
import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class    __lowerCamelCase     (   nn.Linear ):




				def __init__(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = True       ,	UpperCAmelCase = "constant"       ,	UpperCAmelCase = 0.0       ,	UpperCAmelCase = "topK"       ,	):
							super(UpperCAmelCase       ,	self      ).__init__(in_features=UpperCAmelCase       ,	out_features=UpperCAmelCase       ,	bias=UpperCAmelCase      )
							assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
							lowerCamelCase_ 		=     pruning_method

							if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
										lowerCamelCase_ 		=     mask_scale
										lowerCamelCase_ 		=     mask_init
										lowerCamelCase_ 		=     nn.Parameter(torch.empty(self.weight.size()      )      )
										self.init_mask()




				def 					UpperCAmelCase__	(							self      ):
							if self.mask_init == "constant":
										init.constant_(self.mask_scores       ,	val=self.mask_scale      )
							elif self.mask_init == "uniform":
										init.uniform_(self.mask_scores       ,	a=-self.mask_scale       ,	b=self.mask_scale      )
							elif self.mask_init == "kaiming":
										init.kaiming_uniform_(self.mask_scores       ,	a=math.sqrt(5      )      )





				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
							# Get the mask
							if self.pruning_method == "topK":
										lowerCamelCase_ 		=     TopKBinarizer.apply(self.mask_scores       ,	UpperCAmelCase      )
							elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
										lowerCamelCase_ 		=     '''sigmoied''' in self.pruning_method
										lowerCamelCase_ 		=     ThresholdBinarizer.apply(self.mask_scores       ,	UpperCAmelCase       ,	UpperCAmelCase      )
							elif self.pruning_method == "magnitude":
										lowerCamelCase_ 		=     MagnitudeBinarizer.apply(self.weight       ,	UpperCAmelCase      )
							elif self.pruning_method == "l0":
										lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_ 		=     -0.1, 1.1, 2 / 3
										if self.training:
													lowerCamelCase_ 		=     torch.zeros_like(self.mask_scores      ).uniform_().clamp(0.0_0_0_1       ,	0.9_9_9_9      )
													lowerCamelCase_ 		=     torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b      )
										else:
													lowerCamelCase_ 		=     torch.sigmoid(self.mask_scores      )
										lowerCamelCase_ 		=     s * (r - l) + l
										lowerCamelCase_ 		=     s_bar.clamp(min=0.0       ,	max=1.0      )
							# Mask weights with computed mask
							lowerCamelCase_ 		=     mask * self.weight
							# Compute output (linear layer) with masked weights
							return nn.functional.linear(UpperCAmelCase       ,	UpperCAmelCase       ,	self.bias      )





"""simple docstring"""


import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

A_						       =				0.25 + 0.0J
A_						       =				-0.4 + 0.6J
A_						       =				-0.1 + 0.651J
A_						       =				-2.0
A_						       =				56
A_						       =				2.0
A_						       =				666






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return numpy.exp(lowerCAmelCase__	) + c_parameter






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return z_values * z_values + c_parameter






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     numpy.linspace(-window_size  ,lowerCAmelCase__  ,lowerCAmelCase__	)
			lowerCamelCase_ 		=     x.reshape((nb_pixels, 1)	)
			lowerCamelCase_ 		=     numpy.linspace(-window_size  ,lowerCAmelCase__  ,lowerCAmelCase__	)
			lowerCamelCase_ 		=     y.reshape((1, nb_pixels)	)
			return x + 1.0J * y






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__ = None  ,):
			lowerCamelCase_ 		=     z_a.astype('''complex64'''	)
			for _ in range(lowerCAmelCase__	):
						lowerCamelCase_ 		=     eval_function(lowerCAmelCase__  ,lowerCAmelCase__	)
						if infinity is not None:
									numpy.nan_to_num(lowerCAmelCase__  ,copy=lowerCAmelCase__  ,nan=lowerCAmelCase__	)
									lowerCamelCase_ 		=     infinity
			return z_n






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,):
			lowerCamelCase_ 		=     (abs(lowerCAmelCase__	)).transpose()
			lowerCamelCase_ 		=     abs_z_final[::-1, :]
			pyplot.matshow(abs_z_final < escape_radius	)
			pyplot.title(f"Julia set of ${function_label}$, $c={function_params}$"	)
			pyplot.show()






def    lowercase	(  ):
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in multiply'''	)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''invalid value encountered in multiply'''  ,)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in absolute'''	)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in exp'''	)


if __name__ == "__main__":
						A_						       =				prepare_grid(window_size, nb_pixels)

						ignore_overflow_warnings()  # See file header for explanations

						A_						       =				24
						A_						       =				2 * abs(c_cauliflower) + 1
						A_						       =				iterate_function(
						    eval_quadratic_polynomial,
						    c_cauliflower,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("""z^2+c""", c_cauliflower, escape_radius, z_final)

						A_						       =				64
						A_						       =				2 * abs(c_polynomial_a) + 1
						A_						       =				iterate_function(
						    eval_quadratic_polynomial,
						    c_polynomial_a,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

						A_						       =				161
						A_						       =				2 * abs(c_polynomial_a) + 1
						A_						       =				iterate_function(
						    eval_quadratic_polynomial,
						    c_polynomial_a,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

						A_						       =				12
						A_						       =				10000.0
						A_						       =				iterate_function(
						    eval_exponential,
						    c_exponential,
						    nb_iterations,
						    z_a + 2,
						    infinity=1.0e10,
						)
						show_results("""e^z+c""", c_exponential, escape_radius, z_final)



