




'''simple docstring'''



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    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE = True    ,  __SCREAMING_SNAKE_CASE = "constant"    ,  __SCREAMING_SNAKE_CASE = 0.0    ,  __SCREAMING_SNAKE_CASE = "topK"    ,  )      ->  Optional[int]:



            '''simple docstring'''




            super(__SCREAMING_SNAKE_CASE    ,  self  ).__init__(in_features=__SCREAMING_SNAKE_CASE    ,  out_features=__SCREAMING_SNAKE_CASE    ,  bias=__SCREAMING_SNAKE_CASE  )
            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       lowerCAmelCase      (   self  )      ->  List[Any]:



            '''simple docstring'''




            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       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )      ->  List[Any]:



            '''simple docstring'''




            if self.pruning_method == "topK":
                 __snake_case    					=  TopKBinarizer.apply(self.mask_scores    ,  __SCREAMING_SNAKE_CASE  )
            elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
                 __snake_case    					=  '''sigmoied''' in self.pruning_method
                 __snake_case    					=  ThresholdBinarizer.apply(self.mask_scores    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )
            elif self.pruning_method == "magnitude":
                 __snake_case    					=  MagnitudeBinarizer.apply(self.weight    ,  __SCREAMING_SNAKE_CASE  )
            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(0.0_001    ,  0.9_999  )
                      __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(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  self.bias  )












'''simple docstring'''



from datetime import date

import requests

UpperCAmelCase_       :  str      =		'''https://api.carbonintensity.org.uk/intensity'''







def 			_UpperCamelCase ()->   str:




     '''simple docstring'''





     __snake_case    					=  requests.get(_lowerCamelCase	).json()['''data'''][0]
     return last_half_hour["intensity"]["actual"]







def 			_UpperCamelCase (_lowerCamelCase							:     Dict   ,	_lowerCamelCase							:     List[Any]	)->   list:




     '''simple docstring'''





     return requests.get(f'''{BASE_URL}/{start}/{end}'''	).json()["data"]


if __name__ == "__main__":
     for entry in fetch_from_to(start=date(2_0_2_0, 1_0, 1), end=date(2_0_2_0, 1_0, 3)):
          print('''from {from} to {to}: {intensity[actual]}'''.format(**entry))
     print(F"""{fetch_last_half_hour() = }""")






