



import argparse
import os
import sys
import urllib.request
import zipfile


__A             =["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"]
__A             ={
    "CoLA": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4",
    "SST": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8",
    "MRPC": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc",
    "QQP": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5",
    "STS": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5",
    "MNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce",
    "SNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df",
    "QNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601",
    "RTE": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb",
    "WNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf",
    "diagnostic": "https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D",
}

__A             ="https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
__A             ="https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"

def 	a						(				_UpperCAmelCase							: Tuple			,       _UpperCAmelCase							: Optional[int]						):







		'''simple docstring'''





		print(f'Downloading and extracting {task}...'						)
		__UpperCAmelCase   :					Any													=     f'{task}.zip'
		urllib.request.urlretrieve(TASKaPATH[task]			,       _UpperCAmelCase						)
		with zipfile.ZipFile(_UpperCAmelCase						) as zip_ref:
				zip_ref.extractall(_UpperCAmelCase						)
		os.remove(_UpperCAmelCase						)
		print('''\tCompleted!'''						)

def 	a						(				_UpperCAmelCase							: Union[str, Any]			,       _UpperCAmelCase							: List[str]						):







		'''simple docstring'''





		print('''Processing MRPC...'''						)
		__UpperCAmelCase   :					Union[str, Any]													=     os.path.join(_UpperCAmelCase			,       '''MRPC'''						)
		if not os.path.isdir(_UpperCAmelCase						):
				os.mkdir(_UpperCAmelCase						)
		if path_to_data:
				__UpperCAmelCase   :					Union[str, Any]													=     os.path.join(_UpperCAmelCase			,       '''msr_paraphrase_train.txt'''						)
				__UpperCAmelCase   :					Any													=     os.path.join(_UpperCAmelCase			,       '''msr_paraphrase_test.txt'''						)
		else:
				print('''Local MRPC data not specified, downloading data from %s''' % MRPC_TRAIN						)
				__UpperCAmelCase   :					Optional[Any]													=     os.path.join(_UpperCAmelCase			,       '''msr_paraphrase_train.txt'''						)
				__UpperCAmelCase   :					str													=     os.path.join(_UpperCAmelCase			,       '''msr_paraphrase_test.txt'''						)
				urllib.request.urlretrieve(_UpperCAmelCase			,       _UpperCAmelCase						)
				urllib.request.urlretrieve(_UpperCAmelCase			,       _UpperCAmelCase						)
		if not os.path.isfile(_UpperCAmelCase						):
				raise ValueError(f'Train data not found at {mrpc_train_file}'						)
		if not os.path.isfile(_UpperCAmelCase						):
				raise ValueError(f'Test data not found at {mrpc_test_file}'						)
		urllib.request.urlretrieve(TASKaPATH['''MRPC''']			,       os.path.join(_UpperCAmelCase			,       '''dev_ids.tsv'''						)						)

		__UpperCAmelCase   :					Union[str, Any]													=     []
		with open(os.path.join(_UpperCAmelCase			,       '''dev_ids.tsv'''						)			,       encoding='''utf8'''						) as ids_fh:
				for row in ids_fh:
						dev_ids.append(row.strip().split('''\t'''						)						)

		with open(_UpperCAmelCase			,       encoding='''utf8'''						) as data_fh, open(
		    os.path.join(_UpperCAmelCase			,       '''train.tsv'''						)			,       '''w'''			,       encoding='''utf8'''						) as train_fh, open(os.path.join(_UpperCAmelCase			,       '''dev.tsv'''						)			,       '''w'''			,       encoding='''utf8'''						) as dev_fh:
				__UpperCAmelCase   :					Dict													=     data_fh.readline()
				train_fh.write(_UpperCAmelCase						)
				dev_fh.write(_UpperCAmelCase						)
				for row in data_fh:
						__UpperCAmelCase   :					List[Any]													=     row.strip().split('''\t'''						)
						if [ida, ida] in dev_ids:
								dev_fh.write('''%s\t%s\t%s\t%s\t%s\n''' % (label, ida, ida, sa, sa)						)
						else:
								train_fh.write('''%s\t%s\t%s\t%s\t%s\n''' % (label, ida, ida, sa, sa)						)

		with open(_UpperCAmelCase			,       encoding='''utf8'''						) as data_fh, open(
		    os.path.join(_UpperCAmelCase			,       '''test.tsv'''						)			,       '''w'''			,       encoding='''utf8'''						) as test_fh:
				__UpperCAmelCase   :					List[Any]													=     data_fh.readline()
				test_fh.write('''index\t#1 ID\t#2 ID\t#1 String\t#2 String\n'''						)
				for idx, row in enumerate(_UpperCAmelCase						):
						__UpperCAmelCase   :					Union[str, Any]													=     row.strip().split('''\t'''						)
						test_fh.write('''%d\t%s\t%s\t%s\t%s\n''' % (idx, ida, ida, sa, sa)						)
		print('''\tCompleted!'''						)

def 	a						(				_UpperCAmelCase							: Optional[int]						):







