code: 
  
  
  
  """simple docstring"""
  import json
  import os
  from functools import lru_cache
  from typing import List, Optional, Tuple
  
  import regex as re
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  _lowerCAmelCase					:List[str]  =      logging.get_logger(__name__)
  
  
  _lowerCAmelCase					:str  =      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}
  
  # See all MVP models at https://huggingface.co/models?filter=mvp
  _lowerCAmelCase					:str  =      {
      'vocab_file': {
          'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
      },
      'added_tokens.json': {
          'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
      },
      'merges_file': {
          'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
      },
  }
  
  _lowerCAmelCase					:List[Any]  =      {
      'RUCAIBox/mvp': 1_024,
  }
  
  
  
  
  
  @lru_cache()
  def 				lowerCamelCase_		():
      _UpperCAmelCase							:						List[str]       							=					(
          list(range(ord('''!'''							)  , ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)  , ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)  , ord('''ÿ'''							) + 1							)							)
      )
      _UpperCAmelCase							:						List[str]       							=					bs[:]
      _UpperCAmelCase							:						Union[str, Any]       							=					0
      for b in range(2**8							):
          if b not in bs:
              bs.append(UpperCamelCase__							)
              cs.append(2**8 + n							)
              n += 1
      _UpperCAmelCase							:						Dict       							=					[chr(UpperCamelCase__							) for n in cs]
      return dict(zip(UpperCamelCase__  , UpperCamelCase__							)							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[str]							):
      _UpperCAmelCase							:						int       							=					set()
      _UpperCAmelCase							:						Optional[int]       							=					word[0]
      for char in word[1:]:
          pairs.add((prev_char, char)							)
          _UpperCAmelCase							:						Union[str, Any]       							=					char
      return pairs
  
  
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =VOCAB_FILES_NAMES
       a__             =PRETRAINED_VOCAB_FILES_MAP
       a__             =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a__             =['''input_ids''', '''attention_mask''']
  
  
  
  
  
       def __init__(      self ,			A ,			A ,			A="replace" ,			A="<s>" ,			A="</s>" ,			A="</s>" ,			A="<s>" ,			A="<unk>" ,			A="<pad>" ,			A="<mask>" ,			A=False ,			**A ,			)							->      Dict:
           _UpperCAmelCase							:						Any       							=					AddedToken(A ,			lstrip=A ,			rstrip=A	) if isinstance(A ,			A	) else bos_token
           _UpperCAmelCase							:						str       							=					AddedToken(A ,			lstrip=A ,			rstrip=A	) if isinstance(A ,			A	) else eos_token
           _UpperCAmelCase							:						str       							=					AddedToken(A ,			lstrip=A ,			rstrip=A	) if isinstance(A ,			A	) else sep_token
           _UpperCAmelCase							:						Optional[int]       							=					AddedToken(A ,			lstrip=A ,			rstrip=A	) if isinstance(A ,			A	) else cls_token
           _UpperCAmelCase							:						Optional[int]       							=					AddedToken(A ,			lstrip=A ,			rstrip=A	) if isinstance(A ,			A	) else unk_token
           _UpperCAmelCase							:						Optional[int]       							=					AddedToken(A ,			lstrip=A ,			rstrip=A	) if isinstance(A ,			A	) else pad_token
  
           # Mask token behave like a normal word, i.e. include the space before it
           _UpperCAmelCase							:						Optional[int]       							=					AddedToken(A ,			lstrip=A ,			rstrip=A	) if isinstance(A ,			A	) else mask_token
  
           super().__init__(
               errors=A ,			bos_token=A ,			eos_token=A ,			unk_token=A ,			sep_token=A ,			cls_token=A ,			pad_token=A ,			mask_token=A ,			add_prefix_space=A ,			**A ,			)
           with open(A ,			encoding='''utf-8'''	) as vocab_handle:
               _UpperCAmelCase							:						int       							=					json.load(A	)
           _UpperCAmelCase							:						Dict       							=					{v: k for k, v in self.encoder.items()}
           _UpperCAmelCase							:						List[str]       							=					errors  # how to handle errors in decoding
           _UpperCAmelCase							:						Any       							=					bytes_to_unicode()
           _UpperCAmelCase							:						List[Any]       							=					{v: k for k, v in self.byte_encoder.items()}
           with open(A ,			encoding='''utf-8'''	) as merges_handle:
               _UpperCAmelCase							:						Tuple       							=					merges_handle.read().split('''\n'''	)[1:-1]
           _UpperCAmelCase							:						List[str]       							=					[tuple(merge.split()	) for merge in bpe_merges]
           _UpperCAmelCase							:						Optional[Any]       							=					dict(zip(A ,			range(len(A	)	)	)	)
           _UpperCAmelCase							:						Tuple       							=					{}
           _UpperCAmelCase							:						List[str]       							=					add_prefix_space
  
           # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
           _UpperCAmelCase							:						Dict       							=					re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''	)
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      str:
           return len(self.encoder	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      int:
           return dict(self.encoder ,			**self.added_tokens_encoder	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      List[str]:
           if token in self.cache:
               return self.cache[token]
           _UpperCAmelCase							:						Optional[Any]       							=					tuple(A	)
           _UpperCAmelCase							:						Optional[int]       							=					get_pairs(A	)
  
           if not pairs:
               return token
  
           while True:
               _UpperCAmelCase							:						Dict       							=					min(A ,			key=lambda A	: self.bpe_ranks.get(A ,			float('''inf'''	)	)	)
               if bigram not in self.bpe_ranks:
                   break
               _UpperCAmelCase							,       _UpperCAmelCase							:						Any       							=					bigram
               _UpperCAmelCase							:						Tuple       							=					[]
               _UpperCAmelCase							:						Optional[Any]       							=					0
               while i < len(A	):
                   try:
                       _UpperCAmelCase							:						int       							=					word.index(A ,			A	)
                   except ValueError:
                       new_word.extend(word[i:]	)
                       break
                   else:
                       new_word.extend(word[i:j]	)
                       _UpperCAmelCase							:						Union[str, Any]       							=					j
  
                   if word[i] == first and i < len(A	) - 1 and word[i + 1] == second:
                       new_word.append(first + second	)
                       i += 2
                   else:
                       new_word.append(word[i]	)
                       i += 1
               _UpperCAmelCase							:						Tuple       							=					tuple(A	)
               _UpperCAmelCase							:						Dict       							=					new_word
               if len(A	) == 1:
                   break
               else:
                   _UpperCAmelCase							:						Any       							=					get_pairs(A	)
           _UpperCAmelCase							:						List[Any]       							=					''' '''.join(A	)
           _UpperCAmelCase							:						Optional[Any]       							=					word
           return word
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      List[Any]:
           _UpperCAmelCase							:						List[str]       							=					[]
           for token in re.findall(self.pat ,			A	):
               _UpperCAmelCase							:						int       							=					''''''.join(
                   self.byte_encoder[b] for b in token.encode('''utf-8'''	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
               bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A	).split(''' '''	)	)
           return bpe_tokens
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Union[str, Any]:
           return self.encoder.get(A ,			self.encoder.get(self.unk_token	)	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Tuple:
           return self.decoder.get(A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      str:
           _UpperCAmelCase							:						Optional[int]       							=					''''''.join(A	)
           _UpperCAmelCase							:						Union[str, Any]       							=					bytearray([self.byte_decoder[c] for c in text]	).decode('''utf-8''' ,			errors=self.errors	)
           return text
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      Tuple[str]:
           if not os.path.isdir(A	):
               logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
               return
           _UpperCAmelCase							:						List[str]       							=					os.path.join(
               A ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)
           _UpperCAmelCase							:						Any       							=					os.path.join(
               A ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']	)
  
           with open(A ,			'''w''' ,			encoding='''utf-8'''	) as f:
               f.write(json.dumps(self.encoder ,			indent=2 ,			sort_keys=A ,			ensure_ascii=A	) + '''\n'''	)
  
           _UpperCAmelCase							:						Optional[Any]       							=					0
           with open(A ,			'''w''' ,			encoding='''utf-8'''	) as writer:
               writer.write('''#version: 0.2\n'''	)
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,			key=lambda A	: kv[1]	):
                   if index != token_index:
                       logger.warning(
                           f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                           ''' Please check that the tokenizer is not corrupted!'''	)
                       _UpperCAmelCase							:						List[Any]       							=					token_index
                   writer.write(''' '''.join(A	) + '''\n'''	)
                   index += 1
  
           return vocab_file, merge_file
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      List[int]:
           if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _UpperCAmelCase							:						Optional[int]       							=					[self.cls_token_id]
           _UpperCAmelCase							:						Dict       							=					[self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None ,			A = False	)							->      List[int]:
           if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=A ,			token_ids_a=A ,			already_has_special_tokens=A	)
  
           if token_ids_a is None:
               return [1] + ([0] * len(A	)) + [1]
           return [1] + ([0] * len(A	)) + [1, 1] + ([0] * len(A	)) + [1]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      List[int]:
           _UpperCAmelCase							:						str       							=					[self.sep_token_id]
           _UpperCAmelCase							:						Union[str, Any]       							=					[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]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A=False ,			**A	)							->      Dict:
           _UpperCAmelCase							:						str       							=					kwargs.pop('''add_prefix_space''' ,			self.add_prefix_space	)
           if (is_split_into_words or add_prefix_space) and (len(A	) > 0 and not text[0].isspace()):
               _UpperCAmelCase							:						Tuple       							=					''' ''' + text
           return (text, kwargs)
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import os
  from typing import List, Union
  
