'''simple docstring'''


import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


snake_case__	:     List[Any]      =    get_tests_dir('''fixtures/spiece.model''')






@require_sentencepiece
@require_tokenizers
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:Dict		       =						ErnieMTokenizer
  lowerCamelCase_						:int		       =						False
  lowerCamelCase_						:Tuple		       =						True
  lowerCamelCase_						:Optional[int]		       =						False
  lowerCamelCase_						:List[Any]		       =						False


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCAmelCase_				:  List[Any]  = ErnieMTokenizer(snake_case_ ,						unk_token='<unk>' ,						pad_token='<pad>'		)
       tokenizer.save_pretrained(self.tmpdirname		)


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = 'this is a test'
       UpperCAmelCase_				:  str  = 'this is a test'
       return input_text, output_text


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = '<pad>'
       UpperCAmelCase_				:  List[str]  = 0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_		) ,						snake_case_		)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_		) ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = list(self.get_tokenizer().get_vocab().keys()		)

       self.assertEqual(vocab_keys[0] ,						'<pad>'		)
       self.assertEqual(vocab_keys[1] ,						'<unk>'		)
       self.assertEqual(vocab_keys[-1] ,						'▁eloquent'		)
       self.assertEqual(len(snake_case_		) ,						3_0_0_0_0		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       self.assertEqual(self.get_tokenizer().vocab_size ,						3_0_0_0_0		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       if not self.test_rust_tokenizer:
            return

       UpperCAmelCase_				:  Optional[Any]  = self.get_tokenizer()
       UpperCAmelCase_				:  Optional[Any]  = self.get_rust_tokenizer()

       UpperCAmelCase_				:  List[Any]  = 'I was born in 92000, and this is falsé.'

       UpperCAmelCase_				:  List[Any]  = tokenizer.tokenize(snake_case_		)
       UpperCAmelCase_				:  Optional[Any]  = rust_tokenizer.tokenize(snake_case_		)
       self.assertListEqual(snake_case_ ,						snake_case_		)

       UpperCAmelCase_				:  Any  = tokenizer.encode(snake_case_ ,						add_special_tokens=snake_case_		)
       UpperCAmelCase_				:  Dict  = rust_tokenizer.encode(snake_case_ ,						add_special_tokens=snake_case_		)
       self.assertListEqual(snake_case_ ,						snake_case_		)

       UpperCAmelCase_				:  Optional[int]  = self.get_rust_tokenizer()
       UpperCAmelCase_				:  Optional[int]  = tokenizer.encode(snake_case_		)
       UpperCAmelCase_				:  int  = rust_tokenizer.encode(snake_case_		)
       self.assertListEqual(snake_case_ ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = ErnieMTokenizer(snake_case_ ,						do_lower_case=snake_case_ ,						unk_token='<unk>' ,						pad_token='<pad>'		)

       UpperCAmelCase_				:  Union[str, Any]  = tokenizer.tokenize('This is a test'		)
       self.assertListEqual(snake_case_ ,						['▁this', '▁is', '▁a', '▁test']		)

       self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_		) ,						[4_8, 2_5, 2_1, 1_2_8_9]		)

       UpperCAmelCase_				:  List[Any]  = tokenizer.tokenize('I was born in 92000, and this is falsé.'		)
       # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
       self.assertListEqual(
           snake_case_ ,						['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']		)
       UpperCAmelCase_				:  int  = tokenizer.convert_tokens_to_ids(snake_case_		)
       self.assertListEqual(snake_case_ ,						[3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]		)

       UpperCAmelCase_				:  Optional[Any]  = tokenizer.convert_ids_to_tokens(snake_case_		)
       self.assertListEqual(
           snake_case_ ,						['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.'] ,						)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = ErnieMTokenizer(snake_case_ ,						unk_token='<unk>' ,						pad_token='<pad>'		)

       UpperCAmelCase_				:  List[Any]  = tokenizer.encode('sequence builders'		)
       UpperCAmelCase_				:  List[str]  = tokenizer.encode('multi-sequence build'		)

       UpperCAmelCase_				:  Union[str, Any]  = tokenizer.build_inputs_with_special_tokens(snake_case_		)
       UpperCAmelCase_				:  Tuple  = tokenizer.build_inputs_with_special_tokens(snake_case_ ,						snake_case_		)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
           tokenizer.sep_token_id
       ] + text_a + [tokenizer.sep_token_id]






  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = {'input_ids': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=snake_case_ ,						model_name='susnato/ernie-m-base_pytorch' ,						sequences=[
               'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
               'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
               'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
               'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
               'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
               'conditioning on both left and right context in all layers.',
               'The quick brown fox jumps over the lazy dog.',
           ] ,						)













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


_UpperCamelCase       :				Optional[Any]           =			logging.get_logger(__name__)


_UpperCamelCase       :				Optional[int]           =			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
_UpperCamelCase       :				int           =			{
    """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""",
    },
}

_UpperCamelCase       :				str           =			{
    """RUCAIBox/mvp""": 1_0_2_4,
}
@lru_cache()
def  __UpperCamelCase    (						)    ->		Tuple:


   '''simple docstring'''







   __A    =  (
       list(range(ord('''!'''      )							,			ord('''~'''      ) + 1      )      ) + list(range(ord('''¡'''      )							,			ord('''¬'''      ) + 1      )      ) + list(range(ord('''®'''      )							,			ord('''ÿ'''      ) + 1      )      )
   )
   __A    =  bs[:]
   __A    =  0
   for b in range(2**8      ):
      if b not in bs:
         bs.append(snake_case      )
         cs.append(2**8 + n      )
         n += 1
   __A    =  [chr(snake_case      ) for n in cs]
   return dict(zip(snake_case							,			snake_case      )      )
def  __UpperCamelCase    (						snake_case      )    ->		int:


   '''simple docstring'''







   __A    =  set()
   __A    =  word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)      )
      __A    =  char
   return pairs







class 		_lowerCAmelCase(		_a):

  """simple docstring"""







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

  def __init__(   self    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase="replace"    ,							UpperCAmelCase="<s>"    ,							UpperCAmelCase="</s>"    ,							UpperCAmelCase="</s>"    ,							UpperCAmelCase="<s>"    ,							UpperCAmelCase="<unk>"    ,							UpperCAmelCase="<pad>"    ,							UpperCAmelCase="<mask>"    ,							UpperCAmelCase=False    ,							**UpperCAmelCase    ,							)->   Optional[int]:
     __A    =  AddedToken(UpperCAmelCase    ,							lstrip=UpperCAmelCase    ,							rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ) else bos_token
     __A    =  AddedToken(UpperCAmelCase    ,							lstrip=UpperCAmelCase    ,							rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ) else eos_token
     __A    =  AddedToken(UpperCAmelCase    ,							lstrip=UpperCAmelCase    ,							rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ) else sep_token
     __A    =  AddedToken(UpperCAmelCase    ,							lstrip=UpperCAmelCase    ,							rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ) else cls_token
     __A    =  AddedToken(UpperCAmelCase    ,							lstrip=UpperCAmelCase    ,							rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ) else unk_token
     __A    =  AddedToken(UpperCAmelCase    ,							lstrip=UpperCAmelCase    ,							rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     __A    =  AddedToken(UpperCAmelCase    ,							lstrip=UpperCAmelCase    ,							rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ) else mask_token

     super().__init__(
         errors=UpperCAmelCase    ,							bos_token=UpperCAmelCase    ,							eos_token=UpperCAmelCase    ,							unk_token=UpperCAmelCase    ,							sep_token=UpperCAmelCase    ,							cls_token=UpperCAmelCase    ,							pad_token=UpperCAmelCase    ,							mask_token=UpperCAmelCase    ,							add_prefix_space=UpperCAmelCase    ,							**UpperCAmelCase    ,							)
     with open(UpperCAmelCase    ,							encoding='''utf-8'''      ) as vocab_handle:
        __A    =  json.load(UpperCAmelCase      )
     __A    =  {v: k for k, v in self.encoder.items()}
     __A    =  errors  # how to handle errors in decoding
     __A    =  bytes_to_unicode()
     __A    =  {v: k for k, v in self.byte_encoder.items()}
     with open(UpperCAmelCase    ,							encoding='''utf-8'''      ) as merges_handle:
        __A    =  merges_handle.read().split('''\n'''      )[1:-1]
     __A    =  [tuple(merge.split()      ) for merge in bpe_merges]
     __A    =  dict(zip(UpperCAmelCase    ,							range(len(UpperCAmelCase      )      )      )      )
     __A    =  {}
     __A    =  add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     __A    =  re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''      )

