



a_      :      str    =     6_55_21






def     __a					(		__UpperCAmelCase       ):
 a__												= 1
 a__												= 0
 for plain_chr in plain_text:
  a__												= (a + ord(__UpperCAmelCase       )) % MOD_ADLER
  a__												= (b + a) % MOD_ADLER
 return (b << 16) | a










import torch
from torch import nn
class    __UpperCamelCase			(       nn.Module				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=1		,				SCREAMING_SNAKE_CASE=False      ) ->			Tuple:
  super().__init__()

  a__												= n_token
  a__												= d_embed
  a__												= d_proj

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

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

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

  a__												= nn.ModuleList()
  a__												= 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(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )      )      )
    else:
     self.out_projs.append(SCREAMING_SNAKE_CASE      )

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

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

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

  a__												= keep_order

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			str:
  if proj is None:
   a__												= nn.functional.linear(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				bias=SCREAMING_SNAKE_CASE      )
  else:
   # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
   a__												= nn.functional.linear(SCREAMING_SNAKE_CASE		,				proj.t().contiguous()      )
   a__												= nn.functional.linear(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				bias=SCREAMING_SNAKE_CASE      )
   # 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		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=False      ) ->			Optional[int]:

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

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

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

    weights.append(SCREAMING_SNAKE_CASE      )
    biases.append(SCREAMING_SNAKE_CASE      )

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

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

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

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

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

     if indices_i.numel() == 0:
      continue

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

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

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

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

  return out



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

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

    weights.append(SCREAMING_SNAKE_CASE      )
    biases.append(SCREAMING_SNAKE_CASE      )

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

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

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

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

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

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

   return out





