"""simple docstring"""







import matplotlib.pyplot as plt
import numpy as np


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     Optional[Any]      					=				len(__UpperCAmelCase      )  # Number of training samples
	_lowercase			:     Optional[Any]      					=				np.eye(__UpperCAmelCase      )  # Initialize weights as identity matrix
	for j in range(__UpperCAmelCase      ):
		_lowercase			:     int      					=				point - x_train[j]
		_lowercase			:     List[str]      					=				np.exp(diff @ diff.T / (-2.0 * tau**2)      )

	return weights


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     Optional[Any]      					=				weight_matrix(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )
	_lowercase			:     Optional[int]      					=				np.linalg.inv(x_train.T @ weight_mat @ x_train      ) @ (
	    x_train.T @ weight_mat @ y_train.T
	)

	return weight


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     Optional[int]      					=				np.zeros(len(__UpperCAmelCase      )      )  # Initialize array of predictions
	for i, item in enumerate(__UpperCAmelCase      ):
		_lowercase			:     Optional[Any]      					=				item @ local_weight(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )

	return y_pred


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	import seaborn as sns

	_lowercase			:     Tuple      					=				sns.load_dataset(__UpperCAmelCase      )
	_lowercase			:     Tuple      					=				np.array(data[x_name]      )
	_lowercase			:     Tuple      					=				np.array(data[y_name]      )

	_lowercase			:     List[str]      					=				np.ones(len(__UpperCAmelCase      )      )

	# pairing elements of one and x_data
	_lowercase			:     List[str]      					=				np.column_stack((one, x_data)      )

	return x_train, x_data, y_data


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,):






	"""simple docstring"""





	_lowercase			:     Union[str, Any]      					=				np.sort(__UpperCAmelCase  ,axis=0      )
	plt.scatter(__UpperCAmelCase  ,__UpperCAmelCase  ,color='blue'      )
	plt.plot(
	    x_train_sorted[:, 1]  ,preds[x_train[:, 1].argsort(0      )]  ,color='yellow'  ,linewidth=5  ,)
	plt.title('Local Weighted Regression'      )
	plt.xlabel(__UpperCAmelCase      )
	plt.ylabel(__UpperCAmelCase      )
	plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# Demo with a dataset from the seaborn module
						SCREAMING_SNAKE_CASE				,     SCREAMING_SNAKE_CASE				,     SCREAMING_SNAKE_CASE						=    load_data('tips', 'total_bill', 'tip')
						SCREAMING_SNAKE_CASE						=    local_weight_regression(training_data_x, tip, 5)
						plot_preds(training_data_x, predictions, total_bill, tip, 'total_bill', 'tip')




"""simple docstring"""







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


SCREAMING_SNAKE_CASE						=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						=    {
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}



class 			_lowerCamelCase      (__lowerCamelCase  ):
				_snake_case  =	"regnet"
				_snake_case  =	["x", "y"]







				def __init__(			self     :							Any      ,       lowerCamelCase_     :							Optional[Any]=3      ,       lowerCamelCase_     :							List[str]=3_2      ,       lowerCamelCase_     :							str=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]      ,       lowerCamelCase_     :							int=[2, 6, 1_2, 2]      ,       lowerCamelCase_     :							Union[str, Any]=6_4      ,       lowerCamelCase_     :							int="y"      ,       lowerCamelCase_     :							Optional[Any]="relu"      ,       **lowerCamelCase_     :							Optional[Any]      ,       ):

					"""simple docstring"""






					super().__init__(**lowerCamelCase_    )
					if layer_type not in self.layer_types:
						raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types    )}'''    )
					_lowercase			:     str      					=				num_channels
					_lowercase			:     Optional[Any]      					=				embedding_size
					_lowercase			:     Union[str, Any]      					=				hidden_sizes
					_lowercase			:     Optional[int]      					=				depths
					_lowercase			:     Optional[int]      					=				groups_width
					_lowercase			:     Tuple      					=				layer_type
					_lowercase			:     Union[str, Any]      					=				hidden_act
					# always downsample in the first stage
					_lowercase			:     Optional[int]      					=				True



