





'''simple docstring'''


import torch
from torch import nn

class    lowerCAmelCase_		(		nn.Module		):




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=1			,     _lowerCAmelCase=False		)    ->							Optional[int]:
     super().__init__()

     _lowerCAmelCase				=      n_token
     _lowerCAmelCase				=      d_embed
     _lowerCAmelCase				=      d_proj

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

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

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

     _lowerCAmelCase				=      nn.ModuleList()
     _lowerCAmelCase				=      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		)		):
             _lowerCAmelCase				=      self.cutoff_ends[i], self.cutoff_ends[i + 1]
             _lowerCAmelCase				=      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		)		)

     _lowerCAmelCase				=      keep_order




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Tuple:
     if proj is None:
         _lowerCAmelCase				=      nn.functional.linear(_lowerCAmelCase			,     _lowerCAmelCase			,     bias=_lowerCAmelCase		)
     else:
         # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
         _lowerCAmelCase				=      nn.functional.linear(_lowerCAmelCase			,     proj.t().contiguous()		)
         _lowerCAmelCase				=      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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     _lowerCAmelCase=False		)    ->							List[str]:

     if labels is not None:
         # Shift so that tokens < n predict n
         _lowerCAmelCase				=      hidden[..., :-1, :].contiguous()
         _lowerCAmelCase				=      labels[..., 1:].contiguous()
         _lowerCAmelCase				=      hidden.view(-1			,     hidden.size(-1		)		)
         _lowerCAmelCase				=      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:
         _lowerCAmelCase				=      hidden.view(-1			,     hidden.size(-1		)		)

     if self.n_clusters == 0:
         _lowerCAmelCase				=      self._compute_logit(_lowerCAmelCase			,     self.out_layers[0].weight			,     self.out_layers[0].bias			,     self.out_projs[0]		)
         if labels is not None:
             _lowerCAmelCase				=      labels != -100
             _lowerCAmelCase				=      torch.zeros_like(_lowerCAmelCase			,     dtype=hidden.dtype			,     device=hidden.device		)
             _lowerCAmelCase				=      (
                 -nn.functional.log_softmax(_lowerCAmelCase			,     dim=-1		)[mask].gather(1			,     labels[mask].unsqueeze(1		)		).squeeze(1		)
             )
         else:
             _lowerCAmelCase				=      nn.functional.log_softmax(_lowerCAmelCase			,     dim=-1		)
     else:
         # construct weights and biases
         _lowerCAmelCase				=      [], []
         for i in range(len(self.cutoffs		)		):
             if self.div_val == 1:
                 _lowerCAmelCase				=      self.cutoff_ends[i], self.cutoff_ends[i + 1]
                 _lowerCAmelCase				=      self.out_layers[0].weight[l_idx:r_idx]
                 _lowerCAmelCase				=      self.out_layers[0].bias[l_idx:r_idx]
             else:
                 _lowerCAmelCase				=      self.out_layers[i].weight
                 _lowerCAmelCase				=      self.out_layers[i].bias

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

             weights.append(_lowerCAmelCase		)
             biases.append(_lowerCAmelCase		)

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

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

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

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

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

                 if indices_i.numel() == 0:
                     continue

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

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

                 _lowerCAmelCase				=      self._compute_logit(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)
                 _lowerCAmelCase				=      nn.functional.log_softmax(_lowerCAmelCase			,     dim=1		)
                 _lowerCAmelCase				=      self.cutoffs[0] + i - 1  # No probability for the head cluster
                 if labels is not None:
                     _lowerCAmelCase				=      head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                         1			,     target_i[:, None]		).squeeze(1		)
                 else:
                     _lowerCAmelCase				=      head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
                     _lowerCAmelCase				=      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 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Any:
     if self.n_clusters == 0:
         _lowerCAmelCase				=      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
         _lowerCAmelCase				=      [], []
         for i in range(len(self.cutoffs		)		):
             if self.div_val == 1:
                 _lowerCAmelCase				=      self.cutoff_ends[i], self.cutoff_ends[i + 1]
                 _lowerCAmelCase				=      self.out_layers[0].weight[l_idx:r_idx]
                 _lowerCAmelCase				=      self.out_layers[0].bias[l_idx:r_idx]
             else:
                 _lowerCAmelCase				=      self.out_layers[i].weight
                 _lowerCAmelCase				=      self.out_layers[i].bias

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

             weights.append(_lowerCAmelCase		)
             biases.append(_lowerCAmelCase		)

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

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

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

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

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

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

         return out











'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        raise TypeError("Input value must be an 'int' type"    )

    _lowerCAmelCase				=      0
    while number:
        position += 1
        number >>= 1

    return position


if __name__ == "__main__":
       import doctest

       doctest.testmod()


