




"""simple docstring"""





import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase=100      ,			lowerCamelCase=" "		):
						'''simple docstring'''


						snake_case__       :					Any										=					text.split(lowerCamelCase		)
						return [character.join(text[i : i + n]		).strip() for i in range(0      ,			len(lowerCamelCase		)      ,			lowerCamelCase		)]

def 		lowerCAmelCase							( lowerCamelCase		):
						'''simple docstring'''


						snake_case__			,	snake_case__       :					List[str]										=					[], []
						for title, text in zip(documents['''title''']      ,			documents['''text''']		):
												if text is not None:
																		for passage in split_text(lowerCamelCase		):
																								titles.append(title if title is not None else ''''''		)
																								texts.append(lowerCamelCase		)
						return {"title": titles, "text": texts}

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					Optional[Any]										=					load_dataset(
						    '''csv'''      ,			data_files=[csv_path]      ,			split='''train'''      ,			delimiter='''\t'''      ,			column_names=['''title''', '''text''']		)
						snake_case__       :					List[Any]										=					kb_dataset.map(
						    lowerCamelCase      ,			batched=lowerCamelCase      ,			num_proc=1		)  # if you want you can load already splitted csv.
						snake_case__       :					Tuple										=					[kb_dataset.shard(lowerCamelCase      ,			lowerCamelCase      ,			contiguous=lowerCamelCase		) for i in range(lowerCamelCase		)]
						snake_case__       :					Union[str, Any]										=					kb_list[process_num]

						snake_case__       :					Dict										=					'''data_''' + str(lowerCamelCase		)
						snake_case__       :					Optional[int]										=					os.path.join(lowerCamelCase      ,			lowerCamelCase		)

						snake_case__       :					Tuple										=					DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''		)
						snake_case__       :					Tuple										=					ctx_encoder.to(device=lowerCamelCase		)

						def embed(
						    lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		) -> dict:
												snake_case__       :					Dict										=					ctx_tokenizer(
												    documents['''title''']      ,			documents['''text''']      ,			truncation=lowerCamelCase      ,			padding='''longest'''      ,			return_tensors='''pt'''		)['''input_ids''']
												snake_case__       :					Union[str, Any]										=					ctx_encoder(input_ids.to(device=lowerCamelCase		)      ,			return_dict=lowerCamelCase		).pooler_output
												return {"embeddings": embeddings.detach().cpu().numpy()}

						snake_case__       :					Tuple										=					Features(
						    {'''text''': Value('''string'''		), '''title''': Value('''string'''		), '''embeddings''': Sequence(Value('''float32'''		)		)}		)  # optional, save as float32 instead of float64 to save space

						snake_case__       :					str										=					data_shrad.map(
						    partial(lowerCamelCase      ,			ctx_encoder=lowerCamelCase      ,			ctx_tokenizer=lowerCamelCase      ,			device=lowerCamelCase		)      ,			batched=lowerCamelCase      ,			batch_size=16      ,			features=lowerCamelCase      ,			)
						dataset.save_to_disk(lowerCamelCase		)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					Tuple										=					[]

						for shard_address in glob(str(lowerCamelCase		) + '''/*/'''		):
												data_shard_list.append(load_from_disk(lowerCamelCase		)		)

						snake_case__       :					Dict										=					concatenate_datasets(lowerCamelCase		)
						faiss.omp_set_num_threads(96		)

						snake_case__       :					List[str]										=					faiss.IndexHNSWFlat(768      ,			128      ,			faiss.METRIC_INNER_PRODUCT		)
						concat.add_faiss_index('''embeddings'''      ,			custom_index=lowerCamelCase		)
						concat.get_index('''embeddings'''		).save(
						    lowerCamelCase		)  # since we load the index in to memory,we can directly update the index in the disk








"""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      ,			lowerCamelCase		):
						'''simple docstring'''


						print(f'''Downloading and extracting {task}...'''		)
						snake_case__       :					Optional[Any]										=					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      ,			lowerCamelCase		):
						'''simple docstring'''


						print('''Processing MRPC...'''		)
						snake_case__       :					Union[str, Any]										=					os.path.join(lowerCamelCase      ,			'''MRPC'''		)
						if not os.path.isdir(lowerCamelCase		):
												os.mkdir(lowerCamelCase		)
						if path_to_data:
												snake_case__       :					List[Any]										=					os.path.join(lowerCamelCase      ,			'''msr_paraphrase_train.txt'''		)
												snake_case__       :					Any										=					os.path.join(lowerCamelCase      ,			'''msr_paraphrase_test.txt'''		)
						else:
												print('''Local MRPC data not specified, downloading data from %s''' % MRPC_TRAIN		)
												snake_case__       :					Tuple										=					os.path.join(lowerCamelCase      ,			'''msr_paraphrase_train.txt'''		)
												snake_case__       :					Optional[int]										=					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'''		)		)

						snake_case__       :					Optional[Any]										=					[]
						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:
												snake_case__       :					Any										=					data_fh.readline()
												train_fh.write(lowerCamelCase		)
												dev_fh.write(lowerCamelCase		)
												for row in data_fh:
																		snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__       :					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(lowerCamelCase      ,			encoding='''utf8'''		) as data_fh, open(
						    os.path.join(lowerCamelCase      ,			'''test.tsv'''		)      ,			'''w'''      ,			encoding='''utf8'''		) as test_fh:
												snake_case__       :					Tuple										=					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		):
																		snake_case__			,	snake_case__			,	snake_case__			,	snake_case__			,	snake_case__       :					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 		lowerCAmelCase							( lowerCamelCase		):
						'''simple docstring'''


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

def 		lowerCAmelCase							( lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					Any										=					task_names.split(''','''		)
						if "all" in task_names:
												snake_case__       :					str										=					TASKS
						else:
												snake_case__       :					Union[str, Any]										=					[]
												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		):
						'''simple docstring'''


						snake_case__       :					int										=					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=''''''      ,			)
						snake_case__       :					Dict										=					parser.parse_args(lowerCamelCase		)

						if not os.path.isdir(args.data_dir		):
												os.mkdir(args.data_dir		)
						snake_case__       :					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(lowerCamelCase      ,			args.data_dir		)


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


