



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


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					"""▁"""

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

UpperCAmelCase_		 =					{
    """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""",
    },
}

UpperCAmelCase_		 =					{"""vinai/bartpho-syllable""": 1_0_2_4}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: int =  VOCAB_FILES_NAMES
 a__					: Tuple =  PRETRAINED_VOCAB_FILES_MAP
 a__					: Optional[int] =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 a__					: Tuple =  ["input_ids", "attention_mask"]

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

    _A      =       {} 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			,						)

    _A      =       vocab_file
    _A      =       monolingual_vocab_file
    _A      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(str(__lowerCAmelCase      )      )

    # 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(__lowerCAmelCase      ) not in self.fairseq_tokens_to_ids:
          _A      =       cnt
          cnt += 1
    with open(__lowerCAmelCase			,						'''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(__lowerCAmelCase      ) 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      )   ->	List[Any]:
    _A      =       self.__dict__.copy()
    _A      =       None
    _A      =       self.sp_model.serialized_model_proto()
    return state

 def __setstate__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Dict      )   ->	List[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     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = 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     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None			,						__lowerCAmelCase					:					bool = False      )   ->	List[int]:

    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     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = 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     snake_case_      (    self					:					Optional[int]      )   ->	Union[str, Any]:
    return len(self.fairseq_ids_to_tokens      )

 def     snake_case_      (    self					:					Dict      )   ->	Optional[Any]:
    _A      =       {self.convert_ids_to_tokens(__lowerCAmelCase      ): i for i in range(self.vocab_size      )}
    vocab.update(self.added_tokens_encoder      )
    return vocab

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

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Dict:
    if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
    else:
       return self.unk_token_id

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					Optional[int]      )   ->	List[str]:
    return self.fairseq_ids_to_tokens[index]

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	Tuple:
    _A      =       ''''''.join(__lowerCAmelCase      ).replace(__lowerCAmelCase			,						''' '''      ).strip()
    return out_string

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





"""simple docstring"""

import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_            =    '''scheduler_config.json'''
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						1
      __SCREAMING_SNAKE_CASE :				Dict				   =						2
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						3
      __SCREAMING_SNAKE_CASE :				Any				   =						4
      __SCREAMING_SNAKE_CASE :				Any				   =						5
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						6
      __SCREAMING_SNAKE_CASE :				str				   =						7
      __SCREAMING_SNAKE_CASE :				Any				   =						8
      __SCREAMING_SNAKE_CASE :				Tuple				   =						9
      __SCREAMING_SNAKE_CASE :				int				   =						1_0
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						1_1
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						1_2
      __SCREAMING_SNAKE_CASE :				Dict				   =						1_3
      __SCREAMING_SNAKE_CASE :				Optional[Any]				   =						1_4

@dataclass
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				torch.FloatTensor

class   _UpperCAmelCase				:
      __SCREAMING_SNAKE_CASE :				str				   =						SCHEDULER_CONFIG_NAME
      __SCREAMING_SNAKE_CASE :				Dict				   =						[]
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						True




      @classmethod
      def      a_   (				cls ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_=False ,							**lowercase_ ,							)    ->       List[Any]:
          UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase          =       cls.load_config(
              pretrained_model_name_or_path=lowercase_ ,							subfolder=lowercase_ ,							return_unused_kwargs=lowercase_ ,							return_commit_hash=lowercase_ ,							**lowercase_ ,							)
          return cls.from_config(lowercase_ ,							return_unused_kwargs=lowercase_ ,							**lowercase_				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ = False ,							**lowercase_				)    ->       str:
          self.save_config(save_directory=lowercase_ ,							push_to_hub=lowercase_ ,							**lowercase_				)




      @property
      def      a_   (				self				)    ->       Union[str, Any]:
          return self._get_compatibles()




      @classmethod
      def      a_   (				cls				)    ->       Any:
          UpperCAmelCase          =       list(set([cls.__name__] + cls._compatibles				)				)
          UpperCAmelCase          =       importlib.import_module(__name__.split('.'				)[0]				)
          UpperCAmelCase          =       [
              getattr(lowercase_ ,							lowercase_				) for c in compatible_classes_str if hasattr(lowercase_ ,							lowercase_				)
          ]
          return compatible_classes



