code: 
  
  
  '''simple docstring'''
  
  
  
  
  import json
  import os
  import re
  import warnings
  from pathlib import Path
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple, Union
  
  import sentencepiece
  
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
  
  
  __lowercase  :      List[Any]      		=				logging.get_logger(__name__)
  
  __lowercase  :      List[str]      		=				{
      """source_spm""": """source.spm""",
      """target_spm""": """target.spm""",
      """vocab""": """vocab.json""",
      """target_vocab_file""": """target_vocab.json""",
      """tokenizer_config_file""": """tokenizer_config.json""",
  }
  
  __lowercase  :      int      		=				{
      """source_spm""": {
          """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
      },
      """target_spm""": {
          """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
      },
      """vocab""": {
          """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
      },
      """tokenizer_config_file""": {
          """Helsinki-NLP/opus-mt-en-de""": (
              """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
          )
      },
  }
  
  __lowercase  :      Optional[int]      		=				{"""Helsinki-NLP/opus-mt-en-de""": 5_12}
  __lowercase  :      Any      		=				{}
  
  
  
  
  
  class _snake_case   ( a__   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     VOCAB_FILES_NAMES
  							__a												=     PRETRAINED_VOCAB_FILES_MAP
  							__a												=     PRETRAINED_INIT_CONFIGURATION
  							__a												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  							__a												=     ['''input_ids''', '''attention_mask''']
  							__a												=     re.compile('''>>.+<<'''   )  # type: re.Pattern
  
  
  
  
  
  
  
  							def __init__(   self			,				__A			,				__A			,				__A			,				__A=None			,				__A=None			,				__A=None			,				__A="<unk>"			,				__A="</s>"			,				__A="<pad>"			,				__A=512			,				__A = None			,				__A=False			,				**__A			,				)     ->None:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[Any]											=					{} if sp_model_kwargs is None else sp_model_kwargs
  
  											super().__init__(
  											    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
  											    source_lang=__A			,				target_lang=__A			,				unk_token=__A			,				eos_token=__A			,				pad_token=__A			,				model_max_length=__A			,				sp_model_kwargs=self.sp_model_kwargs			,				target_vocab_file=__A			,				separate_vocabs=__A			,				**__A			,				)
  											assert Path(__A	).exists(), f'cannot find spm source {source_spm}'
  
  											__lowerCamelCase		:      Dict											=					separate_vocabs
  											__lowerCamelCase		:      Any											=					load_json(__A	)
  											if self.unk_token not in self.encoder:
  															raise KeyError("<unk> token must be in vocab"	)
  											assert self.pad_token in self.encoder
  
  											if separate_vocabs:
  															__lowerCamelCase		:      Union[str, Any]											=					load_json(__A	)
  															__lowerCamelCase		:      Dict											=					{v: k for k, v in self.target_encoder.items()}
  															__lowerCamelCase		:      Optional[int]											=					[]
  											else:
  															__lowerCamelCase		:      Dict											=					{v: k for k, v in self.encoder.items()}
  															__lowerCamelCase		:      list											=					[k for k in self.encoder if k.startswith(">>"	) and k.endswith("<<"	)]
  
  											__lowerCamelCase		:      List[Any]											=					source_lang
  											__lowerCamelCase		:      Tuple											=					target_lang
  											__lowerCamelCase		:      Union[str, Any]											=					[source_spm, target_spm]
  
  											# load SentencePiece model for pre-processing
  											__lowerCamelCase		:      List[str]											=					load_spm(__A			,				self.sp_model_kwargs	)
  											__lowerCamelCase		:      List[Any]											=					load_spm(__A			,				self.sp_model_kwargs	)
  											__lowerCamelCase		:      Optional[int]											=					self.spm_source
  											__lowerCamelCase		:      int											=					self.encoder
  
  											# Multilingual target side: default to using first supported language code.
  
  											self._setup_normalizer()
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											try:
  															from sacremoses import MosesPunctNormalizer
  