  import tensorflow as tf
  from tensorflow_text import BertTokenizer as BertTokenizerLayer
  from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs
  
  from .tokenization_bert import BertTokenizer
  class 							_UpperCAmelCase							(				tf.keras.layers.Layer   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A ,			A = None ,			A = None ,			A = None ,			A = "longest" ,			A = True ,			A = 5_1_2 ,			A = None ,			A = True ,			A = True ,			A = True ,			**A ,			)							->      Tuple:
           super().__init__()
           if use_fast_bert_tokenizer:
               _UpperCAmelCase							:						Union[str, Any]       							=					FastBertTokenizer(
                   A ,			token_out_type=tf.intaa ,			lower_case_nfd_strip_accents=A ,			**A	)
           else:
               _UpperCAmelCase							:						List[Any]       							=					tf.lookup.StaticVocabularyTable(
                   tf.lookup.KeyValueTensorInitializer(
                       keys=A ,			key_dtype=tf.string ,			values=tf.range(tf.size(A ,			out_type=tf.intaa	) ,			dtype=tf.intaa	) ,			value_dtype=tf.intaa ,			) ,			num_oov_buckets=1 ,			)
               _UpperCAmelCase							:						str       							=					BertTokenizerLayer(
                   A ,			token_out_type=tf.intaa ,			lower_case=A ,			**A	)
  
           _UpperCAmelCase							:						int       							=					vocab_list
           _UpperCAmelCase							:						Dict       							=					do_lower_case
           _UpperCAmelCase							:						Optional[int]       							=					cls_token_id or vocab_list.index('''[CLS]'''	)
           _UpperCAmelCase							:						Any       							=					sep_token_id or vocab_list.index('''[SEP]'''	)
           _UpperCAmelCase							:						List[Any]       							=					pad_token_id or vocab_list.index('''[PAD]'''	)
           _UpperCAmelCase							:						List[str]       							=					ShrinkLongestTrimmer(max_length - 3 ,			axis=1	)  # Allow room for special tokens
           _UpperCAmelCase							:						Tuple       							=					max_length
           _UpperCAmelCase							:						List[Any]       							=					padding
           _UpperCAmelCase							:						List[Any]       							=					truncation
           _UpperCAmelCase							:						Union[str, Any]       							=					pad_to_multiple_of
           _UpperCAmelCase							:						Union[str, Any]       							=					return_token_type_ids
           _UpperCAmelCase							:						Union[str, Any]       							=					return_attention_mask
  
  
  
  
  
       @classmethod
       def 				__lowerCAmelCase			(      cls ,			A ,			**A	)							->      Dict:  # noqa: F821
           _UpperCAmelCase							:						Optional[int]       							=					kwargs.pop('''do_lower_case''' ,			A	)
           _UpperCAmelCase							:						int       							=					tokenizer.do_lower_case if do_lower_case is None else do_lower_case
           _UpperCAmelCase							:						Optional[Any]       							=					kwargs.pop('''cls_token_id''' ,			A	)
           _UpperCAmelCase							:						str       							=					tokenizer.cls_token_id if cls_token_id is None else cls_token_id
           _UpperCAmelCase							:						str       							=					kwargs.pop('''sep_token_id''' ,			A	)
           _UpperCAmelCase							:						int       							=					tokenizer.sep_token_id if sep_token_id is None else sep_token_id
           _UpperCAmelCase							:						List[Any]       							=					kwargs.pop('''pad_token_id''' ,			A	)
           _UpperCAmelCase							:						Optional[Any]       							=					tokenizer.pad_token_id if pad_token_id is None else pad_token_id
  
           _UpperCAmelCase							:						int       							=					tokenizer.get_vocab()
           _UpperCAmelCase							:						List[str]       							=					sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()] ,			key=lambda A	: x[1]	)
           _UpperCAmelCase							:						Dict       							=					[entry[0] for entry in vocab]
           return cls(
               vocab_list=A ,			do_lower_case=A ,			cls_token_id=A ,			sep_token_id=A ,			pad_token_id=A ,			**A ,			)
  
  
  
  
  
       @classmethod
       def 				__lowerCAmelCase			(      cls ,			A ,			*A ,			**A	)							->      int:
           try:
               _UpperCAmelCase							:						int       							=					BertTokenizer.from_pretrained(A ,			*A ,			**A	)
           except:  # noqa: E722
               from .tokenization_bert_fast import BertTokenizerFast
  
