"""simple docstring"""


import torch
from torch import nn


class        a				(		nn.Module   ):


    def __init__(					self	:  List[Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  List[Any]=1   ,				__lowerCAmelCase	:  Tuple=False					):
      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      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Tuple					):
      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      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[Any]=None   ,				__lowerCAmelCase	:  Union[str, Any]=False					):

      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 != -100
          _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      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[Any]					):
      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

"""simple docstring"""


import unittest

from knapsack import knapsack as k


class        a				(		unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      0
      _UpperCAmelCase						 =      [0]
      _UpperCAmelCase						 =      [0]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				0					)

      _UpperCAmelCase						 =      [60]
      _UpperCAmelCase						 =      [10]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				0					)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      _UpperCAmelCase						 =      3
      _UpperCAmelCase						 =      [1, 2, 3]
      _UpperCAmelCase						 =      [3, 2, 1]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				5					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      50
      _UpperCAmelCase						 =      [60, 100, 120]
      _UpperCAmelCase						 =      [10, 20, 30]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				220					)


if __name__ == "__main__":
    unittest.main()
