



'''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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  "▁"

lowercase_		       =  {"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

lowercase_		       =  {
    "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",
    },
}

lowercase_		       =  {"vinai/bartpho-syllable": 1_024}






class __A			(  __snake_case						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       str          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       Optional[Any]          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       Optional[Any]          =       ["""input_ids""", """attention_mask"""]



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


      """simple docstring"""



      _a      =  AddedToken(A ,   lstrip=A ,   rstrip=A       ) if isinstance(A ,   A       ) else mask_token

      _a      =  {} if sp_model_kwargs is None else sp_model_kwargs

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

      _a      =  vocab_file
      _a      =  monolingual_vocab_file
      _a      =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(str(A       )       )

      # 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(A       ) not in self.fairseq_tokens_to_ids:
                _a      =  cnt
                cnt += 1
      with open(A ,   '''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(A       ) 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       )    ->     Optional[int]:


      """simple docstring"""



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



 def __setstate__(self ,   A       )    ->     Optional[Any]:


      """simple docstring"""



      _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 					a__  (self ,   A ,   A = None       )    ->     Dict:


      """simple docstring"""



      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 					a__  (self ,   A ,   A = None ,   A = False       )    ->     Optional[Any]:


      """simple docstring"""



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

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



 def 					a__  (self ,   A ,   A = None       )    ->     List[Any]:


      """simple docstring"""



      _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 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      return len(self.fairseq_ids_to_tokens       )



 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



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



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



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



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



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



 def 					a__  (self ,   A       )    ->     int:


      """simple docstring"""



      return self.fairseq_ids_to_tokens[index]



 def 					a__  (self ,   A       )    ->     Dict:


      """simple docstring"""



      _a      =  ''.join(A       ).replace(A ,   ''' '''       ).strip()
      return out_string



 def 					a__  (self ,   A ,   A = None       )    ->     Union[str, Any]:


      """simple docstring"""



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

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

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

      return out_vocab_file, out_monolingual_vocab_file






from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       int														=     """switch_transformers"""
    __lowerCamelCase						:       Optional[Any]														=     ["""past_key_values"""]
    __lowerCamelCase						:       Any														=     {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}





    def __init__(      self						,   a=3_2128						,   a=768						,   a=64						,   a=2048						,   a=64						,   a=12						,   a=3						,   a=12						,   a=3						,   a=12						,   a=8						,   a=False						,   a=0.01						,   a="float32"						,   a=False						,   a=32						,   a=128						,   a=0.1						,   a=1e-6						,   a=0.001						,   a=0.001						,   a=1.0						,   a="relu"						,   a=True						,   a=False						,   a=True						,   a=0						,   a=1						,   **a						,   ):
         lowercase__    :   Optional[int]      =       vocab_size
         lowercase__    :   List[Any]      =       d_model
         lowercase__    :   List[Any]      =       d_kv
         lowercase__    :   Any      =       d_ff

         lowercase__    :   Optional[int]      =       num_sparse_encoder_layers

         lowercase__    :   Tuple      =       num_layers
         lowercase__    :   Optional[Any]      =       (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         lowercase__    :   str      =       num_sparse_decoder_layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_encoder_layers > 0:
              lowercase__    :   Optional[Any]      =       self.num_layers // self.num_sparse_encoder_layers
         else:
              lowercase__    :   Optional[int]      =       self.num_layers  # HACK: this will create 0 sparse layers

         # This tells us, each how many encoder layer we'll have to set a sparse layer.
         if self.num_sparse_decoder_layers > 0:
              lowercase__    :   Union[str, Any]      =       self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
              lowercase__    :   int      =       self.num_decoder_layers  # HACK: this will create 0 sparse layers

         lowercase__    :   List[Any]      =       num_heads
         lowercase__    :   Union[str, Any]      =       num_experts
         lowercase__    :   str      =       expert_capacity
         lowercase__    :   List[Any]      =       router_bias
         lowercase__    :   Optional[int]      =       router_jitter_noise
         if router_dtype not in ["float32", "float16", "bfloat16"]:
              raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""")
         lowercase__    :   str      =       router_dtype

         lowercase__    :   Optional[int]      =       router_ignore_padding_tokens
         lowercase__    :   int      =       relative_attention_num_buckets
         lowercase__    :   Optional[Any]      =       relative_attention_max_distance

         lowercase__    :   List[str]      =       dropout_rate
         lowercase__    :   str      =       layer_norm_epsilon
         lowercase__    :   int      =       initializer_factor
         lowercase__    :   int      =       feed_forward_proj
         lowercase__    :   Dict      =       use_cache
         lowercase__    :   int      =       add_router_probs

         lowercase__    :   int      =       router_z_loss_coef
         lowercase__    :   List[Any]      =       router_aux_loss_coef

         lowercase__    :   int      =       self.feed_forward_proj.split('-')
         lowercase__    :   Optional[int]      =       act_info[-1]
         lowercase__    :   Dict      =       act_info[0] == 'gated'

         if len(a) > 1 and act_info[0] != "gated" or len(a) > 2:
              raise ValueError(
                  f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                  'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                  '\'gated-gelu\' or \'relu\'')

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
              lowercase__    :   Optional[int]      =       'gelu_new'

         super().__init__(
             pad_token_id=a						,   eos_token_id=a						,   is_encoder_decoder=a						,   **a						,   )