               _UpperCAmelCase							:						Dict       							=					BertTokenizerFast.from_pretrained(A ,			*A ,			**A	)
           return cls.from_tokenizer(A ,			**A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Any:
           if self.do_lower_case:
               _UpperCAmelCase							:						int       							=					case_fold_utfa(A	)
           _UpperCAmelCase							:						str       							=					self.tf_tokenizer.tokenize(A	)
           return tokens.merge_dims(1 ,			-1	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			)							->      Optional[Any]:
           if padding is None:
               _UpperCAmelCase							:						str       							=					self.padding
           if padding not in ("longest", "max_length"):
               raise ValueError('''Padding must be either \'longest\' or \'max_length\'!'''	)
           if max_length is not None and text_pair is not None:
               # Because we have to instantiate a Trimmer to do it properly
               raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!'''	)
           if max_length is None:
               _UpperCAmelCase							:						Union[str, Any]       							=					self.max_length
           if truncation is None:
               _UpperCAmelCase							:						List[Any]       							=					self.truncation
           if pad_to_multiple_of is None:
               _UpperCAmelCase							:						List[Any]       							=					self.pad_to_multiple_of
           if return_token_type_ids is None:
               _UpperCAmelCase							:						List[str]       							=					self.return_token_type_ids
           if return_attention_mask is None:
               _UpperCAmelCase							:						Any       							=					self.return_attention_mask
           if not isinstance(A ,			tf.Tensor	):
               _UpperCAmelCase							:						Dict       							=					tf.convert_to_tensor(A	)
           if text_pair is not None and not isinstance(A ,			tf.Tensor	):
               _UpperCAmelCase							:						str       							=					tf.convert_to_tensor(A	)
           if text_pair is not None:
               if text.shape.rank > 1:
                   raise ValueError('''text argument should not be multidimensional when a text pair is supplied!'''	)
               if text_pair.shape.rank > 1:
                   raise ValueError('''text_pair should not be multidimensional!'''	)
           if text.shape.rank == 2:
               _UpperCAmelCase							,       _UpperCAmelCase							:						str       							=					text[:, 0], text[:, 1]
           _UpperCAmelCase							:						Optional[Any]       							=					self.unpaired_tokenize(A	)
           if text_pair is None:  # Unpaired text
               if truncation:
                   _UpperCAmelCase							:						Optional[int]       							=					text[:, : max_length - 2]  # Allow room for special tokens
               _UpperCAmelCase							,       _UpperCAmelCase							:						Any       							=					combine_segments(
                   (text,) ,			start_of_sequence_id=self.cls_token_id ,			end_of_segment_id=self.sep_token_id	)
           else:  # Paired text
               _UpperCAmelCase							:						Tuple       							=					self.unpaired_tokenize(A	)
               if truncation:
                   _UpperCAmelCase							,       _UpperCAmelCase							:						Optional[int]       							=					self.paired_trimmer.trim([text, text_pair]	)
               _UpperCAmelCase							,       _UpperCAmelCase							:						Union[str, Any]       							=					combine_segments(
                   (text, text_pair) ,			start_of_sequence_id=self.cls_token_id ,			end_of_segment_id=self.sep_token_id	)
           if padding == "longest":
               _UpperCAmelCase							:						List[Any]       							=					input_ids.bounding_shape(axis=1	)
               if pad_to_multiple_of is not None:
                   # No ceiling division in tensorflow, so we negate floordiv instead
                   _UpperCAmelCase							:						Tuple       							=					pad_to_multiple_of * (-tf.math.floordiv(-pad_length ,			A	))
           else:
               _UpperCAmelCase							:						int       							=					max_length
  
           _UpperCAmelCase							,       _UpperCAmelCase							:						Any       							=					pad_model_inputs(A ,			max_seq_length=A ,			pad_value=self.pad_token_id	)
           _UpperCAmelCase							:						Dict       							=					{'''input_ids''': input_ids}
           if return_attention_mask:
               _UpperCAmelCase							:						Optional[int]       							=					attention_mask
           if return_token_type_ids:
               _UpperCAmelCase							,       _UpperCAmelCase							:						Tuple       							=					pad_model_inputs(
                   A ,			max_seq_length=A ,			pad_value=self.pad_token_id	)
               _UpperCAmelCase							:						int       							=					token_type_ids
           return output
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      List[str]:
           return {
               "vocab_list": self.vocab_list,
               "do_lower_case": self.do_lower_case,
               "cls_token_id": self.cls_token_id,
               "sep_token_id": self.sep_token_id,
               "pad_token_id": self.pad_token_id,
           }
  
  
  