


"""simple docstring"""

import torch
from torch import nn

class __lowerCAmelCase		(				nn.Module      ):





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=False				):

      '''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(a_     ,					a_				)				)				)
        else:
         self.out_projs.append(a_				)

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

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

      __UpperCamelCase      =	keep_order





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      if proj is None:
       __UpperCamelCase      =	nn.functional.linear(a_     ,					a_     ,					bias=a_				)
      else:
       # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
       __UpperCamelCase      =	nn.functional.linear(a_     ,					proj.t().contiguous()				)
       __UpperCamelCase      =	nn.functional.linear(a_     ,					a_     ,					bias=a_				)
       # 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     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=False				):

      '''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(a_     ,					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(a_     ,					dtype=hidden.dtype     ,					device=hidden.device				)
        __UpperCamelCase      =	(
            -nn.functional.log_softmax(a_     ,					dim=-1				)[mask].gather(1     ,					labels[mask].unsqueeze(1				)				).squeeze(1				)
        )
       else:
        __UpperCamelCase      =	nn.functional.log_softmax(a_     ,					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(a_				)
        biases.append(a_				)

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

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

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

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

      return out







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




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

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

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

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

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

       return out





"""simple docstring"""

from typing import Union

import fire
import torch
from tqdm import tqdm






def 							A						(    snake_case				:str    , snake_case				:str = "cpu"    , snake_case				:Union[str, None] = None		)				->     None:
 __UpperCamelCase      =	torch.load(snake_case    , map_location=snake_case		)
 for k, v in tqdm(state_dict.items()		):
  if not isinstance(snake_case    , torch.Tensor		):
   raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'		)
  __UpperCamelCase      =	v.half()
 if save_path is None:  # overwrite src_path
  __UpperCamelCase      =	src_path
 torch.save(snake_case    , snake_case		)


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