  															__lowerCamelCase		:      Tuple											=					MosesPunctNormalizer(self.source_lang	).normalize
  											except (ImportError, FileNotFoundError):
  															warnings.warn("Recommended: pip install sacremoses."	)
  															__lowerCamelCase		:      Tuple											=					lambda __A	: x
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->str:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return self.punc_normalizer(__A	) if x else ""
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return self.current_encoder.get(__A			,				self.current_encoder[self.unk_token]	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      List[Any]											=					self.language_code_re.match(__A	)
  											__lowerCamelCase		:      list											=					[match.group(0	)] if match else []
  											return code, self.language_code_re.sub(""			,				__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase						,					__lowerCamelCase		:      Optional[int]											=					self.remove_language_code(__A	)
  											__lowerCamelCase		:      List[str]											=					self.current_spm.encode(__A			,				out_type=__A	)
  											return code + pieces
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->str:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return self.decoder.get(__A			,				self.unk_token	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				**__A	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return super().batch_decode(__A			,				**__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				**__A	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return super().decode(__A			,				**__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->str:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      str											=					self.spm_source if self._decode_use_source_tokenizer else self.spm_target
  											__lowerCamelCase		:      List[str]											=					[]
  											__lowerCamelCase		:      List[Any]											=					""
  											for token in tokens:
  															# make sure that special tokens are not decoded using sentencepiece model
  															if token in self.all_special_tokens:
  																			out_string += sp_model.decode_pieces(__A	) + token + " "
  																			__lowerCamelCase		:      Tuple											=					[]
  															else:
  																			current_sub_tokens.append(__A	)
  											out_string += sp_model.decode_pieces(__A	)
  											return out_string.strip()
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A=None	)     ->List[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if token_ids_a is None:
  															return token_ids_a + [self.eos_token_id]
  											# We don't expect to process pairs, but leave the pair logic for API consistency
  											return token_ids_a + token_ids_a + [self.eos_token_id]
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Dict											=					self.spm_source
  											__lowerCamelCase		:      Optional[Any]											=					self.encoder
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Union[str, Any]											=					self.spm_target
  											if self.separate_vocabs:
  															__lowerCamelCase		:      Optional[Any]											=					self.target_encoder
  
  
  
  
  
  
  
  							@property
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return len(self.encoder	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A = None	)     ->Tuple[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if not os.path.isdir(__A	):
  															logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
  															return
  											__lowerCamelCase		:      Optional[Any]											=					[]
  
  											if self.separate_vocabs:
  															__lowerCamelCase		:      int											=					os.path.join(
  															    __A			,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]			,				)
  															__lowerCamelCase		:      Union[str, Any]											=					os.path.join(
  															    __A			,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]			,				)
  															save_json(self.encoder			,				__A	)
  															save_json(self.target_encoder			,				__A	)
  															saved_files.append(__A	)
  															saved_files.append(__A	)
  											else:
  															__lowerCamelCase		:      int											=					os.path.join(
  															    __A			,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]	)
  															save_json(self.encoder			,				__A	)
  															saved_files.append(__A	)
  
  											for spm_save_filename, spm_orig_path, spm_model in zip(
  											    [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]]			,				self.spm_files			,				[self.spm_source, self.spm_target]			,				):
  															__lowerCamelCase		:      List[str]											=					os.path.join(
  															    __A			,				(filename_prefix + "-" if filename_prefix else "") + spm_save_filename	)
  															if os.path.abspath(__A	) != os.path.abspath(__A	) and os.path.isfile(__A	):
  																			copyfile(__A			,				__A	)
  																			saved_files.append(__A	)
  															elif not os.path.isfile(__A	):
  																			with open(__A			,				"wb"	) as fi:
  																							__lowerCamelCase		:      List[Any]											=					spm_model.serialized_model_proto()
  																							fi.write(__A	)
  																			saved_files.append(__A	)
  
  											return tuple(__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return self.get_src_vocab()
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return dict(self.encoder			,				**self.added_tokens_encoder	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return dict(self.target_encoder			,				**self.added_tokens_decoder	)
  
  
  
  
  
  
  
  							def __getstate__(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[Any]											=					self.__dict__.copy()
  											state.update(
  											    {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]}	)
  											return state
  
  
  
  
  
  
  
  							def __setstate__(   self			,				__A	)     ->None:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Any											=					d
  
  											# for backward compatibility
  											if not hasattr(self			,				"sp_model_kwargs"	):
  															__lowerCamelCase		:      Dict											=					{}
  
