


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class   __a      (   __UpperCamelCase				):
 __lowercase			:							Union[List[PIL.Image.Image], np.ndarray]
 __lowercase			:							Optional[List[bool]]


if is_transformers_available() and is_torch_available():
 from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline






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''': 10_24}





class   __a      (   __UpperCamelCase				):
 __lowercase			:							int       					=			VOCAB_FILES_NAMES
 __lowercase			:							str       					=			PRETRAINED_VOCAB_FILES_MAP
 __lowercase			:							str       					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase			:							Optional[int]       					=			['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'''
        # Mask token behave like a normal word, i.e. include the space before it
        lowercase__:       List[str]        =       AddedToken(lowerCAmelCase__    ,			lstrip=lowerCAmelCase__    ,			rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__    ,			lowerCAmelCase__			) else mask_token

        lowercase__:       Any        =       {} 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__    ,			)

        lowercase__:       Dict        =       vocab_file
        lowercase__:       str        =       monolingual_vocab_file
        lowercase__:       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
        lowercase__:       List[Any]        =       {}
        lowercase__:       Optional[int]        =       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:
                      lowercase__:       str        =       cnt
                      cnt += 1
        with open(lowerCAmelCase__    ,			'r'    ,			encoding='utf-8'			) as f:
               for line in f.readlines():
                      lowercase__:       Optional[Any]        =       line.strip().split()[0]
                      lowercase__:       Optional[Any]        =       len(self.fairseq_tokens_to_ids			)
        if str(lowerCAmelCase__			) not in self.fairseq_tokens_to_ids:
               lowercase__:       Optional[int]        =       len(self.fairseq_tokens_to_ids			)

        lowercase__:       Optional[Any]        =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def __getstate__(       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Tuple        =       self.__dict__.copy()
        lowercase__:       Tuple        =       None
        lowercase__:       Optional[Any]        =       self.sp_model.serialized_model_proto()
        return state
 def __setstate__(       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       d

        # for backward compatibility
        if not hasattr(self    ,			'sp_model_kwargs'			):
               lowercase__:       Union[str, Any]        =       {}

        lowercase__:       List[Any]        =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)
 def 		SCREAMING_SNAKE_CASE__       (       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]
        lowercase__:       Optional[int]        =       [self.cls_token_id]
        lowercase__:       Union[str, Any]        =       [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep
 def 		SCREAMING_SNAKE_CASE__       (       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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    List[int]:




        '''simple docstring'''
        lowercase__:       Dict        =       [self.sep_token_id]
        lowercase__:       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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return len(self.fairseq_ids_to_tokens			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       {self.convert_ids_to_tokens(lowerCAmelCase__			): i for i in range(self.vocab_size			)}
        vocab.update(self.added_tokens_encoder			)
        return vocab
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[str]:




        '''simple docstring'''
        return self.sp_model.encode(lowerCAmelCase__    ,			out_type=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        return self.fairseq_ids_to_tokens[index]
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       ''.join(lowerCAmelCase__			).replace(lowerCAmelCase__    ,			' '			).strip()
        return out_string
 def 		SCREAMING_SNAKE_CASE__       (       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
        lowercase__:       int        =       os.path.join(
            lowerCAmelCase__    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
        lowercase__:       List[str]        =       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:
                      lowercase__:       Optional[Any]        =       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


