

"""simple docstring"""

from math import ceil



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Dict:
 lowerCAmelCase__   :				List[str] 			=				list(range(0					,					lowercase__   )   )

 lowerCAmelCase__   :				Any 			=				[item for sublist in list(device_map.values()   ) for item in sublist]

 # Duplicate check
 lowerCAmelCase__   :				List[Any] 			=				[]
 for i in device_map_blocks:
  if device_map_blocks.count(lowercase__   ) > 1 and i not in duplicate_blocks:
   duplicate_blocks.append(lowercase__   )
    # Missing blocks
 lowerCAmelCase__   :				Any 			=				[i for i in blocks if i not in device_map_blocks]
 lowerCAmelCase__   :				str 			=				[i for i in device_map_blocks if i not in blocks]

 if len(lowercase__   ) != 0:
  raise ValueError(
      "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
      " These attention blocks were specified more than once: " + str(lowercase__   )   )
 if len(lowercase__   ) != 0:
  raise ValueError(
      "There are attention blocks for this model that are not specified in the device_map. Add these attention "
      "blocks to a device on the device_map: " + str(lowercase__   )   )
 if len(lowercase__   ) != 0:
  raise ValueError(
      "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
      + str(lowercase__   )   )






def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						List[Any]:
 lowerCAmelCase__   :				Optional[Any] 			=				list(range(lowercase__   )   )
 lowerCAmelCase__   :				Any 			=				int(ceil(n_layers / len(lowercase__   )   )   )
 lowerCAmelCase__   :				str 			=				[layers[i : i + n_blocks] for i in range(0					,					lowercase__					,					lowercase__   )]

 return dict(zip(lowercase__					,					lowercase__   )   )









"""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


_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 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      int    =						VOCAB_FILES_NAMES
       __UpperCamelCase					:      List[str]    =						PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase					:      List[Any]    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase					:      int    =						['input_ids', 'attention_mask']






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





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				AddedToken(snake_case			,					lstrip=snake_case			,					rstrip=snake_case   ) if isinstance(snake_case			,					snake_case   ) else mask_token

        lowerCAmelCase__   :				Union[str, Any] 			=				{} if sp_model_kwargs is None else sp_model_kwargs

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

        lowerCAmelCase__   :				Union[str, Any] 			=				vocab_file
        lowerCAmelCase__   :				Optional[Any] 			=				monolingual_vocab_file
        lowerCAmelCase__   :				Optional[Any] 			=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
        self.sp_model.Load(str(snake_case   )   )

        # Load the reduced vocab

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

        lowerCAmelCase__   :				Union[str, Any] 			=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}






       def __getstate__(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.__dict__.copy()
        lowerCAmelCase__   :				str 			=				None
        lowerCAmelCase__   :				Union[str, Any] 			=				self.sp_model.serialized_model_proto()
        return state






       def __setstate__(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				d

        # for backward compatibility
        if not hasattr(self			,					"sp_model_kwargs"   ):
         lowerCAmelCase__   :				Any 			=				{}

        lowerCAmelCase__   :				Dict 			=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None			,					snake_case = False   ):





        """simple docstring"""

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

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				[self.sep_token_id]
        lowerCAmelCase__   :				Optional[int] 			=				[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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return len(self.fairseq_ids_to_tokens   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				{self.convert_ids_to_tokens(snake_case   ): i for i in range(self.vocab_size   )}
        vocab.update(self.added_tokens_encoder   )
        return vocab






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        return self.fairseq_ids_to_tokens[index]






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				"".join(snake_case   ).replace(snake_case			,					" "   ).strip()
        return out_string






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        if not os.path.isdir(snake_case   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
        lowerCAmelCase__   :				List[str] 			=				os.path.join(
            snake_case			,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )
        lowerCAmelCase__   :				Optional[Any] 			=				os.path.join(
            snake_case			,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"]			,					)

        if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file			,					snake_case   )
        elif not os.path.isfile(self.vocab_file   ):
         with open(snake_case			,					"wb"   ) as fi:
          lowerCAmelCase__   :				str 			=				self.sp_model.serialized_model_proto()
          fi.write(snake_case   )

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

        return out_vocab_file, out_monolingual_vocab_file






