
'''simple docstring'''


import tensorflow as tf

from ...tf_utils import shape_list


class 						lowercase__	(  tf.keras.layers.Layer   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__=1 ,   lowerCamelCase__=False ,   **lowerCamelCase__   ):

      '''simple docstring'''







      super().__init__(**lowerCamelCase__   )

      UpperCamelCase       =  vocab_size
      UpperCamelCase       =  d_embed
      UpperCamelCase       =  d_proj

      UpperCamelCase       =  cutoffs + [vocab_size]
      UpperCamelCase       =  [0] + self.cutoffs
      UpperCamelCase       =  div_val

      UpperCamelCase       =  self.cutoffs[0]
      UpperCamelCase       =  len(self.cutoffs   ) - 1
      UpperCamelCase       =  self.shortlist_size + self.n_clusters
      UpperCamelCase       =  keep_order

      UpperCamelCase       =  []
      UpperCamelCase       =  []







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if self.n_clusters > 0:
         UpperCamelCase       =  self.add_weight(
             shape=(self.n_clusters, self.d_embed) ,   initializer='''zeros''' ,   trainable=lowerCamelCase__ ,   name='''cluster_weight'''   )
         UpperCamelCase       =  self.add_weight(
             shape=(self.n_clusters,) ,   initializer='''zeros''' ,   trainable=lowerCamelCase__ ,   name='''cluster_bias'''   )

      if self.div_val == 1:
         for i in range(len(self.cutoffs   )   ):
            if self.d_proj != self.d_embed:
               UpperCamelCase       =  self.add_weight(
                   shape=(self.d_embed, self.d_proj) ,   initializer='''zeros''' ,   trainable=lowerCamelCase__ ,   name=f'out_projs_._{i}' ,   )
               self.out_projs.append(lowerCamelCase__   )
            else:
               self.out_projs.append(lowerCamelCase__   )
            UpperCamelCase       =  self.add_weight(
                shape=(self.vocab_size, self.d_embed) ,   initializer='''zeros''' ,   trainable=lowerCamelCase__ ,   name=f'out_layers_._{i}_._weight' ,   )
            UpperCamelCase       =  self.add_weight(
                shape=(self.vocab_size,) ,   initializer='''zeros''' ,   trainable=lowerCamelCase__ ,   name=f'out_layers_._{i}_._bias' ,   )
            self.out_layers.append((weight, bias)   )
      else:
         for i in range(len(self.cutoffs   )   ):
            UpperCamelCase     , UpperCamelCase       =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
            UpperCamelCase       =  self.d_embed // (self.div_val**i)

            UpperCamelCase       =  self.add_weight(
                shape=(d_emb_i, self.d_proj) ,   initializer='''zeros''' ,   trainable=lowerCamelCase__ ,   name=f'out_projs_._{i}'   )
            self.out_projs.append(lowerCamelCase__   )
            UpperCamelCase       =  self.add_weight(
                shape=(r_idx - l_idx, d_emb_i) ,   initializer='''zeros''' ,   trainable=lowerCamelCase__ ,   name=f'out_layers_._{i}_._weight' ,   )
            UpperCamelCase       =  self.add_weight(
                shape=(r_idx - l_idx,) ,   initializer='''zeros''' ,   trainable=lowerCamelCase__ ,   name=f'out_layers_._{i}_._bias' ,   )
            self.out_layers.append((weight, bias)   )
      super().build(lowerCamelCase__   )







   @staticmethod
   def       UpperCAmelCase				(			lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__=None   ):

      '''simple docstring'''







      UpperCamelCase       =  x
      if proj is not None:
         UpperCamelCase       =  tf.einsum('''ibd,ed->ibe''' ,   lowerCamelCase__ ,   lowerCamelCase__   )
      return tf.einsum('''ibd,nd->ibn''' ,   lowerCamelCase__ ,   lowerCamelCase__   ) + b







   @staticmethod
   def       UpperCAmelCase				(			lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  shape_list(lowerCamelCase__   )
      UpperCamelCase       =  tf.range(lp_size[0] ,   dtype=target.dtype   )
      UpperCamelCase       =  tf.stack([r, target] ,   1   )
      return tf.gather_nd(lowerCamelCase__ ,   lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__=True ,   lowerCamelCase__=False   ):

      '''simple docstring'''







      UpperCamelCase       =  0
      if self.n_clusters == 0:
         UpperCamelCase       =  self._logit(lowerCamelCase__ ,   self.out_layers[0][0] ,   self.out_layers[0][1] ,   self.out_projs[0]   )
         if target is not None:
            UpperCamelCase       =  tf.nn.sparse_softmax_cross_entropy_with_logits(labels=lowerCamelCase__ ,   logits=lowerCamelCase__   )
         UpperCamelCase       =  tf.nn.log_softmax(lowerCamelCase__ ,   axis=-1   )
      else:
         UpperCamelCase       =  shape_list(lowerCamelCase__   )
         UpperCamelCase       =  []
         UpperCamelCase       =  tf.zeros(hidden_sizes[:2]   )
         for i in range(len(self.cutoffs   )   ):
            UpperCamelCase     , UpperCamelCase       =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
            if target is not None:
               UpperCamelCase       =  (target >= l_idx) & (target < r_idx)
               UpperCamelCase       =  tf.where(lowerCamelCase__   )
               UpperCamelCase       =  tf.boolean_mask(lowerCamelCase__ ,   lowerCamelCase__   ) - l_idx

            if self.div_val == 1:
               UpperCamelCase       =  self.out_layers[0][0][l_idx:r_idx]
               UpperCamelCase       =  self.out_layers[0][1][l_idx:r_idx]
            else:
               UpperCamelCase       =  self.out_layers[i][0]
               UpperCamelCase       =  self.out_layers[i][1]

            if i == 0:
               UpperCamelCase       =  tf.concat([cur_W, self.cluster_weight] ,   0   )
               UpperCamelCase       =  tf.concat([cur_b, self.cluster_bias] ,   0   )

