





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 lowerCAmelCase__(    __snake_case	,__snake_case							)    ->				List[Any]:







	'''simple docstring'''







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





def lowerCAmelCase__(    __snake_case	,__snake_case							)    ->				Any:







	'''simple docstring'''







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

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

	with open(__snake_case	,encoding='''utf8'''							) as data_fh, open(
	    os.path.join(__snake_case	,'''train.tsv'''							)	,'''w'''	,encoding='''utf8'''							) as train_fh, open(os.path.join(__snake_case	,'''dev.tsv'''							)	,'''w'''	,encoding='''utf8'''							) as dev_fh:
		lowerCamelCase__        =		data_fh.readline()
		train_fh.write(__snake_case							)
		dev_fh.write(__snake_case							)
		for row in data_fh:
			lowerCamelCase__        =		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(__snake_case	,encoding='''utf8'''							) as data_fh, open(
	    os.path.join(__snake_case	,'''test.tsv'''							)	,'''w'''	,encoding='''utf8'''							) as test_fh:
		lowerCamelCase__        =		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(__snake_case							):
			lowerCamelCase__        =		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 lowerCAmelCase__(    __snake_case							)    ->				int:







	'''simple docstring'''







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





def lowerCAmelCase__(    __snake_case							)    ->				Tuple:







	'''simple docstring'''







	lowerCamelCase__        =		task_names.split(''','''							)
	if "all" in task_names:
		lowerCamelCase__        =		TASKS
	else:
		lowerCamelCase__        =		[]
		for task_name in task_names:
			if task_name not in TASKS:
				raise ValueError(F'Task {task_name} not found!'							)
			tasks.append(__snake_case							)
	return tasks





def lowerCAmelCase__(    __snake_case							)    ->				Tuple:







	'''simple docstring'''







	lowerCamelCase__        =		argparse.ArgumentParser()
	parser.add_argument('''--data_dir'''	,help='''directory to save data to'''	,type=__snake_case	,default='''glue_data'''							)
	parser.add_argument(
	    '''--tasks'''	,help='''tasks to download data for as a comma separated string'''	,type=__snake_case	,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=__snake_case	,default=''''''	,)
	lowerCamelCase__        =		parser.parse_args(__snake_case							)

	if not os.path.isdir(args.data_dir							):
		os.mkdir(args.data_dir							)
	lowerCamelCase__        =		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(__snake_case	,args.data_dir							)


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










import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ViltImageProcessor






class  __A   (   unittest.TestCase			):

			'''simple docstring'''







			def __init__(   self	,  __lowerCAmelCase	,  __lowerCAmelCase=7	,  __lowerCAmelCase=3	,  __lowerCAmelCase=1_8	,  __lowerCAmelCase=3_0	,  __lowerCAmelCase=4_0_0	,  __lowerCAmelCase=True	,  __lowerCAmelCase=None	,  __lowerCAmelCase=2	,  __lowerCAmelCase=True	,  __lowerCAmelCase=[0.5, 0.5, 0.5]	,  __lowerCAmelCase=[0.5, 0.5, 0.5]	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		size if size is not None else {'''shortest_edge''': 3_0}
				lowerCamelCase__        =		parent
				lowerCamelCase__        =		batch_size
				lowerCamelCase__        =		num_channels
				lowerCamelCase__        =		image_size
				lowerCamelCase__        =		min_resolution
				lowerCamelCase__        =		max_resolution
				lowerCamelCase__        =		do_resize
				lowerCamelCase__        =		size
				lowerCamelCase__        =		size_divisor
				lowerCamelCase__        =		do_normalize
				lowerCamelCase__        =		image_mean
				lowerCamelCase__        =		image_std





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "size_divisor": self.size_divisor,
				}





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase=False							):



				'''simple docstring'''







				if not batched:
					lowerCamelCase__        =		self.size['''shortest_edge''']
					lowerCamelCase__        =		image_inputs[0]
					if isinstance(__lowerCAmelCase	,  Image.Image							):
						lowerCamelCase__    ,     lowerCamelCase__        =		image.size
					else:
						lowerCamelCase__    ,     lowerCamelCase__        =		image.shape[1], image.shape[2]
					lowerCamelCase__        =		size / min(__lowerCAmelCase	,  __lowerCAmelCase							)
					if h < w:
						lowerCamelCase__    ,     lowerCamelCase__        =		size, scale * w
					else:
						lowerCamelCase__    ,     lowerCamelCase__        =		scale * h, size

