


"""simple docstring"""


import numpy

# List of input, output pairs
_snake_case          =							(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_snake_case          =							(((515, 22, 13), 555), ((61, 35, 49), 150))
_snake_case          =							[2, 4, 1, 5]
_snake_case          =							len(train_data)
_snake_case          =							0.0_09



def   snake_case						(				_a:     Dict		,   _a:     str="train"							)->		int:
      '''simple docstring'''


      return calculate_hypothesis_value(_a		,   _a							) - output(
          _a		,   _a							)



def   snake_case						(				_a:     str							)->		Union[str, Any]:
      '''simple docstring'''


      lowerCamelCase__           =		0
      for i in range(len(_a							) - 1							):
            hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
      hyp_val += parameter_vector[0]
      return hyp_val



def   snake_case						(				_a:     str		,   _a:     Union[str, Any]							)->		Any:
      '''simple docstring'''


      if data_set == "train":
            return train_data[example_no][1]
      elif data_set == "test":
            return test_data[example_no][1]
      return None



def   snake_case						(				_a:     Any		,   _a:     Optional[int]							)->		Optional[Any]:
      '''simple docstring'''


      if data_set == "train":
            return _hypothesis_value(train_data[example_no][0]							)
      elif data_set == "test":
            return _hypothesis_value(test_data[example_no][0]							)
      return None



def   snake_case						(				_a:     int		,   _a:     str=m							)->		Any:
      '''simple docstring'''


      lowerCamelCase__           =		0
      for i in range(_a							):
            if index == -1:
                  summation_value += _error(_a							)
            else:
                  summation_value += _error(_a							) * train_data[i][0][index]
      return summation_value



def   snake_case						(				_a:     str							)->		Dict:
      '''simple docstring'''


      lowerCamelCase__           =		summation_of_cost_derivative(_a		,   _a							) / m
      return cost_derivative_value



def   snake_case						(				)->		Dict:
      '''simple docstring'''


      global parameter_vector
      # Tune these values to set a tolerance value for predicted output
      lowerCamelCase__           =		0.00_0002
      lowerCamelCase__           =		0
      lowerCamelCase__           =		0
      while True:
            j += 1
            lowerCamelCase__           =		[0, 0, 0, 0]
            for i in range(0		,   len(_a							)							):
                  lowerCamelCase__           =		get_cost_derivative(i - 1							)
                  lowerCamelCase__           =		(
                      parameter_vector[i] - LEARNING_RATE * cost_derivative
                  )
            if numpy.allclose(
                _a		,   _a		,   atol=_a		,   rtol=_a		,   ):
                  break
            lowerCamelCase__           =		temp_parameter_vector
      print(('Number of iterations:', j)							)



def   snake_case						(				)->		Optional[Any]:
      '''simple docstring'''


      for i in range(len(_a							)							):
            print(('Actual output value:', output(_a		,   'test'							))							)
            print(('Hypothesis output:', calculate_hypothesis_value(_a		,   'test'							))							)


if __name__ == "__main__":
   run_gradient_descent()
   print("\nTesting gradient descent for a linear hypothesis function.\n")
   test_gradient_descent()







"""simple docstring"""


import argparse
import json

from tqdm import tqdm



def   snake_case						(				)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		argparse.ArgumentParser()

      # Required parameters
      parser.add_argument(
          '--src_path'		,   type=_a		,   default='biencoder-nq-dev.json'		,   help='Path to raw DPR training data'		,   )
      parser.add_argument(
          '--evaluation_set'		,   type=_a		,   help='where to store parsed evaluation_set file'		,   )
      parser.add_argument(
          '--gold_data_path'		,   type=_a		,   help='where to store parsed gold_data_path file'		,   )
      lowerCamelCase__           =		parser.parse_args()

      with open(args.src_path		,   'r'							) as src_file, open(args.evaluation_set		,   'w'							) as eval_file, open(
          args.gold_data_path		,   'w'							) as gold_file:
            lowerCamelCase__           =		json.load(_a							)
            for dpr_record in tqdm(_a							):
                  lowerCamelCase__           =		dpr_record['question']
                  lowerCamelCase__           =		[context['title'] for context in dpr_record['positive_ctxs']]
                  eval_file.write(question + '\n'							)
                  gold_file.write('\t'.join(_a							) + '\n'							)


if __name__ == "__main__":
   main()



