

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


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  "▁"

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

lowercase_       		=  {
    "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",
    },
}

lowercase_       		=  {"vinai/bartpho-syllable": 1024}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							VOCAB_FILES_NAMES
    lowerCamelCase			=							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase			=							['input_ids', 'attention_mask']


    def __init__(      self :       Any,lowercase_ :       Union[str, Any],lowercase_ :       List[Any],lowercase_ :       Optional[int]="<s>",lowercase_ :       List[Any]="</s>",lowercase_ :       Any="</s>",lowercase_ :       Tuple="<s>",lowercase_ :       Optional[int]="<unk>",lowercase_ :       str="<pad>",lowercase_ :       Any="<mask>",lowercase_ :       Optional[Dict[str, Any]] = None,**lowercase_ :       Optional[Any],)->    None:





        '''simple docstring'''







        A__          =   AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else mask_token

        A__          =   {} if sp_model_kwargs is None else sp_model_kwargs

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

        A__          =   vocab_file
        A__          =   monolingual_vocab_file
        A__          =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(str(lowercase_		)		)

        # 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(lowercase_		) not in self.fairseq_tokens_to_ids:
                A__          =   cnt
                cnt += 1
        with open(lowercase_,'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(lowercase_		) 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 :       Optional[int]		)->    Optional[int]:





        '''simple docstring'''







        A__          =   self.__dict__.copy()
        A__          =   None
        A__          =   self.sp_model.serialized_model_proto()
        return state


    def __setstate__(      self :       Union[str, Any],lowercase_ :       Optional[int]		)->    str:





        '''simple docstring'''







        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],lowercase_ :       List[int],lowercase_ :       Optional[List[int]] = None		)->    List[int]:





        '''simple docstring'''







        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 :       int,lowercase_ :       List[int],lowercase_ :       Optional[List[int]] = None,lowercase_ :       bool = False		)->    List[int]:





        '''simple docstring'''







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

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


    def 						snake_case__     (      self :       Any,lowercase_ :       List[int],lowercase_ :       Optional[List[int]] = None		)->    List[int]:





        '''simple docstring'''







        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 :       List[Any]		)->    str:





        '''simple docstring'''







        return len(self.fairseq_ids_to_tokens		)


    def 						snake_case__     (      self :       int		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   {self.convert_ids_to_tokens(lowercase_		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab


    def 						snake_case__     (      self :       Dict,lowercase_ :       str		)->    List[str]:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       List[str],lowercase_ :       int		)->    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 						snake_case__     (      self :       Dict,lowercase_ :       str		)->    List[Any]:





        '''simple docstring'''







        return self.fairseq_ids_to_tokens[index]


    def 						snake_case__     (      self :       List[Any],lowercase_ :       List[str]		)->    str:





        '''simple docstring'''







        A__          =   ''.join(lowercase_		).replace(lowercase_,' '		).strip()
        return out_string


    def 						snake_case__     (      self :       List[str],lowercase_ :       str,lowercase_ :       Optional[str] = None		)->    Tuple[str]:





        '''simple docstring'''







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

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

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

        return out_vocab_file, out_monolingual_vocab_file




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			)							->      float:





    '''simple docstring'''






    A__          =   [redshift, radiation_density, matter_density, dark_energy]
    if any(p < 0 for p in parameters      ):
        raise ValueError('All input parameters must be positive'      )

    if any(p > 1 for p in parameters[1:4]      ):
        raise ValueError('Relative densities cannot be greater than one'      )
    else:
        A__          =   1 - (matter_density + radiation_density + dark_energy)

        A__          =   (
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        A__          =   hubble_constant * e_a ** (1 / 2)
        return hubble


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # demo LCDM approximation
     lowercase_       		=  0.3

     print(
         hubble_parameter(
             hubble_constant=68.3,
             radiation_density=1e-4,
             matter_density=matter_density,
             dark_energy=1 - matter_density,
             redshift=0,
         )
     )

