import torch
from torch import nn





class      A							(				nn.Module     ):


   def __init__(self     :						Any       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Dict=1       ,  __UpperCAmelCase     :						int=False		)						->				List[Any]:
         """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(__UpperCAmelCase       ,  __UpperCAmelCase		)		)		)
                     else:
                           self.out_projs.append(__UpperCAmelCase		)

               self.out_layers.append(nn.Linear(__UpperCAmelCase       ,  __UpperCAmelCase		)		)
         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(__UpperCAmelCase       ,  __UpperCAmelCase		)		)		)

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

         UpperCAmelCase__								=  keep_order


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						Any		)						->				int:
         """simple docstring"""



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

         return logit


   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Union[str, Any]=None       ,  __UpperCAmelCase     :						str=False		)						->				Tuple:
         """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(__UpperCAmelCase       ,  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(__UpperCAmelCase       ,  dtype=hidden.dtype       ,  device=hidden.device		)
                     UpperCAmelCase__								=  (
                         -nn.functional.log_softmax(__UpperCAmelCase       ,  dim=-1		)[mask].gather(1       ,  labels[mask].unsqueeze(1		)		).squeeze(1		)
                     )
               else:
                     UpperCAmelCase__								=  nn.functional.log_softmax(__UpperCAmelCase       ,  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(__UpperCAmelCase		)
                     biases.append(__UpperCAmelCase		)

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

               UpperCAmelCase__								=  self._compute_logit(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)
               UpperCAmelCase__								=  nn.functional.log_softmax(__UpperCAmelCase       ,  dim=1		)

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

               UpperCAmelCase__								=  0
               UpperCAmelCase__								=  [0] + self.cutoffs
               for i in range(len(__UpperCAmelCase		) - 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       ,  __UpperCAmelCase		) - l_idx
                           UpperCAmelCase__								=  head_logprob.index_select(0       ,  __UpperCAmelCase		)
                           UpperCAmelCase__								=  hidden.index_select(0       ,  __UpperCAmelCase		)
                     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(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)
                           UpperCAmelCase__								=  nn.functional.log_softmax(__UpperCAmelCase       ,  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       ,  __UpperCAmelCase       ,  -logprob_i		)
                           else:
                                 out[offset : offset + logprob_i.size(0		)].copy_(-logprob_i		)
                           offset += logprob_i.size(0		)

         return out





   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						Any		)						->				str:
         """simple docstring"""



         if self.n_clusters == 0:
               UpperCAmelCase__								=  self._compute_logit(__UpperCAmelCase       ,  self.out_layers[0].weight       ,  self.out_layers[0].bias       ,  self.out_projs[0]		)
               return nn.functional.log_softmax(__UpperCAmelCase       ,  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(__UpperCAmelCase		)
                     biases.append(__UpperCAmelCase		)

               UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__								=  weights[0], biases[0], self.out_projs[0]
               UpperCAmelCase__								=  self._compute_logit(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)

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

               UpperCAmelCase__								=  [0] + self.cutoffs
               for i in range(len(__UpperCAmelCase		) - 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(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)
                           UpperCAmelCase__								=  nn.functional.log_softmax(__UpperCAmelCase       ,  dim=1		)

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

               return out




def    lowerCAmelCase_ (     __A,  __A							)     ->				None:







      '''simple docstring'''


      UpperCAmelCase__								=  len(__A							)
      print("The following activities are selected:"							)

      # The first activity is always selected
      UpperCAmelCase__								=  0
      print(__A,  end=","							)

      # Consider rest of the activities
      for j in range(__A							):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(__A,  end=","							)
                  UpperCAmelCase__								=  j


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase__											=					[1, 3, 0, 5, 8, 5]
      UpperCamelCase__											=					[2, 4, 6, 7, 9, 9]
      print_max_activities(start, finish)



