




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						:    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						:    str      =  "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
_UpperCAmelCase						:    int      =  "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"



def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase ):
  print(F"Downloading and extracting {task}..." )
  lowercase     :int   						=    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    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase ):
  print("Processing MRPC..." )
  lowercase     :List[str]   						=    os.path.join(lowerCamelCase,      "MRPC" )
  if not os.path.isdir(lowerCamelCase ):
    os.mkdir(lowerCamelCase )
  if path_to_data:
    lowercase     :Optional[Any]   						=    os.path.join(lowerCamelCase,      "msr_paraphrase_train.txt" )
    lowercase     :Optional[int]   						=    os.path.join(lowerCamelCase,      "msr_paraphrase_test.txt" )
  else:
    print("Local MRPC data not specified, downloading data from %s" % MRPC_TRAIN )
    lowercase     :int   						=    os.path.join(lowerCamelCase,      "msr_paraphrase_train.txt" )
    lowercase     :Any   						=    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" ) )

  lowercase     :Union[str, 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:
    lowercase     :List[Any]   						=    data_fh.readline()
    train_fh.write(lowerCamelCase )
    dev_fh.write(lowerCamelCase )
    for row in data_fh:
      lowercase						,     lowercase						,     lowercase						,     lowercase						,     lowercase     :Union[str, Any]   						=    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:
    lowercase     :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(lowerCamelCase ):
      lowercase						,     lowercase						,     lowercase						,     lowercase						,     lowercase     :Optional[int]   						=    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__	(  lowerCamelCase ):
  print("Downloading and extracting diagnostic..." )
  if not os.path.isdir(os.path.join(lowerCamelCase,      "diagnostic" ) ):
    os.mkdir(os.path.join(lowerCamelCase,      "diagnostic" ) )
  lowercase     :str   						=    os.path.join(lowerCamelCase,      "diagnostic",      "diagnostic.tsv" )
  urllib.request.urlretrieve(TASKaPATH["diagnostic"],      lowerCamelCase )
  print("\tCompleted!" )
  return



def    UpperCAmelCase__	(  lowerCamelCase ):
  lowercase     :List[str]   						=    task_names.split("," )
  if "all" in task_names:
    lowercase     :Tuple   						=    TASKS
  else:
    lowercase     :List[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    UpperCAmelCase__	(  lowerCamelCase ):
  lowercase     :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="",      )
  lowercase     :List[str]   						=    parser.parse_args(lowerCamelCase )

  if not os.path.isdir(args.data_dir ):
    os.mkdir(args.data_dir )
  lowercase     :str   						=    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:]))









import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json



def    UpperCAmelCase__	(  lowerCamelCase ):
  lowercase     :Optional[Any]   						=    filter(lambda lowerCamelCase : p.requires_grad,      model.parameters() )
  lowercase     :Optional[int]   						=    sum([np.prod(p.size() ) for p in model_parameters] )
  return params


_UpperCAmelCase						:    Optional[Any]      =  logging.getLogger(__name__)




class  __lowerCAmelCase							(				pl.Callback):






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       List[Any]							,       _lowerCAmelCase:       Union[str, Any]					):
         lowercase     :Dict   						=    {F"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
         pl_module.logger.log_metrics(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule							,       _lowerCAmelCase:       str							,       _lowerCAmelCase:       List[str]=True					):
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"					)
         lowercase     :Tuple   						=    trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}					)
         # Log results
         lowercase     :Optional[int]   						=    Path(pl_module.hparams.output_dir					)
         if type_path == "test":
           lowercase     :List[Any]   						=    od / "test_results.txt"
           lowercase     :Optional[int]   						=    od / "test_generations.txt"
         else:
           # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
           # If people want this it will be easy enough to add back.
           lowercase     :int   						=    od / F"{type_path}_results/{trainer.global_step:05d}.txt"
           lowercase     :Tuple   						=    od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
           results_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
           generations_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
         with open(_lowerCAmelCase							,       "a+"					) as writer:
           for key in sorted(_lowerCAmelCase					):
             if key in ["log", "progress_bar", "preds"]:
               continue
             lowercase     :int   						=    metrics[key]
             if isinstance(_lowerCAmelCase							,       torch.Tensor					):
               lowercase     :int   						=    val.item()
             lowercase     :Optional[Any]   						=    F"{key}: {val:.6f}\n"
             writer.write(_lowerCAmelCase					)

         if not save_generations:
           return

         if "preds" in metrics:
           lowercase     :str   						=    "\n".join(metrics["preds"]					)
           generations_file.open("w+"					).write(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]							,       _lowerCAmelCase:       Tuple							,       _lowerCAmelCase:       Tuple					):
         try:
           lowercase     :Optional[int]   						=    pl_module.model.model.num_parameters()
         except AttributeError:
           lowercase     :Dict   						=    pl_module.model.num_parameters()

         lowercase     :Any   						=    count_trainable_parameters(_lowerCAmelCase					)
         # mp stands for million parameters
         trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule					):
         save_json(pl_module.metrics							,       pl_module.metrics_save_path					)
         return self._write_logs(_lowerCAmelCase							,       _lowerCAmelCase							,       "test"					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       List[str]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       Dict					):
         save_json(pl_module.metrics							,       pl_module.metrics_save_path					)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase,      lowerCamelCase=1,      lowerCamelCase=False ):
  if metric == "rouge2":
    lowercase     :str   						=    "{val_avg_rouge2:.4f}-{step_count}"
  elif metric == "bleu":
    lowercase     :int   						=    "{val_avg_bleu:.4f}-{step_count}"
  elif metric == "loss":
    lowercase     :List[str]   						=    "{val_avg_loss:.4f}-{step_count}"
  else:
    raise NotImplementedError(
        F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
        " this function." )

  lowercase     :int   						=    ModelCheckpoint(
      dirpath=lowerCamelCase,      filename=lowerCamelCase,      monitor=F"val_{metric}",      mode="min" if "loss" in metric else "max",      save_top_k=lowerCamelCase,      )
  return checkpoint_callback







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase ):
  return EarlyStopping(
      monitor=F"val_{metric}",      mode="min" if "loss" in metric else "max",      patience=lowerCamelCase,      verbose=lowerCamelCase,      )



