






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__	:			int			   = logging.get_logger(__name__)

A__	:			Any			   = '▁'

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

A__	:			int			   = {
    '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__	:			Optional[Any]			   = {'vinai/bartpho-syllable': 10_24}
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       VOCAB_FILES_NAMES
				lowercase__											=       PRETRAINED_VOCAB_FILES_MAP
				lowercase__											=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__											=       ["""input_ids""", """attention_mask"""]




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

								'''simple docstring'''
								# Mask token behave like a normal word, i.e. include the space before it
								lowercase__    =			AddedToken(lowerCamelCase,  lstrip=lowerCamelCase,  rstrip=lowerCamelCase  ) if isinstance(lowerCamelCase,  lowerCamelCase  ) else mask_token

								lowercase__    =			{} 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__    =			vocab_file
								lowercase__    =			monolingual_vocab_file
								lowercase__    =			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__    =			{}
								lowercase__    =			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__    =			cnt
																cnt += 1
								with open(lowerCamelCase,  '''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(lowerCamelCase  ) 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						:    str  ):

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




				def __setstate__(     self						:    str,  lowerCamelCase						:    int  ):

								'''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 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    List[int],  lowerCamelCase						:    Optional[List[int]] = None  ):

								'''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 				lowercase__   (     self						:    int,  lowerCamelCase						:    List[int],  lowerCamelCase						:    Optional[List[int]] = None,  lowerCamelCase						:    bool = False  ):

								'''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 				lowercase__   (     self						:    str,  lowerCamelCase						:    List[int],  lowerCamelCase						:    Optional[List[int]] = None  ):

								'''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 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								return len(self.fairseq_ids_to_tokens  )




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			{self.convert_ids_to_tokens(lowerCamelCase  ): i for i in range(self.vocab_size  )}
								vocab.update(self.added_tokens_encoder  )
								return vocab




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    str  ):

								'''simple docstring'''
								return self.sp_model.encode(lowerCamelCase,  out_type=lowerCamelCase  )




				def 				lowercase__   (     self						:    int,  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 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    str  ):

								'''simple docstring'''
								return self.fairseq_ids_to_tokens[index]




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    Union[str, Any]  ):

								'''simple docstring'''
								lowercase__    =			''''''.join(lowerCamelCase  ).replace(lowerCamelCase,  ''' '''  ).strip()
								return out_string




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    str,  lowerCamelCase						:    Optional[str] = None  ):

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












from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       ["""image_processor""", """tokenizer"""]
				lowercase__											=       """BlipImageProcessor"""
				lowercase__											=       ("""BertTokenizer""", """BertTokenizerFast""")




				def __init__(     self						:    Dict,  lowerCamelCase						:    Dict,  lowerCamelCase						:    str  ):

								'''simple docstring'''
								lowercase__    =			False
								super().__init__(lowerCamelCase,  lowerCamelCase  )
								lowercase__    =			self.image_processor




				def __call__(     self						:    int,  lowerCamelCase						:    ImageInput = None,  lowerCamelCase						:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Union[bool, str, PaddingStrategy] = False,  lowerCamelCase						:    Union[bool, str, TruncationStrategy] = None,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    int = 0,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    Optional[bool] = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Optional[Union[str, TensorType]] = None,  **lowerCamelCase						:    Any,  ):

								'''simple docstring'''
								if images is None and text is None:
												raise ValueError('''You have to specify either images or text.'''  )

								# Get only text
								if images is None:
												lowercase__    =			self.tokenizer
												lowercase__    =			self.tokenizer(
												    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
												return text_encoding

								# add pixel_values
								lowercase__    =			self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase  )

								if text is not None:
												lowercase__    =			self.tokenizer(
												    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
								else:
												lowercase__    =			None

								if text_encoding is not None:
												encoding_image_processor.update(lowerCamelCase  )

								return encoding_image_processor




				def 				lowercase__   (     self						:    Tuple,  *lowerCamelCase						:    Union[str, Any],  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[str],  *lowerCamelCase						:    int,  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase  )




				@property
				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			self.tokenizer.model_input_names
								lowercase__    =			self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )




