





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


UpperCAmelCase						:       List[Any]        =					['''CoLA''', '''SST''', '''MRPC''', '''QQP''', '''STS''', '''MNLI''', '''SNLI''', '''QNLI''', '''RTE''', '''WNLI''', '''diagnostic''']
UpperCAmelCase						:       Any        =					{
    '''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''',
}

UpperCAmelCase						:       List[str]        =					'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'''
UpperCAmelCase						:       str        =					'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'''







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				Optional[int]:
					print(F"""Downloading and extracting {task}..."""  )
					__A     :						Tuple          =    F"""{task}.zip"""
					urllib.request.urlretrieve(TASKaPATH[task] ,						a  )
					with zipfile.ZipFile(a  ) as zip_ref:
										zip_ref.extractall(a  )
					os.remove(a  )
					print('\tCompleted!'  )







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

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

					with open(a ,						encoding='utf8'  ) as data_fh, open(
					    os.path.join(a ,						'train.tsv'  ) ,						'w' ,						encoding='utf8'  ) as train_fh, open(os.path.join(a ,						'dev.tsv'  ) ,						'w' ,						encoding='utf8'  ) as dev_fh:
										__A     :						Any          =    data_fh.readline()
										train_fh.write(a  )
										dev_fh.write(a  )
										for row in data_fh:
															__A   ,    __A   ,    __A   ,    __A   ,    __A     :						Dict          =    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(a ,						encoding='utf8'  ) as data_fh, open(
					    os.path.join(a ,						'test.tsv'  ) ,						'w' ,						encoding='utf8'  ) as test_fh:
										__A     :						int          =    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(a  ):
															__A   ,    __A   ,    __A   ,    __A   ,    __A     :						List[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 			_SCREAMING_SNAKE_CASE    (       a  )				->				List[str]:
					print('Downloading and extracting diagnostic...'  )
					if not os.path.isdir(os.path.join(a ,						'diagnostic'  )  ):
										os.mkdir(os.path.join(a ,						'diagnostic'  )  )
					__A     :						List[str]          =    os.path.join(a ,						'diagnostic' ,						'diagnostic.tsv'  )
					urllib.request.urlretrieve(TASKaPATH['diagnostic'] ,						a  )
					print('\tCompleted!'  )
					return







def 			_SCREAMING_SNAKE_CASE    (       a  )				->				Dict:
					__A     :						Union[str, Any]          =    task_names.split(','  )
					if "all" in task_names:
										__A     :						str          =    TASKS
					else:
										__A     :						List[Any]          =    []
										for task_name in task_names:
															if task_name not in TASKS:
																				raise ValueError(F"""Task {task_name} not found!"""  )
															tasks.append(a  )
					return tasks







def 			_SCREAMING_SNAKE_CASE    (       a  )				->				Tuple:
					__A     :						int          =    argparse.ArgumentParser()
					parser.add_argument('--data_dir' ,						help='directory to save data to' ,						type=a ,						default='glue_data'  )
					parser.add_argument(
					    '--tasks' ,						help='tasks to download data for as a comma separated string' ,						type=a ,						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=a ,						default='' ,						)
					__A     :						Union[str, Any]          =    parser.parse_args(a  )

					if not os.path.isdir(args.data_dir  ):
										os.mkdir(args.data_dir  )
					__A     :						Union[str, 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(a ,						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


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import TvltImageProcessor







def 			_SCREAMING_SNAKE_CASE    (       a ,						a=10 ,						a=10 ,						a=False ,						a=False  )				->				Union[str, Any]:
					__A     :						str          =    []
					for i in range(image_processor_tester.num_frames  ):
										video.append(np.random.randint(2_55 ,						size=(image_processor_tester.num_channels, width, height) ,						dtype=np.uinta  )  )

					if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										__A     :						Union[str, Any]          =    [Image.fromarray(np.moveaxis(a ,						0 ,						-1  )  ) for frame in video]

					if torchify:
										__A     :						Dict          =    [torch.from_numpy(a  ) for frame in video]

					return video







def 			_SCREAMING_SNAKE_CASE    (       a ,						a=False ,						a=False ,						a=False  )				->				Union[str, Any]:
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					__A     :						List[Any]          =    []
					for i in range(image_processor_tester.batch_size  ):
										if equal_resolution:
															__A     :						int          =    image_processor_tester.max_resolution
										else:
															__A   ,    __A     :						Tuple          =    np.random.choice(
															    np.arange(image_processor_tester.min_resolution ,						image_processor_tester.max_resolution  ) ,						2  )
															__A     :						Optional[int]          =    prepare_video(
															    image_processor_tester=a ,						width=a ,						height=a ,						numpify=a ,						torchify=a ,						)
										video_inputs.append(a  )

					return video_inputs





class 						_A( unittest.TestCase       ):


