
'''simple docstring'''


import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
   snake_case_ :  Optional[int]     =					"""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input('Search: ')))

   print('Googling.....')

   snake_case_ :  str     =					F'''https://www.google.com/search?q={query}&num=100'''

   snake_case_ :  Optional[Any]     =					requests.get(
       url,
       headers={'User-Agent': str(UserAgent().random)},
   )

   try:
      snake_case_ :  Union[str, Any]     =					(
          BeautifulSoup(res.text, 'html.parser')
          .find('div', attrs={'class': 'yuRUbf'})
          .find('a')
          .get('href')
      )

   except AttributeError:
      snake_case_ :  Optional[int]     =					parse_qs(
          BeautifulSoup(res.text, 'html.parser')
          .find('div', attrs={'class': 'kCrYT'})
          .find('a')
          .get('href')
      )["""url"""][0]

   webbrowser.open(link)


'''simple docstring'''


import torch
from torch import nn


class 						lowercase__	(  nn.Module   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__=1 ,   lowerCamelCase__=False   ):

      '''simple docstring'''







      super().__init__()

      UpperCamelCase       =  n_token
      UpperCamelCase       =  d_embed
      UpperCamelCase       =  d_proj

      UpperCamelCase       =  cutoffs + [n_token]
      UpperCamelCase       =  [0] + self.cutoffs
      UpperCamelCase       =  div_val

      UpperCamelCase       =  self.cutoffs[0]
      UpperCamelCase       =  len(self.cutoffs   ) - 1
      UpperCamelCase       =  self.shortlist_size + self.n_clusters

      if self.n_clusters > 0:
         UpperCamelCase       =  nn.Parameter(torch.zeros(self.n_clusters ,   self.d_embed   )   )
         UpperCamelCase       =  nn.Parameter(torch.zeros(self.n_clusters   )   )

      UpperCamelCase       =  nn.ModuleList()
      UpperCamelCase       =  nn.ParameterList()

      if div_val == 1:
         for i in range(len(self.cutoffs   )   ):
            if d_proj != d_embed:
               self.out_projs.append(nn.Parameter(torch.FloatTensor(lowerCamelCase__ ,   lowerCamelCase__   )   )   )
            else:
               self.out_projs.append(lowerCamelCase__   )

         self.out_layers.append(nn.Linear(lowerCamelCase__ ,   lowerCamelCase__   )   )
      else:
         for i in range(len(self.cutoffs   )   ):
            UpperCamelCase     , UpperCamelCase       =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
            UpperCamelCase       =  d_embed // (div_val**i)

            self.out_projs.append(nn.Parameter(torch.FloatTensor(lowerCamelCase__ ,   lowerCamelCase__   )   )   )

            self.out_layers.append(nn.Linear(lowerCamelCase__ ,   r_idx - l_idx   )   )

      UpperCamelCase       =  keep_order







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      if proj is None:
         UpperCamelCase       =  nn.functional.linear(lowerCamelCase__ ,   lowerCamelCase__ ,   bias=lowerCamelCase__   )
      else:
         # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
         UpperCamelCase       =  nn.functional.linear(lowerCamelCase__ ,   proj.t().contiguous()   )
         UpperCamelCase       =  nn.functional.linear(lowerCamelCase__ ,   lowerCamelCase__ ,   bias=lowerCamelCase__   )
         # else:
         #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
         #     if bias is not None:
         #         logit = logit + bias

      return logit







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__=None ,   lowerCamelCase__=False   ):

      '''simple docstring'''







      if labels is not None:
         # Shift so that tokens < n predict n
         UpperCamelCase       =  hidden[..., :-1, :].contiguous()
         UpperCamelCase       =  labels[..., 1:].contiguous()
         UpperCamelCase       =  hidden.view(-1 ,   hidden.size(-1   )   )
         UpperCamelCase       =  labels.view(-1   )
         if hidden.size(0   ) != labels.size(0   ):
            raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''   )
      else:
         UpperCamelCase       =  hidden.view(-1 ,   hidden.size(-1   )   )

      if self.n_clusters == 0:
         UpperCamelCase       =  self._compute_logit(lowerCamelCase__ ,   self.out_layers[0].weight ,   self.out_layers[0].bias ,   self.out_projs[0]   )
         if labels is not None:
            UpperCamelCase       =  labels != -1_0_0
            UpperCamelCase       =  torch.zeros_like(lowerCamelCase__ ,   dtype=hidden.dtype ,   device=hidden.device   )
            UpperCamelCase       =  (
                -nn.functional.log_softmax(lowerCamelCase__ ,   dim=-1   )[mask].gather(1 ,   labels[mask].unsqueeze(1   )   ).squeeze(1   )
            )
         else:
            UpperCamelCase       =  nn.functional.log_softmax(lowerCamelCase__ ,   dim=-1   )
      else:
         # construct weights and biases
         UpperCamelCase     , UpperCamelCase       =  [], []
         for i in range(len(self.cutoffs   )   ):
            if self.div_val == 1:
               UpperCamelCase     , UpperCamelCase       =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
               UpperCamelCase       =  self.out_layers[0].weight[l_idx:r_idx]
               UpperCamelCase       =  self.out_layers[0].bias[l_idx:r_idx]
            else:
               UpperCamelCase       =  self.out_layers[i].weight
               UpperCamelCase       =  self.out_layers[i].bias

            if i == 0:
               UpperCamelCase       =  torch.cat([weight_i, self.cluster_weight] ,   dim=0   )
               UpperCamelCase       =  torch.cat([bias_i, self.cluster_bias] ,   dim=0   )

            weights.append(lowerCamelCase__   )
            biases.append(lowerCamelCase__   )

         UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  weights[0], biases[0], self.out_projs[0]

         UpperCamelCase       =  self._compute_logit(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )
         UpperCamelCase       =  nn.functional.log_softmax(lowerCamelCase__ ,   dim=1   )

         if labels is None:
            UpperCamelCase       =  hidden.new_empty((head_logit.size(0   ), self.n_token)   )
         else:
            UpperCamelCase       =  torch.zeros_like(lowerCamelCase__ ,   dtype=hidden.dtype ,   device=hidden.device   )

         UpperCamelCase       =  0
         UpperCamelCase       =  [0] + self.cutoffs
         for i in range(len(lowerCamelCase__   ) - 1   ):
            UpperCamelCase     , UpperCamelCase       =  cutoff_values[i], cutoff_values[i + 1]

            if labels is not None:
               UpperCamelCase       =  (labels >= l_idx) & (labels < r_idx)
               UpperCamelCase       =  mask_i.nonzero().squeeze()

               if indices_i.numel() == 0:
                  continue

               UpperCamelCase       =  labels.index_select(0 ,   lowerCamelCase__   ) - l_idx
               UpperCamelCase       =  head_logprob.index_select(0 ,   lowerCamelCase__   )
               UpperCamelCase       =  hidden.index_select(0 ,   lowerCamelCase__   )
            else:
               UpperCamelCase       =  hidden

            if i == 0:
               if labels is not None:
                  UpperCamelCase       =  head_logprob_i.gather(1 ,   target_i[:, None]   ).squeeze(1   )
               else:
                  UpperCamelCase       =  head_logprob[:, : self.cutoffs[0]]
            else:
               UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  weights[i], biases[i], self.out_projs[i]

               UpperCamelCase       =  self._compute_logit(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )
               UpperCamelCase       =  nn.functional.log_softmax(lowerCamelCase__ ,   dim=1   )
               UpperCamelCase       =  self.cutoffs[0] + i - 1  # No probability for the head cluster
               if labels is not None:
                  UpperCamelCase       =  head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                      1 ,   target_i[:, None]   ).squeeze(1   )
               else:
                  UpperCamelCase       =  head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
                  UpperCamelCase       =  logprob_i

            if labels is not None:
               if (hasattr(self ,   '''keep_order'''   ) and self.keep_order) or keep_order:
                  out.index_copy_(0 ,   lowerCamelCase__ ,   -logprob_i   )
               else:
                  out[offset : offset + logprob_i.size(0   )].copy_(-logprob_i   )
               offset += logprob_i.size(0   )

      return out







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if self.n_clusters == 0:
         UpperCamelCase       =  self._compute_logit(lowerCamelCase__ ,   self.out_layers[0].weight ,   self.out_layers[0].bias ,   self.out_projs[0]   )
         return nn.functional.log_softmax(lowerCamelCase__ ,   dim=-1   )
      else:
         # construct weights and biases
         UpperCamelCase     , UpperCamelCase       =  [], []
         for i in range(len(self.cutoffs   )   ):
            if self.div_val == 1:
               UpperCamelCase     , UpperCamelCase       =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
               UpperCamelCase       =  self.out_layers[0].weight[l_idx:r_idx]
               UpperCamelCase       =  self.out_layers[0].bias[l_idx:r_idx]
            else:
               UpperCamelCase       =  self.out_layers[i].weight
               UpperCamelCase       =  self.out_layers[i].bias

            if i == 0:
               UpperCamelCase       =  torch.cat([weight_i, self.cluster_weight] ,   dim=0   )
               UpperCamelCase       =  torch.cat([bias_i, self.cluster_bias] ,   dim=0   )

            weights.append(lowerCamelCase__   )
            biases.append(lowerCamelCase__   )

         UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  weights[0], biases[0], self.out_projs[0]
         UpperCamelCase       =  self._compute_logit(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )

         UpperCamelCase       =  hidden.new_empty((head_logit.size(0   ), self.n_token)   )
         UpperCamelCase       =  nn.functional.log_softmax(lowerCamelCase__ ,   dim=1   )

         UpperCamelCase       =  [0] + self.cutoffs
         for i in range(len(lowerCamelCase__   ) - 1   ):
            UpperCamelCase     , UpperCamelCase       =  cutoff_values[i], cutoff_values[i + 1]

            if i == 0:
               UpperCamelCase       =  head_logprob[:, : self.cutoffs[0]]
            else:
               UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  weights[i], biases[i], self.out_projs[i]

               UpperCamelCase       =  self._compute_logit(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )
               UpperCamelCase       =  nn.functional.log_softmax(lowerCamelCase__ ,   dim=1   )

               UpperCamelCase       =  head_logprob[:, -i] + tail_logprob_i
               UpperCamelCase       =  logprob_i

         return out





