


'''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      a (     __a  )    ->      Dict:



					'''simple docstring'''






					return 1 / (1 + np.exp(-z  ))


def      a (     __a					,   __a  )    ->      List[str]:



					'''simple docstring'''






					return (-y * np.log(__a  ) - (1 - y) * np.log(1 - h  )).mean()


def      a (     __a					,   __a					,   __a  )    ->      str:



					'''simple docstring'''






					UpperCamelCase__  :Union[str, Any]				   =					np.dot(__a					,   __a  )
					return np.sum(y * scores - np.log(1 + np.exp(__a  )  )  )


def      a (     __a					,   __a					,   __a					,   __a=70000  )    ->      str:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					np.zeros(x.shape[1]  )

					for iterations in range(__a  ):
										UpperCamelCase__  :Dict				   =					np.dot(__a					,   __a  )
										UpperCamelCase__  :Any				   =					sigmoid_function(__a  )
										UpperCamelCase__  :Union[str, Any]				   =					np.dot(x.T					,   h - y  ) / y.size
										UpperCamelCase__  :Any				   =					theta - alpha * gradient  # updating the weights
										UpperCamelCase__  :Optional[Any]				   =					np.dot(__a					,   __a  )
										UpperCamelCase__  :Dict				   =					sigmoid_function(__a  )
										UpperCamelCase__  :List[str]				   =					cost_function(__a					,   __a  )
										if iterations % 100 == 0:
															print(f'''loss: {j} \t'''  )  # printing the loss after every 100 iterations
					return theta


# In[68]:

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

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


		def      a (     __a  )    ->      Any:



							'''simple docstring'''






							return sigmoid_function(
							    np.dot(__a					,   __a  )  )  # 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''')
		((__snake_case) ,   (__snake_case))				 =	(x[:, 0].min(), x[:, 0].max())
		((__snake_case) ,   (__snake_case))				 =	(x[:, 1].min(), x[:, 1].max())
		((__snake_case) ,   (__snake_case))				 =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
		__snake_case				 =	np.c_[xxa.ravel(), xxa.ravel()]
		__snake_case				 =	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

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		__snake_case				 =	argparse.ArgumentParser()

		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

		parser.add_argument(
		    '''--txt2img_unclip''',
		    default='''kakaobrain/karlo-v1-alpha''',
		    type=str,
		    required=False,
		    help='''The pretrained txt2img unclip.''',
		)

		__snake_case				 =	parser.parse_args()

		__snake_case				 =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		__snake_case				 =	CLIPImageProcessor()
		__snake_case				 =	CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

		__snake_case				 =	UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)