



"""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)







"""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,
         }


