


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


__UpperCamelCase							:							int    							=					["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"]
__UpperCamelCase							:							Optional[int]    							=					{
    "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							:							int    							=					"https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
__UpperCamelCase							:							Optional[int]    							=					"https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"

def       _a	(					SCREAMING_SNAKE_CASE						:					Optional[int]     ,			SCREAMING_SNAKE_CASE						:					int  ):







 """simple docstring"""



 print(F"Downloading and extracting {task}..."  )
 UpperCamelCase__       :     List[Any] 							= 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       _a	(					SCREAMING_SNAKE_CASE						:					Optional[int]     ,			SCREAMING_SNAKE_CASE						:					Dict  ):







 """simple docstring"""



 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__       :     Any 							= os.path.join(_a     ,			'''msr_paraphrase_train.txt'''  )
  UpperCamelCase__       :     Optional[int] 							= os.path.join(_a     ,			'''msr_paraphrase_test.txt'''  )
 else:
  print('''Local MRPC data not specified, downloading data from %s''' % MRPC_TRAIN  )
  UpperCamelCase__       :     int 							= os.path.join(_a     ,			'''msr_paraphrase_train.txt'''  )
  UpperCamelCase__       :     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__       :     Optional[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__       :     List[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__       :     List[str] 							= 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       _a	(					SCREAMING_SNAKE_CASE						:					List[str]  ):







 """simple docstring"""



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

def       _a	(					SCREAMING_SNAKE_CASE						:					Optional[int]  ):







 """simple docstring"""



 UpperCamelCase__       :     Any 							= task_names.split(''','''  )
 if "all" in task_names:
  UpperCamelCase__       :     str 							= TASKS
 else:
  UpperCamelCase__       :     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       _a	(					SCREAMING_SNAKE_CASE						:					Union[str, Any]  ):







 """simple docstring"""



 UpperCamelCase__       :     Optional[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=''''''     ,			)
 UpperCamelCase__       :     Optional[int] 							= parser.parse_args(_a  )

 if not os.path.isdir(args.data_dir  ):
  os.mkdir(args.data_dir  )
 UpperCamelCase__       :     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:]))




'''simple docstring'''





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def 	snake_case_							(				):
      snake_case_    							=				Dataset.from_dict({"i": range(17      )}      )
      snake_case_    							=				len(_a      )
      snake_case_    							=				3
      snake_case_    							=				[
          split_dataset_by_node(_a			,       rank=_a			,       world_size=_a      ) for rank in range(_a      )
      ]
      assert sum(len(_a      ) for ds in datasets_per_rank      ) == full_size
      assert len({tuple(x.values()      ) for ds in datasets_per_rank for x in ds}      ) == full_size
def 	snake_case_							(				):
      def gen():
            return ({"i": i} for i in range(17      ))

      snake_case_    							=				3
      snake_case_    							=				IterableDataset.from_generator(_a      )
      snake_case_    							=				len(list(_a      )      )
      snake_case_    							=				[
          split_dataset_by_node(_a			,       rank=_a			,       world_size=_a      ) for rank in range(_a      )
      ]
      assert sum(len(list(_a      )      ) for ds in datasets_per_rank      ) == full_size
      assert len({tuple(x.values()      ) for ds in datasets_per_rank for x in ds}      ) == full_size
@pytest.mark.parametrize("shards_per_node"			,       [1, 2, 3]      )
def 	snake_case_							(				_a  :				List[Any]      ):
      def gen(_a  :				Dict      ):
            for shard in shards:
                  yield from ({"i": i, "shard": shard} for i in range(17      ))

      snake_case_    							=				3
      snake_case_    							=				shards_per_node * world_size
      snake_case_    							=				{"shards": [F'''shard_{shard_idx}.txt''' for shard_idx in range(_a      )]}
      snake_case_    							=				IterableDataset.from_generator(_a			,       gen_kwargs=_a      )
      snake_case_    							=				len(list(_a      )      )
      assert full_ds.n_shards == world_size * shards_per_node
      snake_case_    							=				[
          split_dataset_by_node(_a			,       rank=_a			,       world_size=_a      ) for rank in range(_a      )
      ]
      assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
      assert sum(len(list(_a      )      ) for ds in datasets_per_rank      ) == full_size
      assert len({tuple(x.values()      ) for ds in datasets_per_rank for x in ds}      ) == full_size
def 	snake_case_							(				):
      def gen():
            return ({"i": i} for i in range(17      ))

      snake_case_    							=				2
      snake_case_    							=				IterableDataset.from_generator(_a      )
      snake_case_    							=				len(list(_a      )      )

      snake_case_    							=				split_dataset_by_node(_a			,       rank=0			,       world_size=_a      ).shuffle(seed=42      )
      assert len(list(_a      )      ) == 1 + full_size // world_size
      with pytest.raises(_a      ):
            split_dataset_by_node(_a			,       rank=0			,       world_size=_a      ).shuffle()

      snake_case_    							=				split_dataset_by_node(full_ds.shuffle(seed=42      )			,       rank=0			,       world_size=_a      )
      assert len(list(_a      )      ) == 1 + full_size // world_size
      with pytest.raises(_a      ):
            split_dataset_by_node(full_ds.shuffle()			,       rank=0			,       world_size=_a      )
@pytest.mark.parametrize("streaming"			,       [False, True]      )
@require_torch
@pytest.mark.skipif(os.name == "nt"			,       reason="execute_subprocess_async doesn't support windows"      )
@pytest.mark.integration
def 	snake_case_							(				_a  :				Any      ):
      snake_case_    							=				2
      snake_case_    							=				get_torch_dist_unique_port()
      snake_case_    							=				Path(_a      ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
      snake_case_    							=				F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
      snake_case_    							=				F'''
        --streaming={streaming}
    '''.split()
      snake_case_    							=				[sys.executable] + distributed_args + args
      execute_subprocess_async(_a			,       env=os.environ.copy()      )




@pytest.mark.parametrize(
    "nproc_per_node, num_workers"			,       [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]			,       )
@require_torch
@pytest.mark.skipif(os.name == "nt"			,       reason="execute_subprocess_async doesn't support windows"      )
@pytest.mark.integration
def 	snake_case_							(				_a  :				int			,       _a  :				List[Any]      ):
      snake_case_    							=				True
      snake_case_    							=				get_torch_dist_unique_port()
      snake_case_    							=				Path(_a      ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
      snake_case_    							=				F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
      snake_case_    							=				F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
      snake_case_    							=				[sys.executable] + distributed_args + args
      execute_subprocess_async(_a			,       env=os.environ.copy()      )

