



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a_      :      Dict    =     logging.get_logger(__name__)

a_      :      Any    =     '▁'

a_      :      Union[str, Any]    =     {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

a_      :      List[str]    =     {
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

a_      :      Dict    =     {'vinai/bartpho-syllable': 10_24}
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Dict      						=					VOCAB_FILES_NAMES
 _lowercase   :    List[Any]      						=					PRETRAINED_VOCAB_FILES_MAP
 _lowercase   :    Optional[int]      						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowercase   :    int      						=					['''input_ids''', '''attention_mask''']

 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE="<s>"		,				SCREAMING_SNAKE_CASE="</s>"		,				SCREAMING_SNAKE_CASE="</s>"		,				SCREAMING_SNAKE_CASE="<s>"		,				SCREAMING_SNAKE_CASE="<unk>"		,				SCREAMING_SNAKE_CASE="<pad>"		,				SCREAMING_SNAKE_CASE="<mask>"		,				SCREAMING_SNAKE_CASE = None		,				**SCREAMING_SNAKE_CASE		,				) ->			None:
  # Mask token behave like a normal word, i.e. include the space before it
  a__												= AddedToken(SCREAMING_SNAKE_CASE		,				lstrip=SCREAMING_SNAKE_CASE		,				rstrip=SCREAMING_SNAKE_CASE      ) if isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) else mask_token

  a__												= {} if sp_model_kwargs is None else sp_model_kwargs

  super().__init__(
      bos_token=SCREAMING_SNAKE_CASE		,				eos_token=SCREAMING_SNAKE_CASE		,				unk_token=SCREAMING_SNAKE_CASE		,				sep_token=SCREAMING_SNAKE_CASE		,				cls_token=SCREAMING_SNAKE_CASE		,				pad_token=SCREAMING_SNAKE_CASE		,				mask_token=SCREAMING_SNAKE_CASE		,				sp_model_kwargs=self.sp_model_kwargs		,				**SCREAMING_SNAKE_CASE		,				)

  a__												= vocab_file
  a__												= monolingual_vocab_file
  a__												= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
  self.sp_model.Load(str(SCREAMING_SNAKE_CASE      )      )

  # Load the reduced vocab

  # Keep order of special tokens for backward compatibility
  a__												= {}
  a__												= 0
  for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
   if str(SCREAMING_SNAKE_CASE      ) not in self.fairseq_tokens_to_ids:
    a__												= cnt
    cnt += 1
  with open(SCREAMING_SNAKE_CASE		,				'''r'''		,				encoding='''utf-8'''      ) as f:
   for line in f.readlines():
    a__												= line.strip().split()[0]
    a__												= len(self.fairseq_tokens_to_ids      )
  if str(SCREAMING_SNAKE_CASE      ) not in self.fairseq_tokens_to_ids:
   a__												= len(self.fairseq_tokens_to_ids      )

  a__												= {v: k for k, v in self.fairseq_tokens_to_ids.items()}

 def __getstate__(	self      ) ->			Any:
  a__												= self.__dict__.copy()
  a__												= None
  a__												= self.sp_model.serialized_model_proto()
  return state

 def __setstate__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  a__												= d

  # for backward compatibility
  if not hasattr(self		,				'''sp_model_kwargs'''      ):
   a__												= {}

  a__												= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
  self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None      ) ->			List[int]:

  if token_ids_a is None:
   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  a__												= [self.cls_token_id]
  a__												= [self.sep_token_id]
  return cls + token_ids_a + sep + sep + token_ids_a + sep

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = False      ) ->			List[int]:

  if already_has_special_tokens:
   return super().get_special_tokens_mask(
       token_ids_a=SCREAMING_SNAKE_CASE		,				token_ids_a=SCREAMING_SNAKE_CASE		,				already_has_special_tokens=SCREAMING_SNAKE_CASE      )

  if token_ids_a is None:
   return [1] + ([0] * len(SCREAMING_SNAKE_CASE      )) + [1]
  return [1] + ([0] * len(SCREAMING_SNAKE_CASE      )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE      )) + [1]

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None      ) ->			List[int]:
  a__												= [self.sep_token_id]
  a__												= [self.cls_token_id]

  if token_ids_a is None:
   return len(cls + token_ids_a + sep      ) * [0]
  return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]

 @property
 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  return len(self.fairseq_ids_to_tokens      )

 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  a__												= {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE      ): i for i in range(self.vocab_size      )}
  vocab.update(self.added_tokens_encoder      )
  return vocab

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  return self.sp_model.encode(SCREAMING_SNAKE_CASE		,				out_type=SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			int:
  if token in self.fairseq_tokens_to_ids:
   return self.fairseq_tokens_to_ids[token]
  else:
   return self.unk_token_id

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Any:
  return self.fairseq_ids_to_tokens[index]

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[Any]:
  a__												= ''''''.join(SCREAMING_SNAKE_CASE      ).replace(SCREAMING_SNAKE_CASE		,				''' '''      ).strip()
  return out_string



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None      ) ->			Tuple[str]:
  if not os.path.isdir(SCREAMING_SNAKE_CASE      ):
   logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
   return
  a__												= os.path.join(
      SCREAMING_SNAKE_CASE		,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
  a__												= os.path.join(
      SCREAMING_SNAKE_CASE		,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']		,				)

  if os.path.abspath(self.vocab_file      ) != os.path.abspath(SCREAMING_SNAKE_CASE      ) and os.path.isfile(self.vocab_file      ):
   copyfile(self.vocab_file		,				SCREAMING_SNAKE_CASE      )
  elif not os.path.isfile(self.vocab_file      ):
   with open(SCREAMING_SNAKE_CASE		,				'''wb'''      ) as fi:
    a__												= self.sp_model.serialized_model_proto()
    fi.write(SCREAMING_SNAKE_CASE      )

  if os.path.abspath(self.monolingual_vocab_file      ) != os.path.abspath(
      SCREAMING_SNAKE_CASE      ) and os.path.isfile(self.monolingual_vocab_file      ):
   copyfile(self.monolingual_vocab_file		,				SCREAMING_SNAKE_CASE      )
  elif not os.path.isfile(self.monolingual_vocab_file      ):
   with open(SCREAMING_SNAKE_CASE		,				'''w'''		,				encoding='''utf-8'''      ) as fp:
    for token in self.fairseq_tokens_to_ids:
     if token not in self.all_special_tokens:
      fp.write(f"{str(SCREAMING_SNAKE_CASE      )} \n"      )

  return out_vocab_file, out_monolingual_vocab_file










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





