





import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
_snake_case				= False
_snake_case				= Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_snake_case				= datasets.Features(
    OrderedDict(
        {
            """attr_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """attr_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """boxes""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="""float32"""),
            """img_id""": datasets.Value("""int32"""),
            """obj_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """obj_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """roi_features""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)





class 					lowerCAmelCase							:







  def __init__(			self  :Tuple    ,							_lowercase  :int=sys.argv[1:]					):






     '''simple docstring'''





     lowercase__    =  None
     lowercase__    =  None
     lowercase__    =  None
     lowercase__    =  1
     lowercase__   ,							lowercase__    =  getopt.getopt(_lowercase    ,							"i:o:b:s"    ,							["inputdir=", "outfile=", "batch_size=", "subset_list="]					)
     for opt, arg in opts:
        if opt in ("-i", "--inputdir"):
           lowercase__    =  arg
        elif opt in ("-o", "--outfile"):
           lowercase__    =  arg
        elif opt in ("-b", "--batch_size"):
           lowercase__    =  int(_lowercase					)
        elif opt in ("-s", "--subset_list"):
           lowercase__    =  arg

     assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
     assert outputfile is not None and not os.path.isfile(_lowercase					), f'''{outputfile}'''
     if subset_list is not None:
        with open(os.path.realpath(_lowercase					)					) as f:
           lowercase__    =  {self._vqa_file_split()[0] for x in tryload(_lowercase					)}
     else:
        lowercase__    =  None

     lowercase__    =  CONFIG
     if torch.cuda.is_available():
        lowercase__    =  "cuda"
     lowercase__    =  os.path.realpath(_lowercase					)
     lowercase__    =  os.path.realpath(_lowercase					)
     lowercase__    =  Preprocess(self.config					)
     lowercase__    =  GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"    ,							config=self.config					)
     lowercase__    =  batch_size if batch_size != 0 else 1
     lowercase__    =  DEFAULT_SCHEMA







  def   UpperCAmelCase     (			self  :str    ,							_lowercase  :Optional[int]					):






     '''simple docstring'''





     lowercase__    =  int(file.split("."					)[0].split("_"					)[-1]					)
     lowercase__    =  os.path.join(self.inputdir    ,							_lowercase					)
     return (img_id, filepath)







  @property
  def   UpperCAmelCase     (			self  :Dict					):






     '''simple docstring'''





     lowercase__    =  []
     for i, file in enumerate(os.listdir(self.inputdir					)					):
        if self.subset_list is not None and i not in self.subset_list:
           continue
        batch.append(self._vqa_file_split(_lowercase					)					)
        if len(_lowercase					) == self.batch:
           lowercase__    =  batch
           lowercase__    =  []
           yield list(map(_lowercase    ,							zip(*_lowercase					)					)					)

     for i in range(1					):
        yield list(map(_lowercase    ,							zip(*_lowercase					)					)					)







  def __call__(			self  :int					):






     '''simple docstring'''





     if not TEST:
        lowercase__    =  datasets.ArrowWriter(features=self.schema    ,							path=self.outputfile					)
     # do file generator
     for i, (img_ids, filepaths) in enumerate(self.file_generator					):
        lowercase__   ,							lowercase__   ,							lowercase__    =  self.preprocess(_lowercase					)
        lowercase__    =  self.model(
            _lowercase    ,							_lowercase    ,							scales_yx=_lowercase    ,							padding="max_detections"    ,							max_detections=self.config.MAX_DETECTIONS    ,							pad_value=0    ,							return_tensors="np"    ,							location="cpu"    ,							)
        lowercase__    =  output_dict.pop("normalized_boxes"					)
        if not TEST:
           lowercase__    =  np.array(_lowercase					)
           lowercase__    =  self.schema.encode_batch(_lowercase					)
           writer.write_batch(_lowercase					)
        if TEST:
           break
        # finalizer the writer
     if not TEST:
        lowercase__   ,							lowercase__    =  writer.finalize()
        print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''					)


def _A   (    __magic_name__       ):
   try:
      lowercase__    =  json.load(__magic_name__       )
      try:
         lowercase__    =  list(data.keys()       )
      except Exception:
         lowercase__    =  [d["img_id"] for d in data]
   except Exception:
      try:
         lowercase__    =  eval(stream.read()       )
      except Exception:
         lowercase__    =  stream.read().split("\n"       )
   return data


if __name__ == "__main__":
  _snake_case				= Extract(sys.argv[1:])
  extract()
  if not TEST:
    _snake_case				= datasets.Dataset.from_file(extract.outputfile)
    # wala!
    # print(np.array(dataset[0:2]["roi_features"]).shape)













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


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

_snake_case				= """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"""
_snake_case				= """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"""


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


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

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

   with open(__magic_name__  ,					encoding="utf8"       ) as data_fh, open(
       os.path.join(__magic_name__  ,					"train.tsv"       )  ,					"w"  ,					encoding="utf8"       ) as train_fh, open(os.path.join(__magic_name__  ,					"dev.tsv"       )  ,					"w"  ,					encoding="utf8"       ) as dev_fh:
      lowercase__    =  data_fh.readline()
      train_fh.write(__magic_name__       )
      dev_fh.write(__magic_name__       )
      for row in data_fh:
         lowercase__   ,							lowercase__   ,							lowercase__   ,							lowercase__   ,							lowercase__    =  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(__magic_name__  ,					encoding="utf8"       ) as data_fh, open(
       os.path.join(__magic_name__  ,					"test.tsv"       )  ,					"w"  ,					encoding="utf8"       ) as test_fh:
      lowercase__    =  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(__magic_name__       ):
         lowercase__   ,							lowercase__   ,							lowercase__   ,							lowercase__   ,							lowercase__    =  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   (    __magic_name__       ):
   print("Downloading and extracting diagnostic..."       )
   if not os.path.isdir(os.path.join(__magic_name__  ,					"diagnostic"       )       ):
      os.mkdir(os.path.join(__magic_name__  ,					"diagnostic"       )       )
   lowercase__    =  os.path.join(__magic_name__  ,					"diagnostic"  ,					"diagnostic.tsv"       )
   urllib.request.urlretrieve(TASKaPATH["diagnostic"]  ,					__magic_name__       )
   print("\tCompleted!"       )
   return


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


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

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


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






