






'''simple docstring'''





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


_UpperCamelCase		:		Any								=			['CoLA', 'SST', 'MRPC', 'QQP', 'STS', 'MNLI', 'SNLI', 'QNLI', 'RTE', 'WNLI', 'diagnostic']
_UpperCamelCase		:		Dict								=			{
    '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		:		Union[str, Any]								=			'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'
def __UpperCAmelCase (			A			:	Any	,   A			:	Optional[Any]						)  -> int:
			print(F"Downloading and extracting {task}..."						)
			UpperCAmelCase_     :   str       =   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 __UpperCAmelCase (			A			:	str	,   A			:	List[str]						)  -> Optional[int]:
			print('''Processing MRPC...'''						)
			UpperCAmelCase_     :   Optional[Any]       =   os.path.join(A	,   '''MRPC'''						)
			if not os.path.isdir(A						):
						os.mkdir(A						)
			if path_to_data:
						UpperCAmelCase_     :   str       =   os.path.join(A	,   '''msr_paraphrase_train.txt'''						)
						UpperCAmelCase_     :   int       =   os.path.join(A	,   '''msr_paraphrase_test.txt'''						)
			else:
						print('''Local MRPC data not specified, downloading data from %s''' % MRPC_TRAIN						)
						UpperCAmelCase_     :   Optional[Any]       =   os.path.join(A	,   '''msr_paraphrase_train.txt'''						)
						UpperCAmelCase_     :   List[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'''						)						)

			UpperCAmelCase_     :   List[Any]       =   []
			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:
						UpperCAmelCase_     :   Dict       =   data_fh.readline()
						train_fh.write(A						)
						dev_fh.write(A						)
						for row in data_fh:
									UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[int]       =   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:
						UpperCAmelCase_     :   Union[str, 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(A						):
									UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_     :   Dict       =   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 __UpperCAmelCase (			A			:	Any						)  -> int:
			print('''Downloading and extracting diagnostic...'''						)
			if not os.path.isdir(os.path.join(A	,   '''diagnostic'''						)						):
						os.mkdir(os.path.join(A	,   '''diagnostic'''						)						)
			UpperCAmelCase_     :   Tuple       =   os.path.join(A	,   '''diagnostic'''	,   '''diagnostic.tsv'''						)
			urllib.request.urlretrieve(TASKaPATH['''diagnostic''']	,   A						)
			print('''\tCompleted!'''						)
			return
def __UpperCAmelCase (			A			:	Union[str, Any]						)  -> int:
			UpperCAmelCase_     :   Optional[int]       =   task_names.split(''','''						)
			if "all" in task_names:
						UpperCAmelCase_     :   Dict       =   TASKS
			else:
						UpperCAmelCase_     :   Optional[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 __UpperCAmelCase (			A			:	str						)  -> Optional[int]:
			UpperCAmelCase_     :   List[Any]       =   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=''''''	,   )
			UpperCAmelCase_     :   Optional[int]       =   parser.parse_args(A						)

			if not os.path.isdir(args.data_dir						):
						os.mkdir(args.data_dir						)
			UpperCAmelCase_     :   Dict       =   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:]))










'''simple docstring'''





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class 				snake_case__     (			unittest.TestCase):




					@property
					def 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Optional[int]       =   1
								UpperCAmelCase_     :   List[str]       =   3
								UpperCAmelCase_     :   Optional[int]       =   8
								UpperCAmelCase_     :   Optional[Any]       =   8

								UpperCAmelCase_     :   Any       =   torch.rand((batch_size, num_channels, height, width)     )

								return sample




					def 						A     (       self       :						Dict							,				_A       :						str     )  ->    List[Any]:
								if isinstance(image[0]							,				PIL.Image.Image     ):
											return np.stack([np.array(_A     ) for i in image]							,				axis=0     )
								elif isinstance(_A							,				torch.Tensor     ):
											return image.cpu().numpy().transpose(0							,				2							,				3							,				1     )
								return image




					def 						A     (       self       :						Tuple     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[int]       =   self.to_np(_A     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Any       =   image_processor.postprocess(
											    image_processor.preprocess(_A     )							,				output_type=_A							,				)
											UpperCAmelCase_     :   Union[str, Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Union[str, Any]       =   (input_np * 2_55).round() if output_type == '''pil''' else input_np
											assert (
											    np.abs(in_np - out_np     ).max() < 1e-6
											), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Union[str, Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )
								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample.cpu().numpy().transpose(0							,				2							,				3							,				1     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Union[str, Any]       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )

											UpperCAmelCase_     :   List[Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Any       =   (input_np * 2_55).round() if output_type == '''pil''' else input_np
											assert (
											    np.abs(in_np - out_np     ).max() < 1e-6
											), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Any     )  ->    Optional[Any]:
								UpperCAmelCase_     :   Tuple       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[Any]       =   self.dummy_sample.cpu().numpy().transpose(0							,				2							,				3							,				1     )
								UpperCAmelCase_     :   List[Any]       =   image_processor.numpy_to_pil(_A     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   str       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )
											for i, o in zip(_A							,				_A     ):
														UpperCAmelCase_     :   Optional[int]       =   np.array(_A     )
														UpperCAmelCase_     :   Dict       =   self.to_np(_A     ) if output_type == '''pil''' else (self.to_np(_A     ) * 2_55).round()
														assert (
														    np.abs(in_np - out_np     ).max() < 1e-6
														), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Optional[Any]     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[Any]       =   input_pt_ad.squeeze(0     )

								UpperCAmelCase_     :   Tuple       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Any       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   str       =   self.to_np(self.dummy_sample     )
								UpperCAmelCase_     :   Tuple       =   input_np_ad.squeeze(0     )

								UpperCAmelCase_     :   Optional[int]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Optional[Any]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								assert np.abs(out_pt_ad - out_pt_ad     ).max() < 1e-6
								assert np.abs(out_np_ad - out_np_ad     ).max() < 1e-6





					def 						A     (       self       :						Dict     )  ->    Dict:
								UpperCAmelCase_     :   List[Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[int]       =   self.dummy_sample
								UpperCAmelCase_     :   Dict       =   list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[Any]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Dict       =   self.to_np(self.dummy_sample     )
								list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[int]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								assert np.abs(out_pt_ad - out_pt_list     ).max() < 1e-6
								assert np.abs(out_np_ad - out_np_list     ).max() < 1e-6


