


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


lowerCamelCase					:		Optional[Any]            =						logging.get_logger(__name__)

lowerCamelCase					:		Union[str, Any]            =						"""▁"""

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

lowerCamelCase					:		int            =						{
    """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""",
    },
}

lowerCamelCase					:		Optional[Any]            =						{"""vinai/bartpho-syllable""": 1_0_2_4}


class snake_case__			(    UpperCAmelCase__					):
       _lowerCAmelCase   =VOCAB_FILES_NAMES
       _lowerCAmelCase   =PRETRAINED_VOCAB_FILES_MAP
       _lowerCAmelCase   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowerCAmelCase   =['input_ids', 'attention_mask']




       def __init__( self				:    Dict    ,				_lowerCamelCase				:    Union[str, Any]    ,				_lowerCamelCase				:    List[Any]    ,				_lowerCamelCase				:    List[str]="<s>"    ,				_lowerCamelCase				:    List[Any]="</s>"    ,				_lowerCamelCase				:    str="</s>"    ,				_lowerCamelCase				:    List[Any]="<s>"    ,				_lowerCamelCase				:    Optional[int]="<unk>"    ,				_lowerCamelCase				:    List[str]="<pad>"    ,				_lowerCamelCase				:    Tuple="<mask>"    ,				_lowerCamelCase				:    str = None    ,				**_lowerCamelCase				:    Union[str, Any]    ,				):
              # Mask token behave like a normal word, i.e. include the space before it
              snake_case__ :       Optional[Any]	   =							AddedToken(lowerCamelCase__    ,				lstrip=lowerCamelCase__    ,				rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__    ,				lowerCamelCase__			) else mask_token

              snake_case__ :       Union[str, Any]	   =							{} 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__    ,				)

              snake_case__ :       Dict	   =							vocab_file
              snake_case__ :       Dict	   =							monolingual_vocab_file
              snake_case__ :       List[Any]	   =							spm.SentencePieceProcessor(**self.sp_model_kwargs			)
              self.sp_model.Load(str(lowerCamelCase__			)			)

              # Load the reduced vocab

              # Keep order of special tokens for backward compatibility
              snake_case__ :       Tuple	   =							{}
              snake_case__ :       int	   =							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:
                            snake_case__ :       List[Any]	   =							cnt
                            cnt += 1
              with open(lowerCamelCase__    ,				'r'    ,				encoding='utf-8'			) as f:
                     for line in f.readlines():
                            snake_case__ :       Tuple	   =							line.strip().split()[0]
                            snake_case__ :       Tuple	   =							len(self.fairseq_tokens_to_ids			)
              if str(lowerCamelCase__			) not in self.fairseq_tokens_to_ids:
                     snake_case__ :       Union[str, Any]	   =							len(self.fairseq_tokens_to_ids			)

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




       def __getstate__( self				:    Tuple			):
              snake_case__ :       List[str]	   =							self.__dict__.copy()
              snake_case__ :       Tuple	   =							None
              snake_case__ :       List[Any]	   =							self.sp_model.serialized_model_proto()
              return state




       def __setstate__( self				:    Optional[int]    ,				_lowerCamelCase				:    List[Any]			):
              snake_case__ :       Tuple	   =							d

              # for backward compatibility
              if not hasattr(self    ,				'sp_model_kwargs'			):
                     snake_case__ :       Union[str, Any]	   =							{}

              snake_case__ :       Union[str, Any]	   =							spm.SentencePieceProcessor(**self.sp_model_kwargs			)
              self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)




       def     UpperCAmelCase__    ( self				:    Union[str, Any]    ,				_lowerCamelCase				:    List[Any]    ,				_lowerCamelCase				:    Any = None			):

              if token_ids_a is None:
                     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              snake_case__ :       Union[str, Any]	   =							[self.cls_token_id]
              snake_case__ :       Dict	   =							[self.sep_token_id]
              return cls + token_ids_a + sep + sep + token_ids_a + sep




       def     UpperCAmelCase__    ( self				:    List[Any]    ,				_lowerCamelCase				:    Optional[int]    ,				_lowerCamelCase				:    Union[str, Any] = None    ,				_lowerCamelCase				:    Any = False			):

              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				:    Tuple    ,				_lowerCamelCase				:    Union[str, Any]    ,				_lowerCamelCase				:    Optional[int] = None			):
              snake_case__ :       int	   =							[self.sep_token_id]
              snake_case__ :       Any	   =							[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				:    Union[str, Any]			):
              return len(self.fairseq_ids_to_tokens			)




       def     UpperCAmelCase__    ( self				:    Optional[int]			):
              snake_case__ :       Optional[Any]	   =							{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				:    List[Any]    ,				_lowerCamelCase				:    Any			):
              return self.sp_model.encode(lowerCamelCase__    ,				out_type=lowerCamelCase__			)




       def     UpperCAmelCase__    ( self				:    Any    ,				_lowerCamelCase				:    List[str]			):
              if token in self.fairseq_tokens_to_ids:
                     return self.fairseq_tokens_to_ids[token]
              else:
                     return self.unk_token_id