				"""simple docstring"""


				def __init__(      self					, _A					, _A=7					, _A=3					, _A=4					, _A=18					, _A=30					, _A=400					, _A=True					, _A=None					, _A=True					, _A=[0.5, 0.5, 0.5]					, _A=[0.5, 0.5, 0.5]					, _A=True					, _A=None					, ):
									__A     :						List[str]          =    size if size is not None else {'shortest_edge': 18}
									__A     :						Tuple          =    crop_size if crop_size is not None else {'height': 18, 'width': 18}

									__A     :						Dict          =    parent
									__A     :						str          =    batch_size
									__A     :						int          =    num_channels
									__A     :						Optional[Any]          =    num_frames
									__A     :						Any          =    image_size
									__A     :						List[str]          =    min_resolution
									__A     :						List[Any]          =    max_resolution
									__A     :						Tuple          =    do_resize
									__A     :						List[str]          =    size
									__A     :						str          =    do_normalize
									__A     :						str          =    image_mean
									__A     :						int          =    image_std
									__A     :						Optional[Any]          =    do_center_crop
									__A     :						Union[str, Any]          =    crop_size





				def  UpperCAmelCase_		(      self					):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									}





@require_torch
@require_vision
class 						_A( snake_case__    ,			unittest.TestCase       ):


				"""simple docstring"""


				UpperCamelCase   :  Optional[Any]    =     TvltImageProcessor if is_vision_available() else None



				def  UpperCAmelCase_		(      self					):
									__A     :						str          =    TvltImageProcessorTester(self					)



				@property
				def  UpperCAmelCase_		(      self					):
									return self.image_processor_tester.prepare_image_processor_dict()



				def  UpperCAmelCase_		(      self					):
									__A     :						Union[str, Any]          =    self.image_processing_class(**self.image_processor_dict					)
									self.assertTrue(hasattr(_A					, 'image_mean'					)					)
									self.assertTrue(hasattr(_A					, 'image_std'					)					)
									self.assertTrue(hasattr(_A					, 'do_normalize'					)					)
									self.assertTrue(hasattr(_A					, 'do_resize'					)					)
									self.assertTrue(hasattr(_A					, 'do_center_crop'					)					)
									self.assertTrue(hasattr(_A					, 'size'					)					)



				def  UpperCAmelCase_		(      self					):
									# Initialize image_processor
									__A     :						List[str]          =    self.image_processing_class(**self.image_processor_dict					)
									# create random PIL videos
									__A     :						List[str]          =    prepare_video_inputs(self.image_processor_tester					, equal_resolution=_A					)
									for video in video_inputs:
														self.assertIsInstance(_A					, _A					)
														self.assertIsInstance(video[0]					, Image.Image					)

									# Test not batched input
									__A     :						str          =    image_processor(video_inputs[0]					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )

									# Test batched
									__A     :						List[Any]          =    image_processor(_A					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )



				def  UpperCAmelCase_		(      self					):
									# Initialize image_processor
									__A     :						Any          =    self.image_processing_class(**self.image_processor_dict					)
									# create random numpy tensors
									__A     :						Dict          =    prepare_video_inputs(self.image_processor_tester					, equal_resolution=_A					, numpify=_A					)
									for video in video_inputs:
														self.assertIsInstance(_A					, _A					)
														self.assertIsInstance(video[0]					, np.ndarray					)

									# Test not batched input
									__A     :						Dict          =    image_processor(video_inputs[0]					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )

									# Test batched
									__A     :						Optional[int]          =    image_processor(_A					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )





				def  UpperCAmelCase_		(      self					):
									# Initialize image_processor
									__A     :						Any          =    self.image_processing_class(**self.image_processor_dict					)
									# create random PyTorch tensors
									__A     :						Dict          =    prepare_video_inputs(self.image_processor_tester					, equal_resolution=_A					, torchify=_A					)
									for video in video_inputs:
														self.assertIsInstance(_A					, _A					)
														self.assertIsInstance(video[0]					, torch.Tensor					)

									# Test not batched input
									__A     :						Optional[Any]          =    image_processor(video_inputs[0]					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )

									# Test batched
									__A     :						int          =    image_processor(_A					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )




