code: import argparse
  import os
  import sys
  import urllib.request
  import zipfile
  
  
  _SCREAMING_SNAKE_CASE   						=    ["""CoLA""", """SST""", """MRPC""", """QQP""", """STS""", """MNLI""", """SNLI""", """QNLI""", """RTE""", """WNLI""", """diagnostic"""]
  _SCREAMING_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""",
  }
  
  _SCREAMING_SNAKE_CASE   						=    """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"""
  _SCREAMING_SNAKE_CASE   						=    """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"""
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->List[str]:
  
    '''simple docstring'''
  
  
  
  
    print(f"Downloading and extracting {task}..."					)
    a__     		=     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 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->Dict:
  
    '''simple docstring'''
  
  
  
  
    print("Processing MRPC..."					)
    a__     		=     os.path.join(lowerCamelCase__ ,				"MRPC"					)
    if not os.path.isdir(lowerCamelCase__					):
      os.mkdir(lowerCamelCase__					)
    if path_to_data:
      a__     		=     os.path.join(lowerCamelCase__ ,				"msr_paraphrase_train.txt"					)
      a__     		=     os.path.join(lowerCamelCase__ ,				"msr_paraphrase_test.txt"					)
    else:
      print("Local MRPC data not specified, downloading data from %s" % MRPC_TRAIN					)
      a__     		=     os.path.join(lowerCamelCase__ ,				"msr_paraphrase_train.txt"					)
      a__     		=     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"					)					)
  
    a__     		=     []
    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:
      a__     		=     data_fh.readline()
      train_fh.write(lowerCamelCase__					)
      dev_fh.write(lowerCamelCase__					)
      for row in data_fh:
        a__		,a__		,a__		,a__		,a__     		=     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:
      a__     		=     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__					):
        a__		,a__		,a__		,a__		,a__     		=     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 				__lowercase	(						lowerCamelCase__					)				->List[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"					)					)
    a__     		=     os.path.join(lowerCamelCase__ ,				"diagnostic" ,				"diagnostic.tsv"					)
    urllib.request.urlretrieve(TASKaPATH["diagnostic"] ,				lowerCamelCase__					)
    print("\tCompleted!"					)
    return
  def 				__lowercase	(						lowerCamelCase__					)				->Dict:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     task_names.split(","					)
    if "all" in task_names:
      a__     		=     TASKS
    else:
      a__     		=     []
      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 				__lowercase	(						lowerCamelCase__					)				->int:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     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="" ,				)
    a__     		=     parser.parse_args(lowerCamelCase__					)
  
    if not os.path.isdir(args.data_dir					):
      os.mkdir(args.data_dir					)
    a__     		=     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:]))
  
  
style_context: import random
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				:
  
  
  
  
      def __init__(					self  :   Optional[int]      ,      lowerCAmelCase  :   list[list[float]]      ,      lowerCAmelCase  :   list[int]      ,      lowerCAmelCase  :   float = 0.01      ,      lowerCAmelCase  :   int = 1000      ,      lowerCAmelCase  :   float = -1      ,      )	-> None:
        a__     		=     sample
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        a__     		=     target
        if len(self.target   ) == 0:
          raise ValueError("Target data can not be empty"   )
        if len(self.sample   ) != len(self.target   ):
          raise ValueError("Sample data and Target data do not have matching lengths"   )
        a__     		=     learning_rate
        a__     		=     epoch_number
        a__     		=     bias
        a__     		=     len(lowerCAmelCase   )
        a__     		=     len(sample[0]   )  # number of columns in dataset
        a__     		=     []
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> None:
        for sample in self.sample:
          sample.insert(0      ,      self.bias   )
  
        for _ in range(self.col_sample   ):
          self.weight.append(random.random()   )
  
        self.weight.insert(0      ,      self.bias   )
  
        a__     		=     0
  
        while True:
          a__     		=     False
          for i in range(self.number_sample   ):
            a__     		=     0
            for j in range(self.col_sample + 1   ):
              a__     		=     u + self.weight[j] * self.sample[i][j]
            a__     		=     self.sign(lowerCAmelCase   )
            if y != self.target[i]:
              for j in range(self.col_sample + 1   ):
                a__     		=     (
                    self.weight[j]
                    + self.learning_rate
                    * (self.target[i] - y)
                    * self.sample[i][j]
                )
              a__     		=     True
              # print('Epoch: \n',epoch_count)
          a__     		=     epoch_count + 1
          # if you want control the epoch or just by error
          if not has_misclassified:
            print(("\nEpoch:\n", epoch_count)   )
            print("------------------------\n"   )
            # if epoch_count > self.epoch_number or not error:
            break
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]      ,      lowerCAmelCase  :   list[float]   )	-> None:
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        sample.insert(0      ,      self.bias   )
        a__     		=     0
        for i in range(self.col_sample + 1   ):
          a__     		=     u + self.weight[i] * sample[i]
  
        a__     		=     self.sign(lowerCAmelCase   )
  
        if y == -1:
          print(("Sample: ", sample)   )
          print("classification: P1"   )
        else:
          print(("Sample: ", sample)   )
          print("classification: P2"   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   int      ,      lowerCAmelCase  :   float   )	-> int:
        return 1 if u >= 0 else -1
  
  _SCREAMING_SNAKE_CASE   						=    [
      [-0.6508, 0.1097, 4.0009],
      [-1.4492, 0.8896, 4.4005],
      [2.0850, 0.6876, 12.0710],
      [0.2626, 1.1476, 7.7985],
      [0.6418, 1.0234, 7.0427],
      [0.2569, 0.6730, 8.3265],
      [1.1155, 0.6043, 7.4446],
      [0.0914, 0.3399, 7.0677],
      [0.0121, 0.5256, 4.6316],
      [-0.0429, 0.4660, 5.4323],
      [0.4340, 0.6870, 8.2287],
      [0.2735, 1.0287, 7.1934],
      [0.4839, 0.4851, 7.4850],
      [0.4089, -0.1267, 5.5019],
      [1.4391, 0.1614, 8.5843],
      [-0.9115, -0.1973, 2.1962],
      [0.3654, 1.0475, 7.4858],
      [0.2144, 0.7515, 7.1699],
      [0.2013, 1.0014, 6.5489],
      [0.6483, 0.2183, 5.8991],
      [-0.1147, 0.2242, 7.2435],
      [-0.7970, 0.8795, 3.8762],
      [-1.0625, 0.6366, 2.4707],
      [0.5307, 0.1285, 5.6883],
      [-1.2200, 0.7777, 1.7252],
      [0.3957, 0.1076, 5.6623],
      [-0.1013, 0.5989, 7.1812],
      [2.4482, 0.9455, 11.2095],
      [2.0149, 0.6192, 10.9263],
      [0.2012, 0.2611, 5.4631],
  ]
  
  _SCREAMING_SNAKE_CASE   						=    [
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
       _SCREAMING_SNAKE_CASE   						=    Perceptron(
           sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
       )
       network.training()
       print("""Finished training perceptron""")
       print("""Enter values to predict or q to exit""")
       while True:
            _SCREAMING_SNAKE_CASE   						=    []
            for i in range(len(samples[0])):
                 _SCREAMING_SNAKE_CASE   						=    input("""value: """).strip()
                 if user_input == "q":
                      break
                 _SCREAMING_SNAKE_CASE   						=    float(user_input)
                 sample.insert(i, observation)
            network.sort(sample)
  
  