
"""simple docstring"""
import matplotlib.pyplot as plt
import numpy as np

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       len(_lowerCAmelCase		)  # Number of training samples
						__lowerCAmelCase        =       np.eye(_lowerCAmelCase		)  # Initialize weights as identity matrix
						for j in range(_lowerCAmelCase		):
												__lowerCAmelCase        =       point - x_train[j]
												__lowerCAmelCase        =       np.exp(diff @ diff.T / (-2.0 * tau**2)		)

						return weights

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       weight_matrix(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		)
						__lowerCAmelCase        =       np.linalg.inv(x_train.T @ weight_mat @ x_train		) @ (
						    x_train.T @ weight_mat @ y_train.T
						)

						return weight

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       np.zeros(len(_lowerCAmelCase		)		)  # Initialize array of predictions
						for i, item in enumerate(_lowerCAmelCase		):
												__lowerCAmelCase        =       item @ local_weight(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		)

						return y_pred

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		):
						import seaborn as sns

						__lowerCAmelCase        =       sns.load_dataset(_lowerCAmelCase		)
						__lowerCAmelCase        =       np.array(data[x_name]		)
						__lowerCAmelCase        =       np.array(data[y_name]		)

						__lowerCAmelCase        =       np.ones(len(_lowerCAmelCase		)		)

						# pairing elements of one and x_data
						__lowerCAmelCase        =       np.column_stack((one, x_data)		)

						return x_train, x_data, y_data

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  ):
						__lowerCAmelCase        =       np.sort(_lowerCAmelCase				,  axis=0		)
						plt.scatter(_lowerCAmelCase				,  _lowerCAmelCase				,  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(_lowerCAmelCase		)
						plt.ylabel(_lowerCAmelCase		)
						plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Demo with a dataset from the seaborn module
	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"""
import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       args.serialization_dir
						__lowerCAmelCase        =       args.pruning_method
						__lowerCAmelCase        =       args.threshold

						__lowerCAmelCase        =       torch.load(os.path.join(_lowerCAmelCase				,  """pytorch_model.bin"""		)				,  map_location="""cpu"""		)

						__lowerCAmelCase        =       0  # Number of remaining (not pruned) params in the encoder
						__lowerCAmelCase        =       0  # Number of params in the encoder

						print("""name""".ljust(60				,  """ """		)				,  """Remaining Weights %"""				,  """Remaining Weight"""		)
						for name, param in st.items():
												if "encoder" not in name:
																		continue

												if "mask_scores" in name:
																		if pruning_method == "topK":
																								__lowerCAmelCase        =       TopKBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								__lowerCAmelCase        =       ThresholdBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "l0":
																								__lowerCAmelCase ,      __lowerCAmelCase        =       -0.1, 1.1
																								__lowerCAmelCase        =       torch.sigmoid(_lowerCAmelCase		)
																								__lowerCAmelCase        =       s * (r - l) + l
																								__lowerCAmelCase        =       s_bar.clamp(min=0.0				,  max=1.0		)
																								__lowerCAmelCase        =       (mask > 0.0).sum().item()
																		else:
																								raise ValueError("""Unknown pruning method"""		)
																		remaining_count += mask_ones
																		print(name.ljust(60				,  """ """		)				,  str(round(100 * mask_ones / param.numel()				,  3		)		).ljust(20				,  """ """		)				,  str(_lowerCAmelCase		)		)
												else:
																		encoder_count += param.numel()
																		if "bias" in name or "LayerNorm" in name:
																								remaining_count += param.numel()

						print(""""""		)
						print("""Remaining Weights (global) %: """				,  100 * remaining_count / encoder_count		)


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

			parser.add_argument(
			    '''--pruning_method''',
			    choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
			    type=str,
			    required=True,
			    help=(
			        '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
			        ''' pruning)'''
			    ),
			)
			parser.add_argument(
			    '''--threshold''',
			    type=float,
			    required=False,
			    help=(
			        '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
			        '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
			        '''Not needed for `l0`'''
			    ),
			)
			parser.add_argument(
			    '''--serialization_dir''',
			    type=str,
			    required=True,
			    help='''Folder containing the model that was previously fine-pruned''',
			)

			SCREAMING_SNAKE_CASE_			      =					parser.parse_args()

			main(args)