  @property
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Tuple:
     return len(self.encoder      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   str:
     return dict(self.encoder    ,							**self.added_tokens_encoder      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   List[str]:
     if token in self.cache:
        return self.cache[token]
     __A    =  tuple(UpperCAmelCase      )
     __A    =  get_pairs(UpperCAmelCase      )

     if not pairs:
        return token

     while True:
        __A    =  min(UpperCAmelCase    ,							key=lambda UpperCAmelCase      : self.bpe_ranks.get(UpperCAmelCase    ,							float('''inf'''      )      )      )
        if bigram not in self.bpe_ranks:
           break
        __A   ,		__A    =  bigram
        __A    =  []
        __A    =  0
        while i < len(UpperCAmelCase      ):
           try:
              __A    =  word.index(UpperCAmelCase    ,							UpperCAmelCase      )
           except ValueError:
              new_word.extend(word[i:]      )
              break
           else:
              new_word.extend(word[i:j]      )
              __A    =  j

           if word[i] == first and i < len(UpperCAmelCase      ) - 1 and word[i + 1] == second:
              new_word.append(first + second      )
              i += 2
           else:
              new_word.append(word[i]      )
              i += 1
        __A    =  tuple(UpperCAmelCase      )
        __A    =  new_word
        if len(UpperCAmelCase      ) == 1:
           break
        else:
           __A    =  get_pairs(UpperCAmelCase      )
     __A    =  ''' '''.join(UpperCAmelCase      )
     __A    =  word
     return word

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   List[Any]:
     __A    =  []
     for token in re.findall(self.pat    ,							UpperCAmelCase      ):
        __A    =  ''''''.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(UpperCAmelCase      ).split(''' '''      )      )
     return bpe_tokens

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   Any:
     return self.encoder.get(UpperCAmelCase    ,							self.encoder.get(self.unk_token      )      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   List[Any]:
     return self.decoder.get(UpperCAmelCase      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   Tuple:
     __A    =  ''''''.join(UpperCAmelCase      )
     __A    =  bytearray([self.byte_decoder[c] for c in text]      ).decode('''utf-8'''    ,							errors=self.errors      )
     return text

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase = None      )->   Tuple[str]:
     if not os.path.isdir(UpperCAmelCase      ):
        logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
        return
     __A    =  os.path.join(
         UpperCAmelCase    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
     __A    =  os.path.join(
         UpperCAmelCase    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']      )

     with open(UpperCAmelCase    ,							'''w'''    ,							encoding='''utf-8'''      ) as f:
        f.write(json.dumps(self.encoder    ,							indent=2    ,							sort_keys=UpperCAmelCase    ,							ensure_ascii=UpperCAmelCase      ) + '''\n'''      )

     __A    =  0
     with open(UpperCAmelCase    ,							'''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 UpperCAmelCase      : 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!'''      )
              __A    =  token_index
           writer.write(''' '''.join(UpperCAmelCase      ) + '''\n'''      )
           index += 1

     return vocab_file, merge_file

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase = None      )->   List[int]:
     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     __A    =  [self.cls_token_id]
     __A    =  [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase = None    ,							UpperCAmelCase = False      )->   List[int]:
     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 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase = None      )->   List[int]:
     __A    =  [self.sep_token_id]
     __A    =  [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 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase=False    ,							**UpperCAmelCase      )->   Union[str, Any]:
     __A    =  kwargs.pop('''add_prefix_space'''    ,							self.add_prefix_space      )
     if (is_split_into_words or add_prefix_space) and (len(UpperCAmelCase      ) > 0 and not text[0].isspace()):
        __A    =  ''' ''' + text
     return (text, kwargs)