					lowerCamelCase__        =		int((1_3_3_3 / 8_0_0) * size							)
					if max(__lowerCAmelCase	,  __lowerCAmelCase							) > max_size:
						lowerCamelCase__        =		max_size / max(__lowerCAmelCase	,  __lowerCAmelCase							)
						lowerCamelCase__        =		newh * scale
						lowerCamelCase__        =		neww * scale

					lowerCamelCase__    ,     lowerCamelCase__        =		int(newh + 0.5							), int(neww + 0.5							)
					lowerCamelCase__    ,     lowerCamelCase__        =		(
					    newh // self.size_divisor * self.size_divisor,
					    neww // self.size_divisor * self.size_divisor,
					)

				else:
					lowerCamelCase__        =		[]
					for image in image_inputs:
						lowerCamelCase__    ,     lowerCamelCase__        =		self.get_expected_values([image]							)
						expected_values.append((expected_height, expected_width)							)
					lowerCamelCase__        =		max(__lowerCAmelCase	,  key=lambda __lowerCAmelCase							: item[0]							)[0]
					lowerCamelCase__        =		max(__lowerCAmelCase	,  key=lambda __lowerCAmelCase							: item[1]							)[1]

				return expected_height, expected_width






@require_torch
@require_vision
class  __A   (   lowerCAmelCase      ,    unittest.TestCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						ViltImageProcessor if is_vision_available() else None





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		ViltImageProcessingTester(self							)





			@property
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.image_processing_class(**self.image_processor_dict							)
				self.assertTrue(hasattr(__lowerCAmelCase	,  '''image_mean'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase	,  '''image_std'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase	,  '''do_normalize'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase	,  '''do_resize'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase	,  '''size'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase	,  '''size_divisor'''							)							)





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.image_processing_class.from_dict(self.image_processor_dict							)
				self.assertEqual(image_processor.size	,  {'''shortest_edge''': 3_0}							)

				lowerCamelCase__        =		self.image_processing_class.from_dict(self.image_processor_dict	,  size=4_2							)
				self.assertEqual(image_processor.size	,  {'''shortest_edge''': 4_2}							)





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.image_processing_class(**self.image_processor_dict							)
				# create random PIL images
				lowerCamelCase__        =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=__lowerCAmelCase							)
				for image in image_inputs:
					self.assertIsInstance(__lowerCAmelCase	,  Image.Image							)

				# Test not batched input
				lowerCamelCase__        =		image_processing(image_inputs[0]	,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__    ,     lowerCamelCase__        =		self.image_processor_tester.get_expected_values(__lowerCAmelCase							)
				self.assertEqual(
				    encoded_images.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

				# Test batched
				lowerCamelCase__        =		image_processing(__lowerCAmelCase	,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__    ,     lowerCamelCase__        =		self.image_processor_tester.get_expected_values(__lowerCAmelCase	,  batched=__lowerCAmelCase							)
				self.assertEqual(
				    encoded_images.shape	,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )	,  )





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.image_processing_class(**self.image_processor_dict							)
				# create random numpy tensors
				lowerCamelCase__        =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=__lowerCAmelCase	,  numpify=__lowerCAmelCase							)
				for image in image_inputs:
					self.assertIsInstance(__lowerCAmelCase	,  np.ndarray							)

				# Test not batched input
				lowerCamelCase__        =		image_processing(image_inputs[0]	,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__    ,     lowerCamelCase__        =		self.image_processor_tester.get_expected_values(__lowerCAmelCase							)
				self.assertEqual(
				    encoded_images.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

				# Test batched
				lowerCamelCase__        =		image_processing(__lowerCAmelCase	,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__    ,     lowerCamelCase__        =		self.image_processor_tester.get_expected_values(__lowerCAmelCase	,  batched=__lowerCAmelCase							)
				self.assertEqual(
				    encoded_images.shape	,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )	,  )





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.image_processing_class(**self.image_processor_dict							)
				# create random PyTorch tensors
				lowerCamelCase__        =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=__lowerCAmelCase	,  torchify=__lowerCAmelCase							)
				for image in image_inputs:
					self.assertIsInstance(__lowerCAmelCase	,  torch.Tensor							)

				# Test not batched input
				lowerCamelCase__        =		image_processing(image_inputs[0]	,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__    ,     lowerCamelCase__        =		self.image_processor_tester.get_expected_values(__lowerCAmelCase							)
				self.assertEqual(
				    encoded_images.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

				# Test batched
				lowerCamelCase__        =		image_processing(__lowerCAmelCase	,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__    ,     lowerCamelCase__        =		self.image_processor_tester.get_expected_values(__lowerCAmelCase	,  batched=__lowerCAmelCase							)
				self.assertEqual(
				    encoded_images.shape	,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )	,  )



