



'''simple docstring'''






import torch
from torch import nn

class 							a__(      nn.Module			):







    def __init__(   self				:						Optional[int]		,    __snake_case				:						Dict		,    __snake_case				:						Tuple		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						List[Any]		,    __snake_case				:						Tuple=1		,    __snake_case				:						str=False			):
      super().__init__()

      a		:      int       =  n_token
      a		:      List[str]       =  d_embed
      a		:      Union[str, Any]       =  d_proj

      a		:      str       =  cutoffs + [n_token]
      a		:      Optional[int]       =  [0] + self.cutoffs
      a		:      int       =  div_val

      a		:      Tuple       =  self.cutoffs[0]
      a		:      str       =  len(self.cutoffs			) - 1
      a		:      List[Any]       =  self.shortlist_size + self.n_clusters

      if self.n_clusters > 0:
        a		:      Tuple       =  nn.Parameter(torch.zeros(self.n_clusters		,    self.d_embed			)			)
        a		:      List[Any]       =  nn.Parameter(torch.zeros(self.n_clusters			)			)

      a		:      Any       =  nn.ModuleList()
      a		:      Tuple       =  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(__snake_case		,    __snake_case			)			)			)
          else:
            self.out_projs.append(__snake_case			)

        self.out_layers.append(nn.Linear(__snake_case		,    __snake_case			)			)
      else:
        for i in range(len(self.cutoffs			)			):
          a     ,			a		:      Optional[int]       =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
          a		:      Union[str, Any]       =  d_embed // (div_val**i)

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

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

      a		:      Optional[int]       =  keep_order







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						Dict		,    __snake_case				:						int		,    __snake_case				:						Tuple		,    __snake_case				:						Optional[Any]			):
      if proj is None:
        a		:      List[str]       =  nn.functional.linear(__snake_case		,    __snake_case		,    bias=__snake_case			)
      else:
        # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
        a		:      int       =  nn.functional.linear(__snake_case		,    proj.t().contiguous()			)
        a		:      Union[str, Any]       =  nn.functional.linear(__snake_case		,    __snake_case		,    bias=__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     lowercase_ (   self				:						Optional[int]		,    __snake_case				:						Optional[Any]		,    __snake_case				:						List[Any]=None		,    __snake_case				:						Union[str, Any]=False			):

      if labels is not None:
        # Shift so that tokens < n predict n
        a		:      List[str]       =  hidden[..., :-1, :].contiguous()
        a		:      Union[str, Any]       =  labels[..., 1:].contiguous()
        a		:      Dict       =  hidden.view(-1		,    hidden.size(-1			)			)
        a		:      Tuple       =  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		:      List[Any]       =  hidden.view(-1		,    hidden.size(-1			)			)

      if self.n_clusters == 0:
        a		:      Tuple       =  self._compute_logit(__snake_case		,    self.out_layers[0].weight		,    self.out_layers[0].bias		,    self.out_projs[0]			)
        if labels is not None:
          a		:      Tuple       =  labels != -1_00
          a		:      Optional[int]       =  torch.zeros_like(__snake_case		,    dtype=hidden.dtype		,    device=hidden.device			)
          a		:      List[Any]       =  (
              -nn.functional.log_softmax(__snake_case		,    dim=-1			)[mask].gather(1		,    labels[mask].unsqueeze(1			)			).squeeze(1			)
          )
        else:
          a		:      Dict       =  nn.functional.log_softmax(__snake_case		,    dim=-1			)
      else:
        # construct weights and biases
        a     ,			a		:      Dict       =  [], []
        for i in range(len(self.cutoffs			)			):
          if self.div_val == 1:
            a     ,			a		:      Union[str, Any]       =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
            a		:      Dict       =  self.out_layers[0].weight[l_idx:r_idx]
            a		:      List[str]       =  self.out_layers[0].bias[l_idx:r_idx]
          else:
            a		:      Optional[Any]       =  self.out_layers[i].weight
            a		:      int       =  self.out_layers[i].bias

          if i == 0:
            a		:      Tuple       =  torch.cat([weight_i, self.cluster_weight]		,    dim=0			)
            a		:      Union[str, Any]       =  torch.cat([bias_i, self.cluster_bias]		,    dim=0			)

          weights.append(__snake_case			)
          biases.append(__snake_case			)

        a     ,			a     ,			a		:      Dict       =  weights[0], biases[0], self.out_projs[0]

        a		:      Optional[int]       =  self._compute_logit(__snake_case		,    __snake_case		,    __snake_case		,    __snake_case			)
        a		:      int       =  nn.functional.log_softmax(__snake_case		,    dim=1			)

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

        a		:      List[Any]       =  0
        a		:      str       =  [0] + self.cutoffs
        for i in range(len(__snake_case			) - 1			):
          a     ,			a		:      List[str]       =  cutoff_values[i], cutoff_values[i + 1]

          if labels is not None:
            a		:      Union[str, Any]       =  (labels >= l_idx) & (labels < r_idx)
            a		:      Union[str, Any]       =  mask_i.nonzero().squeeze()

            if indices_i.numel() == 0:
              continue

            a		:      int       =  labels.index_select(0		,    __snake_case			) - l_idx
            a		:      Any       =  head_logprob.index_select(0		,    __snake_case			)
            a		:      List[str]       =  hidden.index_select(0		,    __snake_case			)
          else:
            a		:      Tuple       =  hidden

          if i == 0:
            if labels is not None:
              a		:      Optional[Any]       =  head_logprob_i.gather(1		,    target_i[:, None]			).squeeze(1			)
            else:
              a		:      int       =  head_logprob[:, : self.cutoffs[0]]
          else:
            a     ,			a     ,			a		:      List[str]       =  weights[i], biases[i], self.out_projs[i]

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

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

      return out







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

          if i == 0:
            a		:      Union[str, Any]       =  torch.cat([weight_i, self.cluster_weight]		,    dim=0			)
            a		:      Any       =  torch.cat([bias_i, self.cluster_bias]		,    dim=0			)

          weights.append(__snake_case			)
          biases.append(__snake_case			)

        a     ,			a     ,			a		:      int       =  weights[0], biases[0], self.out_projs[0]
        a		:      Tuple       =  self._compute_logit(__snake_case		,    __snake_case		,    __snake_case		,    __snake_case			)

        a		:      Tuple       =  hidden.new_empty((head_logit.size(0			), self.n_token)			)
        a		:      Optional[Any]       =  nn.functional.log_softmax(__snake_case		,    dim=1			)

        a		:      List[str]       =  [0] + self.cutoffs
        for i in range(len(__snake_case			) - 1			):
          a     ,			a		:      Tuple       =  cutoff_values[i], cutoff_values[i + 1]

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

            a		:      Dict       =  self._compute_logit(__snake_case		,    __snake_case		,    __snake_case		,    __snake_case			)
            a		:      Optional[Any]       =  nn.functional.log_softmax(__snake_case		,    dim=1			)

            a		:      int       =  head_logprob[:, -i] + tail_logprob_i
            a		:      List[str]       =  logprob_i

        return out




'''simple docstring'''






from pathlib import Path

import fire
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[Any]       =  Path(_A       )
  a		:      Tuple       =  Path(_A       )
  dest_dir.mkdir(exist_ok=_A       )
  for path in src_dir.iterdir():
    a		:      Tuple       =  [x.rstrip() for x in list(path.open().readlines()       )][:n]
    a		:      List[str]       =  dest_dir.joinpath(path.name       )
    print(_A       )
    dest_path.open('w'       ).write('\n'.join(_A       )       )


if __name__ == "__main__":
     fire.Fire(minify)