
'''simple docstring'''





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


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

A							     = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
A							     = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"
def 	snake_case_							(				_a  :				Optional[int]			,       _a  :				int      ):
      print(F'''Downloading and extracting {task}...'''      )
      snake_case_    							=				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 	snake_case_							(				_a  :				Optional[int]			,       _a  :				Dict      ):
      print("Processing MRPC..."      )
      snake_case_    							=				os.path.join(_a			,       "MRPC"      )
      if not os.path.isdir(_a      ):
            os.mkdir(_a      )
      if path_to_data:
            snake_case_    							=				os.path.join(_a			,       "msr_paraphrase_train.txt"      )
            snake_case_    							=				os.path.join(_a			,       "msr_paraphrase_test.txt"      )
      else:
            print("Local MRPC data not specified, downloading data from %s" % MRPC_TRAIN      )
            snake_case_    							=				os.path.join(_a			,       "msr_paraphrase_train.txt"      )
            snake_case_    							=				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"      )      )

      snake_case_    							=				[]
      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:
            snake_case_    							=				data_fh.readline()
            train_fh.write(_a      )
            dev_fh.write(_a      )
            for row in data_fh:
                  snake_case_							,						snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    							=				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:
            snake_case_    							=				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      ):
                  snake_case_							,						snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    							=				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 	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"      )      )
      snake_case_    							=				os.path.join(_a			,       "diagnostic"			,       "diagnostic.tsv"      )
      urllib.request.urlretrieve(TASKaPATH["diagnostic"]			,       _a      )
      print("\tCompleted!"      )
      return
def 	snake_case_							(				_a  :				Optional[int]      ):
      snake_case_    							=				task_names.split(","      )
      if "all" in task_names:
            snake_case_    							=				TASKS
      else:
            snake_case_    							=				[]
            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 	snake_case_							(				_a  :				Union[str, Any]      ):
      snake_case_    							=				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=""			,       )
      snake_case_    							=				parser.parse_args(_a      )

      if not os.path.isdir(args.data_dir      ):
            os.mkdir(args.data_dir      )
      snake_case_    							=				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"""







from typing import Dict, Optional, Tuple

import torch




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor						)    ->     torch.Tensor:







     '''simple docstring'''


     __A					:					List[str]								=		boundaries[1] - boundaries[0]
     __A					:					int								=		boundaries + step / 2
     __A					:					Union[str, Any]								=		torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1						)]  ,       dim=0						)
     return bin_centers




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       )    ->     Tuple[torch.Tensor, torch.Tensor]:







     '''simple docstring'''


     __A					:					str								=		_calculate_bin_centers(_SCREAMING_SNAKE_CASE						)
     return (
         torch.sum(aligned_distance_error_probs * bin_centers  ,       dim=-1						),
         bin_centers[-1],
     )




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				int = 31  ,       _SCREAMING_SNAKE_CASE				:				int = 64  ,       **_SCREAMING_SNAKE_CASE				:				Optional[Any]  ,       )    ->     Dict[str, torch.Tensor]:







     '''simple docstring'''


     __A					:					int								=		torch.linspace(0  ,       _SCREAMING_SNAKE_CASE  ,       steps=(no_bins - 1)  ,       device=logits.device						)

     __A					:					Any								=		torch.nn.functional.softmax(_SCREAMING_SNAKE_CASE  ,       dim=-1						)
     __A						,		__A					:					Tuple								=		_calculate_expected_aligned_error(
         alignment_confidence_breaks=_SCREAMING_SNAKE_CASE  ,       aligned_distance_error_probs=_SCREAMING_SNAKE_CASE  ,       )

     return {
         "aligned_confidence_probs": aligned_confidence_probs,
         "predicted_aligned_error": predicted_aligned_error,
         "max_predicted_aligned_error": max_predicted_aligned_error,
     }




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				Optional[torch.Tensor] = None  ,       _SCREAMING_SNAKE_CASE				:				int = 31  ,       _SCREAMING_SNAKE_CASE				:				int = 64  ,       _SCREAMING_SNAKE_CASE				:				float = 1E-8  ,       **_SCREAMING_SNAKE_CASE				:				Union[str, Any]  ,       )    ->     torch.Tensor:







     '''simple docstring'''


     if residue_weights is None:
          __A					:					Optional[int]								=		logits.new_ones(logits.shape[-2]						)

     __A					:					List[str]								=		torch.linspace(0  ,       _SCREAMING_SNAKE_CASE  ,       steps=(no_bins - 1)  ,       device=logits.device						)

     __A					:					Tuple								=		_calculate_bin_centers(_SCREAMING_SNAKE_CASE						)
     torch.sum(_SCREAMING_SNAKE_CASE						)
     __A					:					int								=		logits.shape[-2]
     __A					:					str								=		max(_SCREAMING_SNAKE_CASE  ,       19						)

     __A					:					Tuple								=		1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

     __A					:					Tuple								=		torch.nn.functional.softmax(_SCREAMING_SNAKE_CASE  ,       dim=-1						)

     __A					:					List[str]								=		1.0 / (1 + (bin_centers**2) / (da**2))
     __A					:					int								=		torch.sum(probs * tm_per_bin  ,       dim=-1						)

     __A					:					Any								=		residue_weights / (eps + residue_weights.sum())
     __A					:					List[Any]								=		torch.sum(predicted_tm_term * normed_residue_mask  ,       dim=-1						)

     __A					:					int								=		per_alignment * residue_weights

     __A					:					Optional[int]								=		(weighted == torch.max(_SCREAMING_SNAKE_CASE						)).nonzero()[0]
     return per_alignment[tuple(_SCREAMING_SNAKE_CASE						)]