		'''simple docstring'''





		print('''Downloading and extracting diagnostic...'''						)
		if not os.path.isdir(os.path.join(_UpperCAmelCase			,       '''diagnostic'''						)						):
				os.mkdir(os.path.join(_UpperCAmelCase			,       '''diagnostic'''						)						)
		__UpperCAmelCase   :					Optional[int]													=     os.path.join(_UpperCAmelCase			,       '''diagnostic'''			,       '''diagnostic.tsv'''						)
		urllib.request.urlretrieve(TASKaPATH['''diagnostic''']			,       _UpperCAmelCase						)
		print('''\tCompleted!'''						)
		return

def 	a						(				_UpperCAmelCase							: Optional[int]						):







		'''simple docstring'''





		__UpperCAmelCase   :					Optional[int]													=     task_names.split(''','''						)
		if "all" in task_names:
				__UpperCAmelCase   :					Tuple													=     TASKS
		else:
				__UpperCAmelCase   :					Optional[int]													=     []
				for task_name in task_names:
						if task_name not in TASKS:
								raise ValueError(f'Task {task_name} not found!'						)
						tasks.append(_UpperCAmelCase						)
		return tasks

def 	a						(				_UpperCAmelCase							: int						):







		'''simple docstring'''





		__UpperCAmelCase   :					Union[str, Any]													=     argparse.ArgumentParser()
		parser.add_argument('''--data_dir'''			,       help='''directory to save data to'''			,       type=_UpperCAmelCase			,       default='''glue_data'''						)
		parser.add_argument(
		    '''--tasks'''			,       help='''tasks to download data for as a comma separated string'''			,       type=_UpperCAmelCase			,       default='''all'''						)
		parser.add_argument(
		    '''--path_to_mrpc'''			,       help='''path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt'''			,       type=_UpperCAmelCase			,       default=''''''			,       )
		__UpperCAmelCase   :					Optional[Any]													=     parser.parse_args(_UpperCAmelCase						)

		if not os.path.isdir(args.data_dir						):
				os.mkdir(args.data_dir						)
		__UpperCAmelCase   :					Optional[Any]													=     get_tasks(args.tasks						)

		for task in tasks:
				if task == "MRPC":
						format_mrpc(args.data_dir			,       args.path_to_mrpc						)
				elif task == "diagnostic":
						download_diagnostic(args.data_dir						)
				else:
						download_and_extract(_UpperCAmelCase			,       args.data_dir						)


if __name__ == "__main__":
					sys.exit(main(sys.argv[1:]))










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 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			int	,	a_							:			str	,	a_							:			List[str]=sys.maxsize			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     '''bilinear'''
					__UpperCAmelCase   :					str													=     max_size
					__UpperCAmelCase   :					Dict													=     short_edge_length




			def __call__(       self							:			str	,	a_							:			Union[str, Any]			):
					'''simple docstring'''





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

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

					return img_augs

class 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			Tuple	,	a_							:			List[Any]			):
					'''simple docstring'''





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




			def 			snake_case__					(       self							:			int	,	a_							:			Tuple			):
					'''simple docstring'''





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

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




			def __call__(       self							:			List[str]	,	a_							:			int	,	a_							:			int=False			):
					'''simple docstring'''





					with torch.no_grad():
							if not isinstance(a_	,	a_			):
									__UpperCAmelCase   :					Optional[Any]													=     [images]
							if single_image:
									assert len(a_			) == 1
							for i in range(len(a_			)			):
									if isinstance(images[i]	,	torch.Tensor			):
											images.insert(a_	,	images.pop(a_			).to(self.device			).float()			)
									elif not isinstance(images[i]	,	torch.Tensor			):
											images.insert(
											    a_	,	torch.as_tensor(img_tensorize(images.pop(a_			)	,	input_format=self.input_format			)			)
											    .to(self.device			)
											    .float()	,	)
            # resize smallest edge
							__UpperCAmelCase   :					Optional[Any]													=     torch.tensor([im.shape[:2] for im in images]			)
							__UpperCAmelCase   :					Dict													=     self.aug(a_			)
							# 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   :					Optional[int]													=     [self.normalizer(a_			) for x in images]
							# now pad them to do the following operations
							__UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     self.pad(a_			)
							# Normalize

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

def 	a						(				_UpperCAmelCase							: str			,       _UpperCAmelCase							: Optional[int]						):







		'''simple docstring'''





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

def 	a						(				_UpperCAmelCase							: Any			,       _UpperCAmelCase							: Tuple[int, int]						):







		'''simple docstring'''





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





