

import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger





class  __UpperCAmelCase					(	lowerCamelCase__						):



   def __init__(							self							:					int,		__A							:					Tuple,		__A							:					int						):
     UpperCAmelCase	:							Optional[Any]						=			params

     UpperCAmelCase	:							str						=			np.array(__A						)
     UpperCAmelCase	:							List[Any]						=			np.array([len(__A						) for t in data]						)

     self.check()
     self.remove_long_sequences()
     self.remove_empty_sequences()
     self.remove_unknown_sequences()
     self.check()
     self.print_statistics()



   def __getitem__(							self							:					Tuple,		__A							:					Any						):
     return (self.token_ids[index], self.lengths[index])



   def __len__(							self							:					Tuple						):
     return len(self.lengths						)



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     assert len(self.token_ids						) == len(self.lengths						)
     assert all(self.lengths[i] == len(self.token_ids[i]						) for i in range(len(self.lengths						)						)						)



   def 	__magic_name__   (							self							:					List[Any]						):
     UpperCAmelCase	:							Optional[Any]						=			self.params.max_model_input_size
     UpperCAmelCase	:							Dict						=			self.lengths > max_len
     logger.info(F'''Splitting {sum(__A						)} too long sequences.'''						)

     def divide_chunks(__A							:					int,		__A							:					Tuple						):
       return [l[i : i + n] for i in range(0,		len(__A						),		__A						)]

     UpperCAmelCase	:							Union[str, Any]						=			[]
     UpperCAmelCase	:							Optional[int]						=			[]
     if self.params.mlm:
       UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
     else:
       UpperCAmelCase			,						UpperCAmelCase	:							List[str]						=			self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

     for seq_, len_ in zip(self.token_ids,		self.lengths						):
       assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
       if len_ <= max_len:
         new_tok_ids.append(seq_						)
         new_lengths.append(len_						)
       else:
         UpperCAmelCase	:							Optional[int]						=			[]
         for sub_s in divide_chunks(seq_,		max_len - 2						):
           if sub_s[0] != cls_id:
             UpperCAmelCase	:							List[Any]						=			np.insert(__A,		0,		__A						)
           if sub_s[-1] != sep_id:
             UpperCAmelCase	:							Optional[int]						=			np.insert(__A,		len(__A						),		__A						)
           assert len(__A						) <= max_len
           assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
           sub_seqs.append(__A						)

         new_tok_ids.extend(__A						)
         new_lengths.extend([len(__A						) for l in sub_seqs]						)

     UpperCAmelCase	:							List[str]						=			np.array(__A						)
     UpperCAmelCase	:							List[Any]						=			np.array(__A						)



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							Tuple						=			len(self						)
     UpperCAmelCase	:							str						=			self.lengths > 1_1
     UpperCAmelCase	:							Optional[Any]						=			self.token_ids[indices]
     UpperCAmelCase	:							str						=			self.lengths[indices]
     UpperCAmelCase	:							Union[str, Any]						=			len(self						)
     logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''						)



   def 	__magic_name__   (							self							:					str						):
     if "unk_token" not in self.params.special_tok_ids:
       return
     else:
       UpperCAmelCase	:							Dict						=			self.params.special_tok_ids['''unk_token''']
     UpperCAmelCase	:							List[Any]						=			len(self						)
     UpperCAmelCase	:							List[Any]						=			np.array([np.count_nonzero(a == unk_token_id						) for a in self.token_ids]						)
     UpperCAmelCase	:							Optional[Any]						=			(unk_occs / self.lengths) < 0.5
     UpperCAmelCase	:							List[Any]						=			self.token_ids[indices]
     UpperCAmelCase	:							int						=			self.lengths[indices]
     UpperCAmelCase	:							List[str]						=			len(self						)
     logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''						)



   def 	__magic_name__   (							self							:					List[Any]						):
     if not self.params.is_master:
       return
     logger.info(F'''{len(self						)} sequences'''						)
     # data_len = sum(self.lengths)
     # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
     # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

     # unk_idx = self.params.special_tok_ids['unk_token']
     # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
     # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')





   def 	__magic_name__   (							self							:					List[Any],		__A							:					Tuple						):
     UpperCAmelCase	:							Dict						=			[t[0] for t in batch]
     UpperCAmelCase	:							List[str]						=			[t[1] for t in batch]
     assert len(__A						) == len(__A						)

     # Max for paddings
     UpperCAmelCase	:							Union[str, Any]						=			max(__A						)

     # Pad token ids
     if self.params.mlm:
       UpperCAmelCase	:							List[Any]						=			self.params.special_tok_ids['''pad_token''']
     else:
       UpperCAmelCase	:							List[str]						=			self.params.special_tok_ids['''unk_token''']
     UpperCAmelCase	:							List[str]						=			[list(t.astype(__A						)						) + [pad_idx] * (max_seq_len_ - len(__A						)) for t in token_ids]
     assert len(tk_						) == len(__A						)
     assert all(len(__A						) == max_seq_len_ for t in tk_						)

