





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_    =				logging.get_logger(__name__)

lowerCAmelCase_    =				'''▁'''

lowerCAmelCase_    =				{'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

lowerCAmelCase_    =				{
    '''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_    =				{'''vinai/bartpho-syllable''': 1_0_2_4}



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Any       			=      VOCAB_FILES_NAMES
			lowerCamelCase_     :     Optional[Any]       			=      PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_     :     Tuple       			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_     :     Any       			=      ['''input_ids''', '''attention_mask''']






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


							'''simple docstring'''



							snake_case_						:    List[str]				=  AddedToken(__magic_name__					,							lstrip=__magic_name__					,							rstrip=__magic_name__    ) if isinstance(__magic_name__					,							__magic_name__    ) else mask_token

							snake_case_						:    List[str]				=  {} if sp_model_kwargs is None else sp_model_kwargs

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

							snake_case_						:    Union[str, Any]				=  vocab_file
							snake_case_						:    List[str]				=  monolingual_vocab_file
							snake_case_						:    List[Any]				=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(str(__magic_name__    )    )

							# Load the reduced vocab

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

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






			def __getstate__(self    )     ->   List[Any]:


							'''simple docstring'''



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






			def __setstate__(self					,							__magic_name__    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    int				=  d

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

							snake_case_						:    Optional[Any]				=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__ = None    )     ->   List[int]:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__ = None					,							__magic_name__ = False    )     ->   List[int]:


							'''simple docstring'''



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

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






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__ = None    )     ->   List[int]:


							'''simple docstring'''



							snake_case_						:    str				=  [self.sep_token_id]
							snake_case_						:    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    )     ->   Optional[Any]:


							'''simple docstring'''



							return len(self.fairseq_ids_to_tokens    )






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


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  {self.convert_ids_to_tokens(__magic_name__    ): i for i in range(self.vocab_size    )}
							vocab.update(self.added_tokens_encoder    )
							return vocab






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   List[str]:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   List[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					,							__magic_name__    )     ->   Optional[Any]:


							'''simple docstring'''



							return self.fairseq_ids_to_tokens[index]






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Dict				=  ''''''.join(__magic_name__    ).replace(__magic_name__					,							''' '''    ).strip()
							return out_string






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__ = None    )     ->   Tuple[str]:


							'''simple docstring'''



							if not os.path.isdir(__magic_name__    ):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
											return
							snake_case_						:    int				=  os.path.join(
							    __magic_name__					,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
							snake_case_						:    List[str]				=  os.path.join(
							    __magic_name__					,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']					,							)

							if os.path.abspath(self.vocab_file    ) != os.path.abspath(__magic_name__    ) and os.path.isfile(self.vocab_file    ):
											copyfile(self.vocab_file					,							__magic_name__    )
							elif not os.path.isfile(self.vocab_file    ):
											with open(__magic_name__					,							'''wb'''    ) as fi:
															snake_case_						:    int				=  self.sp_model.serialized_model_proto()
															fi.write(__magic_name__    )

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

							return out_vocab_file, out_monolingual_vocab_file







lowerCAmelCase_    =				{
    "joule": 1.0,
    "kilojoule": 1_0_0_0,
    "megajoule": 1_0_0_0_0_0_0,
    "gigajoule": 1_0_0_0_0_0_0_0_0_0,
    "wattsecond": 1.0,
    "watthour": 3_6_0_0,
    "kilowatthour": 3_6_0_0_0_0_0,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_1_8_6.8,
    "kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
    "electronvolt": 1.602_176_634e-19,
    "britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
    "footpound": 1.355_818,
}






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						float:






				"""simple docstring"""


				if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
								snake_case_						:    str				=  (
								    f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
								    f'''Valid values are: {", ".join(_UpperCamelCase )}'''
								)
								raise ValueError(_UpperCamelCase )
				return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()
