




from typing import Union

import fire
import torch
from tqdm import tqdm




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case = "cpu"			,     snake_case = None       )						->				None:
						_UpperCAmelCase      =			torch.load(snake_case			,     map_location=snake_case       )
						for k, v in tqdm(state_dict.items()       ):
												if not isinstance(snake_case			,     torch.Tensor       ):
																		raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""       )
												_UpperCAmelCase      =			v.half()
						if save_path is None:  # overwrite src_path
												_UpperCAmelCase      =			src_path
						torch.save(snake_case			,     snake_case       )


if __name__ == "__main__":
				fire.Fire(convert)





# 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      (		snake_case       )						->				List[str]:
						return 1 / (1 + np.exp(-z       ))




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Tuple:
						return (-y * np.log(snake_case       ) - (1 - y) * np.log(1 - h       )).mean()




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
						return np.sum(y * scores - np.log(1 + np.exp(snake_case       )       )       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=7_0_0_0_0       )						->				Tuple:
						_UpperCAmelCase      =			np.zeros(x.shape[1]       )

						for iterations in range(snake_case       ):
												_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
												_UpperCAmelCase      =			sigmoid_function(snake_case       )
												_UpperCAmelCase      =			np.dot(x.T			,     h - y       ) / y.size
												_UpperCAmelCase      =			theta - alpha * gradient  # updating the weights
												_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
												_UpperCAmelCase      =			sigmoid_function(snake_case       )
												_UpperCAmelCase      =			cost_function(snake_case			,     snake_case       )
												if iterations % 1_0_0 == 0:
																		print(f"loss: {j} \t"       )  # printing the loss after every 100 iterations
						return theta


# In[68]:

if __name__ == "__main__":
				a													=			datasets.load_iris()
				a													=			iris.data[:, :2]
				a													=			(iris.target != 0) * 1

				a													=			0.1
				a													=			logistic_reg(alpha, x, y, max_iterations=70000)
				print("theta: ", theta)  # printing the theta i.e our weights vector




				def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[Any]:
										return sigmoid_function(
										    np.dot(snake_case			,     snake_case       )       )  # 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)							,  (a))													=			(x[:, 0].min(), x[:, 0].max())
				((a)							,  (a))													=			(x[:, 1].min(), x[:, 1].max())
				((a)							,  (a))													=			np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
				a													=			np.c_[xxa.ravel(), xxa.ravel()]
				a													=			predict_prob(grid).reshape(xxa.shape)
				plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")

				plt.legend()

				plt.show()