permute(2    ,   0    ,   1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
																	UpperCamelCase__   :str												= nn.functional.interpolate(
																	    UpperCamelCase_    ,   (newh, neww)    ,   mode=self.interp_method    ,   align_corners=UpperCamelCase_							).squeeze(0							)
												img_augs.append(UpperCamelCase_							)

							return img_augs



class    lowercase	:

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :str												= ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]    ,   cfg.INPUT.MAX_SIZE_TEST							)
							UpperCamelCase__   :Optional[int]												= cfg.INPUT.FORMAT
							UpperCamelCase__   :Optional[Any]												= cfg.SIZE_DIVISIBILITY
							UpperCamelCase__   :Tuple												= cfg.PAD_VALUE
							UpperCamelCase__   :Union[str, Any]												= cfg.INPUT.MAX_SIZE_TEST
							UpperCamelCase__   :List[str]												= cfg.MODEL.DEVICE
							UpperCamelCase__   :str												= torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)    ,   1    ,   1							)
							UpperCamelCase__   :List[str]												= torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)    ,   1    ,   1							)
							UpperCamelCase__   :List[Any]												= lambda UpperCamelCase_							: (x - self.pixel_mean) / self.pixel_std




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :str												= tuple(max(UpperCamelCase_							) for s in zip(*[img.shape for img in images]							)							)
							UpperCamelCase__   :Optional[Any]												= [im.shape[-2:] for im in images]
							UpperCamelCase__   :Dict												= [
							    nn.functional.pad(
							        UpperCamelCase_    ,   [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]    ,   value=self.pad_value    ,   )
							    for size, im in zip(UpperCamelCase_    ,   UpperCamelCase_							)
							]

							return torch.stack(UpperCamelCase_							), torch.tensor(UpperCamelCase_							)




		def __call__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=False							):

							'''simple docstring'''


							with torch.no_grad():
												if not isinstance(UpperCamelCase_    ,   UpperCamelCase_							):
																	UpperCamelCase__   :Union[str, Any]												= [images]
												if single_image:
																	assert len(UpperCamelCase_							) == 1
												for i in range(len(UpperCamelCase_							)							):
																	if isinstance(images[i]    ,   torch.Tensor							):
																						images.insert(UpperCamelCase_    ,   images.pop(UpperCamelCase_							).to(self.device							).float()							)
																	elif not isinstance(images[i]    ,   torch.Tensor							):
																						images.insert(
																						    UpperCamelCase_    ,   torch.as_tensor(img_tensorize(images.pop(UpperCamelCase_							)    ,   input_format=self.input_format							)							)
																						    .to(self.device							)
																						    .float()    ,   )
            # resize smallest edge
												UpperCamelCase__   :Tuple												= torch.tensor([im.shape[:2] for im in images]							)
												UpperCamelCase__   :Optional[Any]												= self.aug(UpperCamelCase_							)
												# transpose images and convert to torch tensors
												# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
												# now normalize before pad to avoid useless arithmetic
												UpperCamelCase__   :Tuple												= [self.normalizer(UpperCamelCase_							) for x in images]
												# now pad them to do the following operations
												UpperCamelCase__					,				UpperCamelCase__   :Dict												= self.pad(UpperCamelCase_							)
												# Normalize

												if self.size_divisibility > 0:
																	raise NotImplementedError()
												# pad
												UpperCamelCase__   :Union[str, Any]												= torch.true_divide(UpperCamelCase_    ,   UpperCamelCase_							)
												if single_image:
																	return images[0], sizes[0], scales_yx[0]
												else:
																	return images, sizes, scales_yx



def 			a      (			__a ,					__a   )  ->	List[Any]:



					'''simple docstring'''






					boxes[:, 0::2] *= scale_yx[:, 1]
					boxes[:, 1::2] *= scale_yx[:, 0]
					return boxes







def 			a      (			__a ,					__a   )  ->	str:



					'''simple docstring'''






					assert torch.isfinite(__a   ).all(), "Box tensor contains infinite or NaN!"
					UpperCamelCase__					,				UpperCamelCase__   :Tuple												= box_size
					tensor[:, 0].clamp_(min=0 ,					max=__a   )
					tensor[:, 1].clamp_(min=0 ,					max=__a   )
					tensor[:, 2].clamp_(min=0 ,					max=__a   )
					tensor[:, 3].clamp_(min=0 ,					max=__a   )



'''simple docstring'''
from pathlib import Path

import fire
from tqdm import tqdm


def 			a      (			__a="ro" ,					__a="en" ,					__a="wmt16" ,					__a=None   )  ->	None:



					'''simple docstring'''






					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''   )
					UpperCamelCase__   :int												= f'''{src_lang}-{tgt_lang}'''
					print(f'''Converting {dataset}-{pair}'''   )
					UpperCamelCase__   :Tuple												= datasets.load_dataset(__a ,					__a   )
					if save_dir is None:
										UpperCamelCase__   :Any												= f'''{dataset}-{pair}'''
					UpperCamelCase__   :Dict												= Path(__a   )
					save_dir.mkdir(exist_ok=__a   )

					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
										UpperCamelCase__   :Dict												= '''val''' if split == '''validation''' else split
										UpperCamelCase__   :List[Any]												= save_dir.joinpath(f'''{fn}.source'''   )
										UpperCamelCase__   :int												= save_dir.joinpath(f'''{fn}.target'''   )
										UpperCamelCase__   :Union[str, Any]												= src_path.open('''w+'''   )
										UpperCamelCase__   :Tuple												= 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]   ):
															UpperCamelCase__   :Union[str, Any]												= 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)