'''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__  :						Any					=      logging.get_logger(__name__)

UpperCamelCase__  :						Optional[int]					=      '▁'

UpperCamelCase__  :						List[str]					=      {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

UpperCamelCase__  :						Any					=      {
    '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__  :						List[Any]					=      {'vinai/bartpho-syllable': 1_024}


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Tuple   				= VOCAB_FILES_NAMES
      UpperCAmelCase_   :   List[str]   				= PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_   :   Any   				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_   :   List[Any]   				= ['''input_ids''', '''attention_mask''']




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



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	AddedToken(lowerCamelCase_       ,lstrip=lowerCamelCase_       ,rstrip=lowerCamelCase_				) if isinstance(lowerCamelCase_       ,lowerCamelCase_				) else mask_token

         UpperCAmelCase__				:					Optional[int] 					=	{} 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_       ,)

         UpperCAmelCase__				:					Any 					=	vocab_file
         UpperCAmelCase__				:					Any 					=	monolingual_vocab_file
         UpperCAmelCase__				:					int 					=	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.Load(str(lowerCamelCase_				)				)

         # Load the reduced vocab

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

         UpperCAmelCase__				:					Any 					=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}




      def __getstate__(					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.__dict__.copy()
         UpperCAmelCase__				:					Optional[int] 					=	None
         UpperCAmelCase__				:					str 					=	self.sp_model.serialized_model_proto()
         return state




      def __setstate__(					self       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	d

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

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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = None				)      ->      List[int]:



         '''simple docstring'''



         if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCAmelCase__				:					int 					=	[self.cls_token_id]
         UpperCAmelCase__				:					List[str] 					=	[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = None       ,lowerCamelCase_ = False				)      ->      List[int]:



         '''simple docstring'''



         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 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = None				)      ->      List[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	[self.sep_token_id]
         UpperCAmelCase__				:					Dict 					=	[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 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return len(self.fairseq_ids_to_tokens				)




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	{self.convert_ids_to_tokens(lowerCamelCase_				): i for i in range(self.vocab_size				)}
         vocab.update(self.added_tokens_encoder				)
         return vocab




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      Optional[Any]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         return self.fairseq_ids_to_tokens[index]




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	''''''.join(lowerCamelCase_				).replace(lowerCamelCase_       ,''' '''				).strip()
         return out_string




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = None				)      ->      Tuple[str]:



         '''simple docstring'''



         if not os.path.isdir(lowerCamelCase_				):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
            return
         UpperCAmelCase__				:					Optional[Any] 					=	os.path.join(
             lowerCamelCase_       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
         UpperCAmelCase__				:					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:
               UpperCAmelCase__				:					List[str] 					=	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'''

from math import factorial, radians







def __UpperCamelCase( _A       :					float	,      _A       :					int = 18	,      _A       :					int = 10     ):
   '''simple docstring'''
   UpperCAmelCase__				:					int 					=	angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)

   # Converting from degrees to radians
   UpperCAmelCase__				:					Dict 					=	radians(_A     )

   UpperCAmelCase__				:					Union[str, Any] 					=	angle_in_radians
   UpperCAmelCase__				:					List[str] 					=	3
   UpperCAmelCase__				:					int 					=	-1

   for _ in range(_A     ):
      result += (b * (angle_in_radians**a)) / factorial(_A     )

      UpperCAmelCase__				:					List[str] 					=	-b  # One positive term and the next will be negative and so on...
      a += 2  # Increased by 2 for every term.

   return round(_A	,      _A     )


if __name__ == "__main__":
  __import__('doctest').testmod()