     UpperCAmelCase	:							Optional[Any]						=			torch.tensor(tk_						)  # (bs, max_seq_len_)
     UpperCAmelCase	:							Dict						=			torch.tensor(__A						)  # (bs)
     return tk_t, lg_t








import torch
from torch import nn





class  __UpperCAmelCase					(	nn.Module						):



   def __init__(							self							:					List[Any],		__A							:					List[Any],		__A							:					Optional[Any],		__A							:					int,		__A							:					List[Any],		__A							:					int=1,		__A							:					List[str]=False						):
     super().__init__()

     UpperCAmelCase	:							Union[str, Any]						=			n_token
     UpperCAmelCase	:							List[str]						=			d_embed
     UpperCAmelCase	:							Dict						=			d_proj

     UpperCAmelCase	:							List[Any]						=			cutoffs + [n_token]
     UpperCAmelCase	:							Dict						=			[0] + self.cutoffs
     UpperCAmelCase	:							int						=			div_val

     UpperCAmelCase	:							Union[str, Any]						=			self.cutoffs[0]
     UpperCAmelCase	:							str						=			len(self.cutoffs						) - 1
     UpperCAmelCase	:							Optional[int]						=			self.shortlist_size + self.n_clusters

     if self.n_clusters > 0:
       UpperCAmelCase	:							str						=			nn.Parameter(torch.zeros(self.n_clusters,		self.d_embed						)						)
       UpperCAmelCase	:							List[str]						=			nn.Parameter(torch.zeros(self.n_clusters						)						)

     UpperCAmelCase	:							Dict						=			nn.ModuleList()
     UpperCAmelCase	:							Optional[int]						=			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	:							Dict						=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
         UpperCAmelCase	:							str						=			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	:							Optional[int]						=			keep_order



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

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

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

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

         weights.append(__A						)
         biases.append(__A						)

       UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							str						=			weights[0], biases[0], self.out_projs[0]

       UpperCAmelCase	:							Dict						=			self._compute_logit(__A,		__A,		__A,		__A						)
       UpperCAmelCase	:							int						=			nn.functional.log_softmax(__A,		dim=1						)

       if labels is None:
         UpperCAmelCase	:							Optional[int]						=			hidden.new_empty((head_logit.size(0						), self.n_token)						)
       else:
         UpperCAmelCase	:							Union[str, Any]						=			torch.zeros_like(__A,		dtype=hidden.dtype,		device=hidden.device						)

       UpperCAmelCase	:							Union[str, Any]						=			0
       UpperCAmelCase	:							Any						=			[0] + self.cutoffs
       for i in range(len(__A						) - 1						):
         UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			cutoff_values[i], cutoff_values[i + 1]

         if labels is not None:
           UpperCAmelCase	:							List[str]						=			(labels >= l_idx) & (labels < r_idx)
           UpperCAmelCase	:							Tuple						=			mask_i.nonzero().squeeze()

           if indices_i.numel() == 0:
             continue

           UpperCAmelCase	:							Any						=			labels.index_select(0,		__A						) - l_idx
           UpperCAmelCase	:							Dict						=			head_logprob.index_select(0,		__A						)
           UpperCAmelCase	:							List[str]						=			hidden.index_select(0,		__A						)
         else:
           UpperCAmelCase	:							Tuple						=			hidden

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

           UpperCAmelCase	:							List[str]						=			self._compute_logit(__A,		__A,		__A,		__A						)
           UpperCAmelCase	:							Dict						=			nn.functional.log_softmax(__A,		dim=1						)
           UpperCAmelCase	:							int						=			self.cutoffs[0] + i - 1  # No probability for the head cluster
           if labels is not None:
             UpperCAmelCase	:							Union[str, Any]						=			head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                 1,		target_i[:, None]						).squeeze(1						)
           else:
             UpperCAmelCase	:							int						=			head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
             UpperCAmelCase	:							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,		__A,		-logprob_i						)
           else:
             out[offset : offset + logprob_i.size(0						)].copy_(-logprob_i						)
           offset += logprob_i.size(0						)

     return out





   def 	__magic_name__   (							self							:					Tuple,		__A							:					List[str]						):
     if self.n_clusters == 0:
       UpperCAmelCase	:							int						=			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	:							Any						=			[], []
       for i in range(len(self.cutoffs						)						):
         if self.div_val == 1:
           UpperCAmelCase			,						UpperCAmelCase	:							Any						=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
           UpperCAmelCase	:							List[Any]						=			self.out_layers[0].weight[l_idx:r_idx]
           UpperCAmelCase	:							List[str]						=			self.out_layers[0].bias[l_idx:r_idx]
         else:
           UpperCAmelCase	:							List[str]						=			self.out_layers[i].weight
           UpperCAmelCase	:							Dict						=			self.out_layers[i].bias

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

         weights.append(__A						)
         biases.append(__A						)

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

       UpperCAmelCase	:							Optional[int]						=			hidden.new_empty((head_logit.size(0						), self.n_token)						)
       UpperCAmelCase	:							Dict						=			nn.functional.log_softmax(__A,		dim=1						)

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

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

           UpperCAmelCase	:							Tuple						=			self._compute_logit(__A,		__A,		__A,		__A						)
           UpperCAmelCase	:							List[Any]						=			nn.functional.log_softmax(__A,		dim=1						)

           UpperCAmelCase	:							Optional[int]						=			head_logprob[:, -i] + tail_logprob_i
           UpperCAmelCase	:							Optional[Any]						=			logprob_i

       return out





