


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


A			:						str        =  logging.get_logger(__name__)

A			:						List[str]        =  '▁'

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

A			:						str        =  {
    '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',
    },
}

A			:						Dict        =  {'vinai/bartpho-syllable': 1_0_2_4}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






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

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




							"""simple docstring"""


							lowercase__	     =						AddedToken(_UpperCAmelCase ,   lstrip=_UpperCAmelCase ,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else mask_token

							lowercase__	     =						{} if sp_model_kwargs is None else sp_model_kwargs

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

							lowercase__	     =						vocab_file
							lowercase__	     =						monolingual_vocab_file
							lowercase__	     =						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(str(_UpperCAmelCase						)						)

							# Load the reduced vocab

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

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

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




							"""simple docstring"""


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

					def __setstate__(self       :		Optional[int] ,   _UpperCAmelCase       :		List[str]						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						d

							# for backward compatibility
							if not hasattr(self ,   """sp_model_kwargs"""						):
									lowercase__	     =						{}

							lowercase__	     =						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		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]
							lowercase__	     =						[self.cls_token_id]
							lowercase__	     =						[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep

					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		Optional[List[int]] = None ,   _UpperCAmelCase       :		bool = False						)		->  List[int]:




							"""simple docstring"""


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

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

					def     lowerCamelCase__	(self       :		str ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		Optional[List[int]] = None						)		->  List[int]:




							"""simple docstring"""


							lowercase__	     =						[self.sep_token_id]
							lowercase__	     =						[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						)		->  str:




							"""simple docstring"""


							return len(self.fairseq_ids_to_tokens						)

					def     lowerCamelCase__	(self       :		Dict						)		->  Any:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		str						)		->  List[str]:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		Dict ,   _UpperCAmelCase       :		List[Any]						)		->  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       :		Tuple ,   _UpperCAmelCase       :		Tuple						)		->  Dict:




							"""simple docstring"""


							return self.fairseq_ids_to_tokens[index]

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		Tuple						)		->  str:




							"""simple docstring"""


							lowercase__	     =						"""""".join(_UpperCAmelCase						).replace(_UpperCAmelCase ,   """ """						).strip()
							return out_string


					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[str] = None						)		->  Tuple[str]:




							"""simple docstring"""


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

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

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

							return out_vocab_file, out_monolingual_vocab_file





import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)

