
import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






class 			A			(		nn.Linear		):
     """simple docstring"""

     def __init__(    self  :   int	,  A__  :   int	,  A__  :   int	,  A__  :   bool = True	,  A__  :   str = "constant"	,  A__  :   float = 0.0	,  A__  :   str = "topK"	,  )				->       Optional[Any]:
           super(A__	,  self			).__init__(in_features=A__	,  out_features=A__	,  bias=A__			)
           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  __snake_case   (    self  :   List[Any]			)				->       Optional[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  __snake_case   (    self  :   Optional[Any]	,  A__  :   torch.tensor	,  A__  :   float			)				->       Tuple:
           # Get the mask
           if self.pruning_method == "topK":
                 lowerCamelCase__        =				TopKBinarizer.apply(self.mask_scores	,  A__			)
           elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
                 lowerCamelCase__        =				'sigmoied' in self.pruning_method
                 lowerCamelCase__        =				ThresholdBinarizer.apply(self.mask_scores	,  A__	,  A__			)
           elif self.pruning_method == "magnitude":
                 lowerCamelCase__        =				MagnitudeBinarizer.apply(self.weight	,  A__			)
           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(0.0001	,  0.9999			)
                       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(A__	,  A__	,  self.bias			)





import shutil

import requests

def 			lowerCAmelCase_			(  lowerCamelCase:       str     ,				lowerCamelCase:       bool = False     ,				lowerCamelCase:       str = "."						)					->			dict:


      '''simple docstring'''




      lowerCamelCase__        =				'https://api.nasa.gov/planetary/apod'
      return requests.get(lowerCamelCase     ,				params={'api_key': api_key}						).json()

def 			lowerCAmelCase_			(  lowerCamelCase:       str     ,				lowerCamelCase:       str = "."						)					->			dict:


      '''simple docstring'''




      lowerCamelCase__        =				get_apod_data(lowerCamelCase						)
      lowerCamelCase__        =				apod_data['url']
      lowerCamelCase__        =				img_url.split('/'						)[-1]
      lowerCamelCase__        =				requests.get(lowerCamelCase     ,				stream=lowerCamelCase						)

      with open(f"""{path}/{img_name}"""     ,				'wb+'						) as img_file:
            shutil.copyfileobj(response.raw     ,				lowerCamelCase						)
      del response
      return apod_data

def 			lowerCAmelCase_			(  lowerCamelCase:       str						)					->			dict:


      '''simple docstring'''




      lowerCamelCase__        =				'https://images-api.nasa.gov/search'
      return requests.get(lowerCamelCase     ,				params={'q': query}						).json()


if __name__ == "__main__":
      print(save_apod("""YOUR API KEY"""))
      __SCREAMING_SNAKE_CASE    :   Tuple       =get_archive_data("""apollo 2011""")["""collection"""]["""items"""]
      print(apollo_aaaa_items[0]["""data"""][0]["""description"""])