




import torch
from torch import nn





class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict=1       ,     SCREAMING_SNAKE_CASE__					:					Dict=False       )				->	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        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Union[str, Any]:






         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        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					List[str]=None       ,     SCREAMING_SNAKE_CASE__					:					int=False       )				->	int:






         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        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	List[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






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: str       ,    **lowerCAmelCase_					: str      ):
     """simple docstring"""

     lowerCAmelCase__          =			AutoConfig.from_pretrained(lowerCAmelCase_       ,    **lowerCAmelCase_      )
     lowerCAmelCase__          =			AutoModelForSeqaSeqLM.from_config(lowerCAmelCase_      )
     model.save_pretrained(lowerCAmelCase_      )
     AutoTokenizer.from_pretrained(lowerCAmelCase_      ).save_pretrained(lowerCAmelCase_      )
     return model


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