               UpperCamelCase       =  self._logit(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   self.out_projs[0]   )
               UpperCamelCase       =  tf.nn.log_softmax(lowerCamelCase__   )
               out.append(head_logprob[..., : self.cutoffs[0]]   )
               if target is not None:
                  UpperCamelCase       =  tf.boolean_mask(lowerCamelCase__ ,   lowerCamelCase__   )
                  UpperCamelCase       =  self._gather_logprob(lowerCamelCase__ ,   lowerCamelCase__   )
            else:
               UpperCamelCase       =  self._logit(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   self.out_projs[i]   )
               UpperCamelCase       =  tf.nn.log_softmax(lowerCamelCase__   )
               UpperCamelCase       =  self.cutoffs[0] + i - 1  # No probability for the head cluster
               UpperCamelCase       =  head_logprob[..., cluster_prob_idx, None] + tail_logprob
               out.append(lowerCamelCase__   )
               if target is not None:
                  UpperCamelCase       =  tf.boolean_mask(lowerCamelCase__ ,   lowerCamelCase__   )
                  UpperCamelCase       =  tf.boolean_mask(lowerCamelCase__ ,   lowerCamelCase__   )
                  UpperCamelCase       =  self._gather_logprob(lowerCamelCase__ ,   lowerCamelCase__   )
                  cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
            if target is not None:
               loss += tf.scatter_nd(lowerCamelCase__ ,   -cur_logprob ,   shape_list(lowerCamelCase__   )   )
         UpperCamelCase       =  tf.concat(lowerCamelCase__ ,   axis=-1   )

      if target is not None:
         if return_mean:
            UpperCamelCase       =  tf.reduce_mean(lowerCamelCase__   )
         # Add the training-time loss value to the layer using `self.add_loss()`.
         self.add_loss(lowerCamelCase__   )

         # Log the loss as a metric (we could log arbitrary metrics,
         # including different metrics for training and inference.
         self.add_metric(lowerCamelCase__ ,   name=self.name ,   aggregation='''mean''' if return_mean else ''''''   )

      return out







'''simple docstring'''


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


snake_case_ :  Optional[int]     =					logging.get_logger(__name__)

snake_case_ :  int     =					'▁'

snake_case_ :  str     =					{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

snake_case_ :  Optional[Any]     =					{
    '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',
    },
}

snake_case_ :  Dict     =					{'vinai/bartpho-syllable': 1_024}


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							VOCAB_FILES_NAMES
   _snake_case		   =							PRETRAINED_VOCAB_FILES_MAP
   _snake_case		   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _snake_case		   =							['''input_ids''', '''attention_mask''']







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__="<s>" ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="<s>" ,   lowerCamelCase__="<unk>" ,   lowerCamelCase__="<pad>" ,   lowerCamelCase__="<mask>" ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  AddedToken(lowerCamelCase__ ,   lstrip=lowerCamelCase__ ,   rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__ ,   lowerCamelCase__   ) else mask_token

      UpperCamelCase       =  {} if sp_model_kwargs is None else sp_model_kwargs

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

      UpperCamelCase       =  vocab_file
      UpperCamelCase       =  monolingual_vocab_file
      UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(lowerCamelCase__   )   )

      # Load the reduced vocab

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

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







   def __getstate__(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.__dict__.copy()
      UpperCamelCase       =  None
      UpperCamelCase       =  self.sp_model.serialized_model_proto()
      return state







   def __setstate__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  d

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

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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







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

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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      UpperCamelCase       =  [self.sep_token_id]
      UpperCamelCase       =  [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   ):

      '''simple docstring'''







      return len(self.fairseq_ids_to_tokens   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  {self.convert_ids_to_tokens(lowerCamelCase__   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.sp_model.encode(lowerCamelCase__ ,   out_type=lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
      else:
         return self.unk_token_id







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.fairseq_ids_to_tokens[index]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  ''''''.join(lowerCamelCase__   ).replace(lowerCamelCase__ ,   ''' '''   ).strip()
      return out_string







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if not os.path.isdir(lowerCamelCase__   ):
         logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
         return
      UpperCamelCase       =  os.path.join(
          lowerCamelCase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
      UpperCamelCase       =  os.path.join(
          lowerCamelCase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file'''] ,   )

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

      if os.path.abspath(self.monolingual_vocab_file   ) != os.path.abspath(
          lowerCamelCase__   ) and os.path.isfile(self.monolingual_vocab_file   ):
         copyfile(self.monolingual_vocab_file ,   lowerCamelCase__   )
      elif not os.path.isfile(self.monolingual_vocab_file   ):
         with open(lowerCamelCase__ ,   '''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(lowerCamelCase__   )} \n'   )

      return out_vocab_file, out_monolingual_vocab_file





