



"""simple docstring"""




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

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

_lowerCAmelCase							:   Dict				=				{"vocab_file": "sentencepiece.bpe.model"}

_lowerCAmelCase							:   Optional[int]				=				{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

_lowerCAmelCase							:   str				=				{
    "facebook/xglm-564M": 20_48,
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Dict						=					VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE		:  str						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE		:  Tuple						=					['input_ids', 'attention_mask']



      def __init__(	self	:      Tuple	,      A	:      Optional[Any]	,      A	:      Dict="<s>"	,      A	:      int="</s>"	,      A	:      Dict="</s>"	,      A	:      int="<s>"	,      A	:      Optional[int]="<unk>"	,      A	:      Any="<pad>"	,      A	:      Optional[Dict[str, Any]] = None	,      **A	:      Tuple	,      ):
           _UpperCAmelCase     :      List[Any]														=       {} if sp_model_kwargs is None else sp_model_kwargs

           # Compatibility with the original tokenizer
           _UpperCAmelCase     :      List[Any]														=       7
           _UpperCAmelCase     :      Tuple														=       [f'<madeupword{i}>' for i in range(self.num_madeup_words		)]

           _UpperCAmelCase     :      List[str]														=       kwargs.get("additional_special_tokens"	,      []		)
           kwargs["additional_special_tokens"] += [
               word for word in madeup_words if word not in kwargs["additional_special_tokens"]
           ]

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

           _UpperCAmelCase     :      Tuple														=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
           self.sp_model.Load(str(A		)		)
           _UpperCAmelCase     :      Optional[Any]														=       vocab_file

           # Original fairseq vocab and spm vocab must be "aligned":
           # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
           # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
           # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
           # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

           # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
           _UpperCAmelCase     :      List[str]														=       1

           # Mimic fairseq token-to-id alignment for the first 4 token
           _UpperCAmelCase     :      List[Any]														=       {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

           _UpperCAmelCase     :      Any														=       len(self.sp_model		)
           _UpperCAmelCase     :      List[str]														=       {f'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words		)}
           self.fairseq_tokens_to_ids.update(A		)

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



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



      def __setstate__(	self	:      Optional[int]	,      A	:      Optional[Any]		):
           _UpperCAmelCase     :      str														=       d

           # for backward compatibility
           if not hasattr(self	,      "sp_model_kwargs"		):
                _UpperCAmelCase     :      List[Any]														=       {}

           _UpperCAmelCase     :      List[str]														=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
           self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)



      def 		snake_case_   (	self	:      List[str]	,      A	:      List[int]	,      A	:      Optional[List[int]] = None		):

           if token_ids_a is None:
                return [self.sep_token_id] + token_ids_a
           _UpperCAmelCase     :      List[Any]														=       [self.sep_token_id]
           return sep + token_ids_a + sep + sep + token_ids_a



      def 		snake_case_   (	self	:      int	,      A	:      List[int]	,      A	:      Optional[List[int]] = None	,      A	:      bool = False		):

           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		))
           return [1] + ([0] * len(A		)) + [1, 1] + ([0] * len(A		))



      def 		snake_case_   (	self	:      Any	,      A	:      List[int]	,      A	:      Optional[List[int]] = None		):
           _UpperCAmelCase     :      Optional[int]														=       [self.sep_token_id]

           if token_ids_a is None:
                return len(sep + token_ids_a		) * [0]
           return len(sep + token_ids_a + sep + sep + token_ids_a		) * [0]



      @property
      def 		snake_case_   (	self	:      List[str]		):
           return len(self.sp_model		) + self.fairseq_offset + self.num_madeup_words



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      Optional[Any]														=       {self.convert_ids_to_tokens(A		): i for i in range(self.vocab_size		)}
           vocab.update(self.added_tokens_encoder		)
           return vocab



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



      def 		snake_case_   (	self	:      Any	,      A	:      List[str]		):
           if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
           _UpperCAmelCase     :      Optional[int]														=       self.sp_model.PieceToId(A		)

           # Need to return unknown token if the SP model returned 0
           return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



      def 		snake_case_   (	self	:      Optional[Any]	,      A	:      Union[str, Any]		):
           if index in self.fairseq_ids_to_tokens:
                return self.fairseq_ids_to_tokens[index]
           return self.sp_model.IdToPiece(index - self.fairseq_offset		)



      def 		snake_case_   (	self	:      Optional[Any]	,      A	:      List[Any]		):
           _UpperCAmelCase     :      int														=       "".join(A		).replace(A	,      " "		).strip()
           return out_string



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      str	,      A	:      Optional[str] = None		):
           if not os.path.isdir(A		):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
                return
           _UpperCAmelCase     :      List[Any]														=       os.path.join(
               A	,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["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:
                     _UpperCAmelCase     :      Any														=       self.sp_model.serialized_model_proto()
                     fi.write(A		)

           return (out_vocab_file,)





"""simple docstring"""




def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  bool:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       0
     for ch in input_str:
          _UpperCAmelCase     :      Optional[int]														=       ord(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      int														=       pow(2		,    SCREAMING_SNAKE_CASE__							)

          # If we already turned on bit for current character's unicode
          if bitmap >> ch_unicode & 1 == 1:
               return False
          bitmap |= ch_bit_index_on
     return True


if __name__ == "__main__":
 import doctest

 doctest.testmod()
