



'''simple docstring'''

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







def 						__UpperCamelCase				(							_lowercase					) ->			Tuple:
	return 1 / (1 + np.exp(-z					))







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			int:
	return (-y * np.log(_A					) - (1 - y) * np.log(1 - h					)).mean()







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Dict:
	_lowercase					:       Optional[Any]       =		np.dot(_A,				_A					)
	return np.sum(y * scores - np.log(1 + np.exp(_A					)					)					)







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase,				_lowercase=7_0000					) ->			int:
	_lowercase					:       Optional[Any]       =		np.zeros(x.shape[1]					)

	for iterations in range(_A					):
		_lowercase					:       Union[str, Any]       =		np.dot(_A,				_A					)
		_lowercase					:       str       =		sigmoid_function(_A					)
		_lowercase					:       Any       =		np.dot(x.T,				h - y					) / y.size
		_lowercase					:       str       =		theta - alpha * gradient  # updating the weights
		_lowercase					:       List[Any]       =		np.dot(_A,				_A					)
		_lowercase					:       Union[str, Any]       =		sigmoid_function(_A					)
		_lowercase					:       List[Any]       =		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__":
		_A				:    List[Any]     						=datasets.load_iris()
		_A				:    List[str]     						=iris.data[:, :2]
		_A				:    Optional[Any]     						=(iris.target != 0) * 1

		_A				:    List[Any]     						=0.1
		_A				:    Optional[int]     						=logistic_reg(alpha, x, y, max_iterations=7_0_0_0_0)
		print('''theta: ''', theta)  # printing the theta i.e our weights vector







		def 						__UpperCamelCase				(							_lowercase					) ->			str:
			return sigmoid_function(
			    np.dot(_A,				_A					)					)  # predicting the value of probability from the logistic regression algorithm

		plt.figure(figsize=(1_0, 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)				:    str     						=(x[:, 0].min(), x[:, 0].max())
		(_A)				:    Tuple     						=(x[:, 1].min(), x[:, 1].max())
		(_A)				:    List[str]     						=np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
		_A				:    Any     						=np.c_[xxa.ravel(), xxa.ravel()]
		_A				:    str     						=predict_prob(grid).reshape(xxa.shape)
		plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

		plt.legend()






		plt.show()








'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A				:    Optional[Any]     						=logging.get_logger(__name__)

_A				:    Optional[int]     						={
    '''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
    '''microsoft/markuplm-large''': '''https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json''',
}



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""markuplm"""




					def __init__(  self				:	int     ,  UpperCamelCase_				:	Optional[Any]=3_0522     ,  UpperCamelCase_				:	Optional[Any]=768     ,  UpperCamelCase_				:	Tuple=12     ,  UpperCamelCase_				:	Union[str, Any]=12     ,  UpperCamelCase_				:	Tuple=3072     ,  UpperCamelCase_				:	Union[str, Any]="gelu"     ,  UpperCamelCase_				:	List[Any]=0.1     ,  UpperCamelCase_				:	List[Any]=0.1     ,  UpperCamelCase_				:	Dict=512     ,  UpperCamelCase_				:	Optional[int]=2     ,  UpperCamelCase_				:	Any=0.02     ,  UpperCamelCase_				:	Optional[Any]=1E-12     ,  UpperCamelCase_				:	List[str]=0     ,  UpperCamelCase_				:	Optional[int]=0     ,  UpperCamelCase_				:	Tuple=2     ,  UpperCamelCase_				:	str=256     ,  UpperCamelCase_				:	Optional[Any]=1024     ,  UpperCamelCase_				:	Union[str, Any]=216     ,  UpperCamelCase_				:	int=1001     ,  UpperCamelCase_				:	int=32     ,  UpperCamelCase_				:	int=50     ,  UpperCamelCase_				:	str="absolute"     ,  UpperCamelCase_				:	Union[str, Any]=True     ,  UpperCamelCase_				:	Optional[int]=None     ,  **UpperCamelCase_				:	Any     ,  )  ->       Optional[int]:



						'''simple docstring'''





						super().__init__(
						    pad_token_id=UpperCamelCase_     ,  bos_token_id=UpperCamelCase_     ,  eos_token_id=UpperCamelCase_     ,  **UpperCamelCase_     ,  )
						_lowercase					:       List[Any]       =		vocab_size
						_lowercase					:       Union[str, Any]       =		hidden_size
						_lowercase					:       Dict       =		num_hidden_layers
						_lowercase					:       Optional[Any]       =		num_attention_heads
						_lowercase					:       Dict       =		hidden_act
						_lowercase					:       Optional[Any]       =		intermediate_size
						_lowercase					:       Optional[int]       =		hidden_dropout_prob
						_lowercase					:       Union[str, Any]       =		attention_probs_dropout_prob
						_lowercase					:       Any       =		max_position_embeddings
						_lowercase					:       List[Any]       =		type_vocab_size
						_lowercase					:       Union[str, Any]       =		initializer_range
						_lowercase					:       Optional[int]       =		layer_norm_eps
						_lowercase					:       Optional[Any]       =		position_embedding_type
						_lowercase					:       str       =		use_cache
						_lowercase					:       str       =		classifier_dropout
						# additional properties
						_lowercase					:       int       =		max_depth
						_lowercase					:       Dict       =		max_xpath_tag_unit_embeddings
						_lowercase					:       str       =		max_xpath_subs_unit_embeddings
						_lowercase					:       List[str]       =		tag_pad_id
						_lowercase					:       Optional[int]       =		subs_pad_id
						_lowercase					:       Any       =		xpath_unit_hidden_size
