






"""simple docstring"""

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


lowercase           =							['''CoLA''', '''SST''', '''MRPC''', '''QQP''', '''STS''', '''MNLI''', '''SNLI''', '''QNLI''', '''RTE''', '''WNLI''', '''diagnostic''']
lowercase           =							{
    '''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''',
}

lowercase           =							'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'''
lowercase           =							'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'''






def _lowerCAmelCase						(	__lowerCamelCase:Union[str, Any]			,    __lowerCamelCase:Dict ):

						'''simple docstring'''






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






def _lowerCAmelCase						(	__lowerCamelCase:List[Any]			,    __lowerCamelCase:int ):

						'''simple docstring'''






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

						__magic_name__      =  []
						with open(os.path.join(__lowerCamelCase			,    "dev_ids.tsv" )			,    encoding="utf8" ) as ids_fh:
												for row in ids_fh:
																		dev_ids.append(row.strip().split("\t" ) )

						with open(__lowerCamelCase			,    encoding="utf8" ) as data_fh, open(
						    os.path.join(__lowerCamelCase			,    "train.tsv" )			,    "w"			,    encoding="utf8" ) as train_fh, open(os.path.join(__lowerCamelCase			,    "dev.tsv" )			,    "w"			,    encoding="utf8" ) as dev_fh:
												__magic_name__      =  data_fh.readline()
												train_fh.write(__lowerCamelCase )
												dev_fh.write(__lowerCamelCase )
												for row in data_fh:
																		__magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__      =  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(__lowerCamelCase			,    encoding="utf8" ) as data_fh, open(
						    os.path.join(__lowerCamelCase			,    "test.tsv" )			,    "w"			,    encoding="utf8" ) as test_fh:
												__magic_name__      =  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(__lowerCamelCase ):
																		__magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__      =  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						(	__lowerCamelCase:Union[str, Any] ):

						'''simple docstring'''






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






def _lowerCAmelCase						(	__lowerCamelCase:str ):

						'''simple docstring'''






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






def _lowerCAmelCase						(	__lowerCamelCase:List[str] ):

						'''simple docstring'''






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

						if not os.path.isdir(args.data_dir ):
												os.mkdir(args.data_dir )
						__magic_name__      =  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(__lowerCamelCase			,    args.data_dir )


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











"""simple docstring"""

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			import torch

			from transformers import CamembertModel

@require_torch
@require_sentencepiece
@require_tokenizers
class     A_				(   unittest.TestCase	):







				@slow
				def      _snake_case       (				self				:    Tuple						)					->       Dict:
										__magic_name__      =  CamembertModel.from_pretrained("camembert-base"						)
										model.to(__lowerCamelCase						)

										__magic_name__      =  torch.tensor(
										    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,     device=__lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__      =  model(__lowerCamelCase						)["last_hidden_state"]
										__magic_name__      =  torch.Size((1, 1_0, 7_6_8)						)
										self.assertEqual(output.shape    ,     __lowerCamelCase						)
										# compare the actual values for a slice.
										__magic_name__      =  torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,     device=__lowerCamelCase    ,     dtype=torch.float    ,     )
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]    ,     __lowerCamelCase    ,     atol=1e-4						)						)



