

import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
      from scipy.stats import pearsonr, spearmanr
      from sklearn.metrics import fa_score, matthews_corrcoef


lowerCAmelCase__       = (
    "This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate "
    "library. You can have a look at this example script for pointers: "
    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"
)
def      _lowerCAmelCase(	__A   ,       __A			):
  warnings.warn(__A   ,       __A			)
  requires_backends(__A   ,       "sklearn"			)
  return (preds == labels).mean()
def      _lowerCAmelCase(	__A   ,       __A			):
  warnings.warn(__A   ,       __A			)
  requires_backends(__A   ,       "sklearn"			)
  UpperCAmelCase					      =  simple_accuracy(__A   ,       __A			)
  UpperCAmelCase					      =  fa_score(y_true=__A   ,       y_pred=__A			)
  return {
      "acc": acc,
      "f1": fa,
      "acc_and_f1": (acc + fa) / 2,
  }
def      _lowerCAmelCase(	__A   ,       __A			):
  warnings.warn(__A   ,       __A			)
  requires_backends(__A   ,       "sklearn"			)
  UpperCAmelCase					      =  pearsonr(__A   ,       __A			)[0]
  UpperCAmelCase					      =  spearmanr(__A   ,       __A			)[0]
  return {
      "pearson": pearson_corr,
      "spearmanr": spearman_corr,
      "corr": (pearson_corr + spearman_corr) / 2,
  }
def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):
  warnings.warn(__A   ,       __A			)
  requires_backends(__A   ,       "sklearn"			)
  assert len(__A			) == len(__A			), F"Predictions and labels have mismatched lengths {len(__A			)} and {len(__A			)}"
  if task_name == "cola":
    return {"mcc": matthews_corrcoef(__A   ,       __A			)}
  elif task_name == "sst-2":
    return {"acc": simple_accuracy(__A   ,       __A			)}
  elif task_name == "mrpc":
    return acc_and_fa(__A   ,       __A			)
  elif task_name == "sts-b":
    return pearson_and_spearman(__A   ,       __A			)
  elif task_name == "qqp":
    return acc_and_fa(__A   ,       __A			)
  elif task_name == "mnli":
    return {"mnli/acc": simple_accuracy(__A   ,       __A			)}
  elif task_name == "mnli-mm":
    return {"mnli-mm/acc": simple_accuracy(__A   ,       __A			)}
  elif task_name == "qnli":
    return {"acc": simple_accuracy(__A   ,       __A			)}
  elif task_name == "rte":
    return {"acc": simple_accuracy(__A   ,       __A			)}
  elif task_name == "wnli":
    return {"acc": simple_accuracy(__A   ,       __A			)}
  elif task_name == "hans":
    return {"acc": simple_accuracy(__A   ,       __A			)}
  else:
    raise KeyError(__A			)





def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):
  warnings.warn(__A   ,       __A			)
  requires_backends(__A   ,       "sklearn"			)
  if len(__A			) != len(__A			):
    raise ValueError(F"Predictions and labels have mismatched lengths {len(__A			)} and {len(__A			)}"			)
  if task_name == "xnli":
    return {"acc": simple_accuracy(__A   ,       __A			)}
  else:
    raise KeyError(__A			)






import numpy

# List of input, output pairs
lowerCAmelCase__       = (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCAmelCase__       = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase__       = [2, 4, 1, 5]
lowerCAmelCase__       = len(train_data)
lowerCAmelCase__       = 0.0_0_9
def      _lowerCAmelCase(	__A   ,       __A="train"			):
  return calculate_hypothesis_value(__A   ,       __A			) - output(
      __A   ,       __A			)
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  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      _lowerCAmelCase(	__A   ,       __A			):
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None
def      _lowerCAmelCase(	__A   ,       __A			):
  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      _lowerCAmelCase(	__A   ,       __A=m			):
  UpperCAmelCase					      =  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      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  summation_of_cost_derivative(__A   ,       __A			) / m
  return cost_derivative_value
def      _lowerCAmelCase(	):
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  UpperCAmelCase					      =  0.000002
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  0
  while True:
    j += 1
    UpperCAmelCase					      =  [0, 0, 0, 0]
    for i in range(0   ,       len(__A			)			):
      UpperCAmelCase					      =  get_cost_derivative(i - 1			)
      UpperCAmelCase					      =  (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        __A   ,       __A   ,       atol=__A   ,       rtol=__A   ,       ):
      break
    UpperCAmelCase					      =  temp_parameter_vector
  print(("Number of iterations:", j)			)
def      _lowerCAmelCase(	):
  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()