  											__lowerCamelCase						,					__lowerCamelCase		:      Union[str, Any]											=					(load_spm(__A			,				self.sp_model_kwargs	) for f in self.spm_files)
  											__lowerCamelCase		:      int											=					self.spm_source
  											self._setup_normalizer()
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				*__A			,				**__A	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return 1
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      int											=					set(self.all_special_ids	)  # call it once instead of inside list comp
  											all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special
  											return [1 if x in all_special_ids else 0 for x in seq]
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A = None			,				__A = False	)     ->List[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if already_has_special_tokens:
  															return self._special_token_mask(__A	)
  											elif token_ids_a is None:
  															return self._special_token_mask(__A	) + [1]
  											else:
  															return self._special_token_mask(token_ids_a + token_ids_a	) + [1]
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase    )			->sentencepiece.SentencePieceProcessor:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      Optional[Any]											=					sentencepiece.SentencePieceProcessor(**lowercase    )
  				spm.Load(lowercase    )
  				return spm
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase    )			->None:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				with open(lowercase							,						"w"    ) as f:
  								json.dump(lowercase							,						lowercase							,						indent=2    )
  
  
  
  
  
  def 							a_       (						lowercase    )			->Union[Dict, List]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				with open(lowercase							,						"r"    ) as f:
  								return json.load(lowercase    )
  
  
  
  
  
  
style_context: 
  
  
  '''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
  
  
  __lowercase  :      Any      		=				get_tests_dir("""fixtures/spiece.model""")
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  class _snake_case   ( a__    ,   unittest.TestCase   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     ErnieMTokenizer
  							__a												=     False
  							__a												=     True
  							__a												=     False
  							__a												=     False
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											super().setUp()
  
  											# We have a SentencePiece fixture for testing
  											__lowerCamelCase		:      Optional[int]											=					ErnieMTokenizer(__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)
  											tokenizer.save_pretrained(self.tmpdirname	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[int]											=					"this is a test"
  											__lowerCamelCase		:      List[str]											=					"this is a test"
  											return input_text, output_text
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Any											=					"<pad>"
  											__lowerCamelCase		:      Any											=					0
  
  											self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A	)			,				__A	)
  											self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A	)			,				__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Union[str, Any]											=					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(__A	)			,				30000	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											self.assertEqual(self.get_tokenizer().vocab_size			,				30000	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if not self.test_rust_tokenizer:
  															return
  
  											__lowerCamelCase		:      Union[str, Any]											=					self.get_tokenizer()
  											__lowerCamelCase		:      Optional[Any]											=					self.get_rust_tokenizer()
  
  											__lowerCamelCase		:      Dict											=					"I was born in 92000, and this is falsé."
  
  											__lowerCamelCase		:      str											=					tokenizer.tokenize(__A	)
  											__lowerCamelCase		:      str											=					rust_tokenizer.tokenize(__A	)
  											self.assertListEqual(__A			,				__A	)
  
  											__lowerCamelCase		:      Tuple											=					tokenizer.encode(__A			,				add_special_tokens=__A	)
  											__lowerCamelCase		:      Tuple											=					rust_tokenizer.encode(__A			,				add_special_tokens=__A	)
  											self.assertListEqual(__A			,				__A	)
  
  											__lowerCamelCase		:      Optional[int]											=					self.get_rust_tokenizer()
  											__lowerCamelCase		:      Dict											=					tokenizer.encode(__A	)
  											__lowerCamelCase		:      List[str]											=					rust_tokenizer.encode(__A	)
  											self.assertListEqual(__A			,				__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Tuple											=					ErnieMTokenizer(__A			,				do_lower_case=__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)
  
  											__lowerCamelCase		:      Optional[Any]											=					tokenizer.tokenize("This is a test"	)
  											self.assertListEqual(__A			,				["▁this", "▁is", "▁a", "▁test"]	)
  
  											self.assertListEqual(tokenizer.convert_tokens_to_ids(__A	)			,				[48, 25, 21, 1289]	)
  
  											__lowerCamelCase		:      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(
  											    __A			,				["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]	)
  											__lowerCamelCase		:      Optional[int]											=					tokenizer.convert_tokens_to_ids(__A	)
  											self.assertListEqual(__A			,				[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]	)
  
  											__lowerCamelCase		:      Dict											=					tokenizer.convert_ids_to_tokens(__A	)
  											self.assertListEqual(
  											    __A			,				["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]			,				)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Union[str, Any]											=					ErnieMTokenizer(__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)
  
  											__lowerCamelCase		:      int											=					tokenizer.encode("sequence builders"	)
  											__lowerCamelCase		:      List[Any]											=					tokenizer.encode("multi-sequence build"	)
  
  											__lowerCamelCase		:      Tuple											=					tokenizer.build_inputs_with_special_tokens(__A	)
  											__lowerCamelCase		:      Optional[int]											=					tokenizer.build_inputs_with_special_tokens(__A			,				__A	)
  
  											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 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Tuple											=					{"input_ids": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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=__A			,				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.",
  											    ]			,				)
  
  
  
  
  
  