       def     UpperCAmelCase__    ( self				:    Union[str, Any]    ,				_lowerCamelCase				:    Tuple			):
              return self.fairseq_ids_to_tokens[index]




       def     UpperCAmelCase__    ( self				:    List[Any]    ,				_lowerCamelCase				:    str			):
              snake_case__ :       int	   =							"".join(lowerCamelCase__			).replace(lowerCamelCase__    ,				' '			).strip()
              return out_string




       def     UpperCAmelCase__    ( self				:    List[str]    ,				_lowerCamelCase				:    Optional[int]    ,				_lowerCamelCase				:    Union[str, Any] = None			):
              if not os.path.isdir(lowerCamelCase__			):
                     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
                     return
              snake_case__ :       Dict	   =							os.path.join(
                  lowerCamelCase__    ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
              snake_case__ :       Dict	   =							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:
                            snake_case__ :       Any	   =							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






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a  :      Tuple            =    logging.get_logger(__name__)

a  :      List[str]            =    {
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Any          =    'xlm'
    SCREAMING_SNAKE_CASE:				Optional[int]          =    {
        'hidden_size': 'emb_dim',
        'num_attention_heads': 'n_heads',
        'num_hidden_layers': 'n_layers',
        'n_words': 'vocab_size',  # For backward compatibility
    }




    def __init__(    self       ,						lowerCamelCase__=30_145       ,						lowerCamelCase__=2_048       ,						lowerCamelCase__=12       ,						lowerCamelCase__=16       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=True       ,						lowerCamelCase__=False       ,						lowerCamelCase__=False       ,						lowerCamelCase__=False       ,						lowerCamelCase__=1       ,						lowerCamelCase__=True       ,						lowerCamelCase__=512       ,						lowerCamelCase__=2_048**-0.5       ,						lowerCamelCase__=1E-12       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=0       ,						lowerCamelCase__=1       ,						lowerCamelCase__=2       ,						lowerCamelCase__=3       ,						lowerCamelCase__=5       ,						lowerCamelCase__=True       ,						lowerCamelCase__="first"       ,						lowerCamelCase__=True       ,						lowerCamelCase__=None       ,						lowerCamelCase__=True       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=5       ,						lowerCamelCase__=5       ,						lowerCamelCase__=0       ,						lowerCamelCase__=0       ,						lowerCamelCase__=2       ,						lowerCamelCase__=0       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							Any       =	vocab_size
       lowerCAmelCase_:							int       =	emb_dim
       lowerCAmelCase_:							int       =	n_layers
       lowerCAmelCase_:							Optional[int]       =	n_heads
       lowerCAmelCase_:							Tuple       =	dropout
       lowerCAmelCase_:							Tuple       =	attention_dropout
       lowerCAmelCase_:							Tuple       =	gelu_activation
       lowerCAmelCase_:							Dict       =	sinusoidal_embeddings
       lowerCAmelCase_:							str       =	causal
       lowerCAmelCase_:							Tuple       =	asm
       lowerCAmelCase_:							Any       =	n_langs
       lowerCAmelCase_:							int       =	use_lang_emb
       lowerCAmelCase_:							Dict       =	layer_norm_eps
       lowerCAmelCase_:							Dict       =	bos_index
       lowerCAmelCase_:							Union[str, Any]       =	eos_index
       lowerCAmelCase_:							Optional[Any]       =	pad_index
       lowerCAmelCase_:							List[Any]       =	unk_index
       lowerCAmelCase_:							Dict       =	mask_index
       lowerCAmelCase_:							Any       =	is_encoder
       lowerCAmelCase_:							Dict       =	max_position_embeddings
       lowerCAmelCase_:							int       =	embed_init_std
       lowerCAmelCase_:							Optional[int]       =	init_std
       lowerCAmelCase_:							str       =	summary_type
       lowerCAmelCase_:							Union[str, Any]       =	summary_use_proj
       lowerCAmelCase_:							Dict       =	summary_activation
       lowerCAmelCase_:							List[Any]       =	summary_proj_to_labels
       lowerCAmelCase_:							List[str]       =	summary_first_dropout
       lowerCAmelCase_:							List[str]       =	start_n_top
       lowerCAmelCase_:							List[Any]       =	end_n_top
       lowerCAmelCase_:							Any       =	mask_token_id
       lowerCAmelCase_:							List[Any]       =	lang_id

       if "n_words" in kwargs:
          lowerCAmelCase_:							Union[str, Any]       =	kwargs["n_words"]

       super().__init__(pad_token_id=lowerCamelCase__       ,						bos_token_id=lowerCamelCase__       ,						**lowerCamelCase__ )





class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    @property
    def      _a    (    self ):
       if self.task == "multiple-choice":
          lowerCAmelCase_:							Optional[Any]       =	{0: "batch", 1: "choice", 2: "sequence"}
       else:
          lowerCAmelCase_:							Any       =	{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("token_type_ids", dynamic_axis),
           ] )