

import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer



class     lowercase_       (    nn.Linear	):



      def __init__(     self :							Dict     ,   A__ :							int     ,   A__ :							int     ,   A__ :							bool = True     ,   A__ :							str = "constant"     ,   A__ :							float = 0.0     ,   A__ :							str = "topK"     ,   )       ->	int:
        super(A__     ,   self						).__init__(in_features=A__     ,   out_features=A__     ,   bias=A__						)
        assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
        _snake_case		=				pruning_method

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



      def 		UpperCamelCase_						(     self :							Any						)       ->	int:
        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 :							Optional[Any]     ,   A__ :							torch.tensor     ,   A__ :							float						)       ->	Dict:
        # Get the mask
        if self.pruning_method == "topK":
          _snake_case		=				TopKBinarizer.apply(self.mask_scores     ,   A__						)
        elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
          _snake_case		=				'''sigmoied''' in self.pruning_method
          _snake_case		=				ThresholdBinarizer.apply(self.mask_scores     ,   A__     ,   A__						)
        elif self.pruning_method == "magnitude":
          _snake_case		=				MagnitudeBinarizer.apply(self.weight     ,   A__						)
        elif self.pruning_method == "l0":
          _snake_case,	_snake_case,	_snake_case		=				-0.1, 1.1, 2 / 3
          if self.training:
            _snake_case		=				torch.zeros_like(self.mask_scores						).uniform_().clamp(0.0001     ,   0.9999						)
            _snake_case		=				torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b						)
          else:
            _snake_case		=				torch.sigmoid(self.mask_scores						)
          _snake_case		=				s * (r - l) + l
          _snake_case		=				s_bar.clamp(min=0.0     ,   max=1.0						)
        # Mask weights with computed mask
        _snake_case		=				mask * self.weight
        # Compute output (linear layer) with masked weights
        return nn.functional.linear(A__     ,   A__     ,   self.bias						)




import functools
from math import sqrt
from time import time



def  snake_case_(_UpperCamelCase					,			*_UpperCamelCase					,			**_UpperCamelCase	)				->      str:



  """simple docstring"""






  _snake_case		=				time()
  _snake_case		=				func(*_UpperCamelCase					,			**_UpperCamelCase	)
  _snake_case		=				time()
  if int(end - start	) > 0:
    print(F"""{func.__name__} runtime: {(end - start):0.4f} s"""	)
  else:
    print(F"""{func.__name__} runtime: {(end - start) * 1_000:0.4f} ms"""	)
  return output



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  if n == 0:
    return [0]
  _snake_case		=				[0, 1]
  for _ in range(n - 1	):
    fib.append(fib[-1] + fib[-2]	)
  return fib



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  def fib_recursive_term(_UpperCamelCase	) -> int:
    if i < 0:
      raise Exception('''n is negative'''	)
    if i < 2:
      return i
    return fib_recursive_term(i - 1	) + fib_recursive_term(i - 2	)

  if n < 0:
    raise Exception('''n is negative'''	)
  return [fib_recursive_term(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  @functools.cache
  def fib_recursive_term(_UpperCamelCase	) -> int:
    if i < 0:
      raise Exception('''n is negative'''	)
    if i < 2:
      return i
    return fib_recursive_term(i - 1	) + fib_recursive_term(i - 2	)

  if n < 0:
    raise Exception('''n is negative'''	)
  return [fib_recursive_term(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  # Cache must be outside recursuive function
  # other it will reset every time it calls itself.
  _snake_case		=				{0: 0, 1: 1, 2: 1}  # Prefilled cache

  def rec_fn_memoized(_UpperCamelCase	) -> int:
    if num in cache:
      return cache[num]

    _snake_case		=				rec_fn_memoized(num - 1	) + rec_fn_memoized(num - 2	)
    _snake_case		=				value
    return value

  return [rec_fn_memoized(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  if n >= 1_475:
    raise Exception('''n is too large'''	)
  _snake_case		=				sqrt(5	)
  _snake_case		=				(1 + sqrt_a) / 2
  return [round(phi**i / sqrt_a	) for i in range(n + 1	)]


if __name__ == "__main__":
 __A     						=      30
 time_func(fib_iterative, num)
 time_func(fib_recursive, num)  # Around 3s runtime
 time_func(fib_recursive_cached, num)  # Around 0ms runtime
 time_func(fib_memoization, num)
 time_func(fib_binet, num)

