"""simple docstring"""




# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]			) ->   Union[str, Any]:
  return 1 / (1 + np.exp(-z			))





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Dict			) ->   List[str]:
  return (-y * np.log(_lowerCamelCase			) - (1 - y) * np.log(1 - h			)).mean()





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Union[str, Any]			) ->   List[str]:
  _lowerCAmelCase		: List[str]						    =  np.dot(_lowerCamelCase    ,_lowerCamelCase			)
  return np.sum(y * scores - np.log(1 + np.exp(_lowerCamelCase			)			)			)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: List[str]    ,_lowerCamelCase		: Union[str, Any]=70000			) ->   List[str]:
  _lowerCAmelCase		: Union[str, Any]						    =  np.zeros(x.shape[1]			)

  for iterations in range(_lowerCamelCase			):
    _lowerCAmelCase		: Tuple						    =  np.dot(_lowerCamelCase    ,_lowerCamelCase			)
    _lowerCAmelCase		: str						    =  sigmoid_function(_lowerCamelCase			)
    _lowerCAmelCase		: str						    =  np.dot(x.T    ,h - y			) / y.size
    _lowerCAmelCase		: Any						    =  theta - alpha * gradient  # updating the weights
    _lowerCAmelCase		: List[str]						    =  np.dot(_lowerCamelCase    ,_lowerCamelCase			)
    _lowerCAmelCase		: Dict						    =  sigmoid_function(_lowerCamelCase			)
    _lowerCAmelCase		: str						    =  cost_function(_lowerCamelCase    ,_lowerCamelCase			)
    if iterations % 100 == 0:
      print(f"loss: {j} \t"			)  # printing the loss after every 100 iterations
  return theta


# In[68]:

if __name__ == "__main__":
     _a   :	Union[str, Any] 					=     datasets.load_iris()
     _a   :	Tuple 					=     iris.data[:, :2]
     _a   :	Tuple 					=     (iris.target != 0) * 1

     _a   :	Any 					=     0.1
     _a   :	List[Any] 					=     logistic_reg(alpha, x, y, max_iterations=70_000)
     print('theta: ', theta)  # printing the theta i.e our weights vector





     def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str			) ->   int:
       return sigmoid_function(
           np.dot(_lowerCamelCase    ,_lowerCamelCase			)			)  # predicting the value of probability from the logistic regression algorithm

     plt.figure(figsize=(10, 6))
     plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
     plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
     (_a)   :	int 					=     (x[:, 0].min(), x[:, 0].max())
     (_a)   :	Any 					=     (x[:, 1].min(), x[:, 1].max())
     (_a)   :	Dict 					=     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
     _a   :	Optional[Any] 					=     np.c_[xxa.ravel(), xxa.ravel()]
     _a   :	List[str] 					=     predict_prob(grid).reshape(xxa.shape)
     plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

     plt.legend()






     plt.show()


"""simple docstring"""




import argparse
import json
import subprocess





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Any			) ->   List[Any]:
  _lowerCAmelCase		: Tuple						    =  []

  _lowerCAmelCase		: Optional[int]						    =  (
      f"curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""
      """ https://api.github.com/repos/huggingface/transformers/actions/runners"""
  )
  _lowerCAmelCase		: List[str]						    =  subprocess.run(_lowerCamelCase    ,shell=_lowerCamelCase    ,stdout=subprocess.PIPE			)
  _lowerCAmelCase		: int						    =  output.stdout.decode("""utf-8"""			)
  _lowerCAmelCase		: Tuple						    =  json.loads(_lowerCamelCase			)

  _lowerCAmelCase		: int						    =  status["""runners"""]
  for runner in runners:
    if runner["name"] in target_runners:
      if runner["status"] == "offline":
        offline_runners.append(_lowerCamelCase			)

    # save the result so we can report them on Slack
  with open("""offline_runners.txt"""    ,"""w"""			) as fp:
    fp.write(json.dumps(_lowerCamelCase			)			)

  if len(_lowerCamelCase			) > 0:
    _lowerCAmelCase		: int						    =  """\n""".join([x["""name"""] for x in offline_runners]			)
    raise ValueError(f"The following runners are offline:\n{failed}"			)


if __name__ == "__main__":





     def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]			) ->   Optional[int]:
       return values.split(""","""			)

     _a   :	int 					=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--target_runners',
         default=None,
         type=list_str,
         required=True,
         help='Comma-separated list of runners to check status.',
     )

     parser.add_argument(
         '--token', default=None, type=str, required=True, help='A token that has actions:read permission.'
     )
     _a   :	Tuple 					=     parser.parse_args()






     get_runner_status(args.target_runners, args.token)




