


import numpy

# List of input, output pairs
SCREAMING_SNAKE_CASE							    =							(
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
SCREAMING_SNAKE_CASE							    =							(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
SCREAMING_SNAKE_CASE							    =							[2, 4, 1, 5]
SCREAMING_SNAKE_CASE							    =							len(train_data)
SCREAMING_SNAKE_CASE							    =							0.009




def  a   (lowerCAmelCase__		,				lowerCAmelCase__="train"   ):
   return calculate_hypothesis_value(__lowercase		,				__lowercase   ) - output(
       __lowercase		,				__lowercase   )




def  a   (lowerCAmelCase__   ):
   __a					=  0
   for i in range(len(__lowercase   ) - 1   ):
      hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
   hyp_val += parameter_vector[0]
   return hyp_val




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   if data_set == "train":
      return train_data[example_no][1]
   elif data_set == "test":
      return test_data[example_no][1]
   return None




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   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  a   (lowerCAmelCase__		,				lowerCAmelCase__=m   ):
   __a					=  0
   for i in range(__lowercase   ):
      if index == -1:
         summation_value += _error(__lowercase   )
      else:
         summation_value += _error(__lowercase   ) * train_data[i][0][index]
   return summation_value




def  a   (lowerCAmelCase__   ):
   __a					=  summation_of_cost_derivative(__lowercase		,				__lowercase   ) / m
   return cost_derivative_value




def  a   ():
   global parameter_vector
   # Tune these values to set a tolerance value for predicted output
   __a					=  0.0_0_0_0_0_2
   __a					=  0
   __a					=  0
   while True:
      j += 1
      __a					=  [0, 0, 0, 0]
      for i in range(0		,				len(__lowercase   )   ):
         __a					=  get_cost_derivative(i - 1   )
         __a					=  (
             parameter_vector[i] - LEARNING_RATE * cost_derivative
         )
      if numpy.allclose(
          __lowercase		,				__lowercase		,				atol=__lowercase		,				rtol=__lowercase		,				):
         break
      __a					=  temp_parameter_vector
   print(("""Number of iterations:""", j)   )




def  a   ():
   for i in range(len(__lowercase   )   ):
      print(("""Actual output value:""", output(__lowercase		,				"""test"""   ))   )
      print(("""Hypothesis output:""", calculate_hypothesis_value(__lowercase		,				"""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 logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase      :   Any      =       logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase      :   List[Any]      =       argparse.ArgumentParser(
          description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
      )
      parser.add_argument(
          """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
      )
      parser.add_argument(
          """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
      )
      parser.add_argument("""--vocab_size""", default=3_0522, type=int)
      lowercase      :   str      =       parser.parse_args()

      logger.info(F"""Loading data from {args.data_file}""")
      with open(args.data_file, """rb""") as fp:
            lowercase      :   int      =       pickle.load(fp)

      logger.info("""Counting occurrences for MLM.""")
      lowercase      :   List[Any]      =       Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase      :   int      =       [0] * args.vocab_size
      for k, v in counter.items():
            lowercase      :   List[Any]      =       v

      logger.info(F"""Dump to {args.token_counts_dump}""")
      with open(args.token_counts_dump, """wb""") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)



