




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


__SCREAMING_SNAKE_CASE    :   List[str]       =["""CoLA""", """SST""", """MRPC""", """QQP""", """STS""", """MNLI""", """SNLI""", """QNLI""", """RTE""", """WNLI""", """diagnostic"""]
__SCREAMING_SNAKE_CASE    :   Tuple       ={
    """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""",
}

__SCREAMING_SNAKE_CASE    :   Tuple       ="""https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"""
__SCREAMING_SNAKE_CASE    :   str       ="""https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"""

def 			lowerCAmelCase_			(  lowerCamelCase:       Dict     ,				lowerCamelCase:       Any						)					->			str:


      '''simple docstring'''




      print(f"""Downloading and extracting {task}..."""						)
      lowerCamelCase__        =				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:       str     ,				lowerCamelCase:       Tuple						)					->			int:


      '''simple docstring'''




      print('Processing MRPC...'						)
      lowerCamelCase__        =				os.path.join(lowerCamelCase     ,				'MRPC'						)
      if not os.path.isdir(lowerCamelCase						):
            os.mkdir(lowerCamelCase						)
      if path_to_data:
            lowerCamelCase__        =				os.path.join(lowerCamelCase     ,				'msr_paraphrase_train.txt'						)
            lowerCamelCase__        =				os.path.join(lowerCamelCase     ,				'msr_paraphrase_test.txt'						)
      else:
            print('Local MRPC data not specified, downloading data from %s' % MRPC_TRAIN						)
            lowerCamelCase__        =				os.path.join(lowerCamelCase     ,				'msr_paraphrase_train.txt'						)
            lowerCamelCase__        =				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'						)						)

      lowerCamelCase__        =				[]
      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:
            lowerCamelCase__        =				data_fh.readline()
            train_fh.write(lowerCamelCase						)
            dev_fh.write(lowerCamelCase						)
            for row in data_fh:
                  lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				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:
            lowerCamelCase__        =				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						):
                  lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				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:       Dict						)					->			Any:


      '''simple docstring'''




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

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[int]						)					->			List[Any]:


      '''simple docstring'''




      lowerCamelCase__        =				task_names.split(','						)
      if "all" in task_names:
            lowerCamelCase__        =				TASKS
      else:
            lowerCamelCase__        =				[]
            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:       Tuple						)					->			Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__        =				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=''     ,				)
      lowerCamelCase__        =				parser.parse_args(lowerCamelCase						)

      if not os.path.isdir(args.data_dir						):
            os.mkdir(args.data_dir						)
      lowerCamelCase__        =				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 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
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =False
__SCREAMING_SNAKE_CASE    :   int       =Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =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, 2_048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)






class 			A			:
     """simple docstring"""

     def __init__(    self  :   List[str]	,  A__  :   int=sys.argv[1:]			)				->       Optional[Any]:
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				1
           lowerCamelCase__  ,		lowerCamelCase__        =				getopt.getopt(A__	,  'i:o:b:s'	,  ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']			)
           for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                       lowerCamelCase__        =				arg
                 elif opt in ("-o", "--outfile"):
                       lowerCamelCase__        =				arg
                 elif opt in ("-b", "--batch_size"):
                       lowerCamelCase__        =				int(A__			)
                 elif opt in ("-s", "--subset_list"):
                       lowerCamelCase__        =				arg

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

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




     def  __snake_case   (    self  :   Dict	,  A__  :   Any			)				->       Union[str, Any]:
           lowerCamelCase__        =				int(file.split('.'			)[0].split('_'			)[-1]			)
           lowerCamelCase__        =				os.path.join(self.inputdir	,  A__			)
           return (img_id, filepath)




     @property
     def  __snake_case   (    self  :   int			)				->       Optional[Any]:
           lowerCamelCase__        =				[]
           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(A__			)			)
                 if len(A__			) == self.batch:
                       lowerCamelCase__        =				batch
                       lowerCamelCase__        =				[]
                       yield list(map(A__	,  zip(*A__			)			)			)

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




     def __call__(    self  :   List[Any]			)				->       Optional[Any]:
           # make writer
           if not TEST:
                 lowerCamelCase__        =				datasets.ArrowWriter(features=self.schema	,  path=self.outputfile			)
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator			):
                 lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				self.preprocess(A__			)
                 lowerCamelCase__        =				self.model(
                     A__	,  A__	,  scales_yx=A__	,  padding='max_detections'	,  max_detections=self.config.MAX_DETECTIONS	,  pad_value=0	,  return_tensors='np'	,  location='cpu'	,  )
                 lowerCamelCase__        =				output_dict.pop('normalized_boxes'			)
                 if not TEST:
                       lowerCamelCase__        =				np.array(A__			)
                       lowerCamelCase__        =				self.schema.encode_batch(A__			)
                       writer.write_batch(A__			)
                 if TEST:
                       break
                 # finalizer the writer
           if not TEST:
                 lowerCamelCase__  ,		lowerCamelCase__        =				writer.finalize()
                 print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""			)

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple						)					->			Optional[Any]:


      '''simple docstring'''




      try:
            lowerCamelCase__        =				json.load(lowerCamelCase						)
            try:
                  lowerCamelCase__        =				list(data.keys()						)
            except Exception:
                  lowerCamelCase__        =				[d['img_id'] for d in data]
      except Exception:
            try:
                  lowerCamelCase__        =				eval(stream.read()						)
            except Exception:
                  lowerCamelCase__        =				stream.read().split('\n'						)
      return data


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