
import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


__lowerCamelCase							=       logging.getLogger(__name__)






class   UpperCAmelCase		(						A_ ):
   A__					:		int						     =     "sequence-classification"



   def __init__(self		:					List[str]    ,						snake_case__		:					str )		->	Dict:


          '''simple docstring'''


          if type(snake_case__ ) == dict:
                 snake_case       :   Dict							=      Namespace(**snake_case__ )
          snake_case       :   Dict							=      glue_output_modes[hparams.task]
          snake_case       :   Union[str, Any]							=      glue_tasks_num_labels[hparams.task]

          super().__init__(snake_case__    ,						snake_case__    ,						self.mode )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						**snake_case__		:					Optional[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          return self.model(**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					List[str]    ,						snake_case__		:					List[Any] )		->	str:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

          if self.config.model_type not in ["distilbert", "bart"]:
                 snake_case       :   Union[str, Any]							=      batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

          snake_case       :   str							=      self(**snake_case__ )
          snake_case       :   List[str]							=      outputs[0]

          snake_case       :   List[str]							=      self.trainer.lr_schedulers[0]["scheduler"]
          snake_case       :   Union[str, Any]							=      {"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
          return {"loss": loss, "log": tensorboard_logs}



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      self.hparams
          snake_case       :   Union[str, Any]							=      processors[args.task]()
          snake_case       :   Union[str, Any]							=      processor.get_labels()

          for mode in ["train", "dev"]:
                 snake_case       :   str							=      self._feature_file(snake_case__ )
                 if os.path.exists(snake_case__ ) and not args.overwrite_cache:
                        logger.info("Loading features from cached file %s"    ,						snake_case__ )
                 else:
                        logger.info("Creating features from dataset file at %s"    ,						args.data_dir )
                        snake_case       :   Union[str, Any]							=      (
                            processor.get_dev_examples(args.data_dir )
                            if mode == "dev"
                            else processor.get_train_examples(args.data_dir )
                        )
                        snake_case       :   Optional[int]							=      convert_examples_to_features(
                            snake_case__    ,						self.tokenizer    ,						max_length=args.max_seq_length    ,						label_list=self.labels    ,						output_mode=args.glue_output_mode    ,						)
                        logger.info("Saving features into cached file %s"    ,						snake_case__ )
                        torch.save(snake_case__    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					str    ,						snake_case__		:					int    ,						snake_case__		:					bool = False )		->	DataLoader:


          '''simple docstring'''


          snake_case       :   Dict							=      "dev" if mode == "test" else mode

          snake_case       :   Any							=      self._feature_file(snake_case__ )
          logger.info("Loading features from cached file %s"    ,						snake_case__ )
          snake_case       :   Any							=      torch.load(snake_case__ )
          snake_case       :   Any							=      torch.tensor([f.input_ids for f in features]    ,						dtype=torch.long )
          snake_case       :   str							=      torch.tensor([f.attention_mask for f in features]    ,						dtype=torch.long )
          snake_case       :   int							=      torch.tensor([f.token_type_ids for f in features]    ,						dtype=torch.long )
          if self.hparams.glue_output_mode == "classification":
                 snake_case       :   List[str]							=      torch.tensor([f.label for f in features]    ,						dtype=torch.long )
          elif self.hparams.glue_output_mode == "regression":
                 snake_case       :   Dict							=      torch.tensor([f.label for f in features]    ,						dtype=torch.float )

          return DataLoader(
              TensorDataset(snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__ )    ,						batch_size=snake_case__    ,						shuffle=snake_case__    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					int    ,						snake_case__		:					Tuple )		->	str:


          '''simple docstring'''


          snake_case       :   str							=      {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

          if self.config.model_type not in ["distilbert", "bart"]:
                 snake_case       :   List[Any]							=      batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

          snake_case       :   Dict							=      self(**snake_case__ )
          snake_case				,					snake_case       :   Union[str, Any]							=      outputs[:2]
          snake_case       :   Union[str, Any]							=      logits.detach().cpu().numpy()
          snake_case       :   Dict							=      inputs["labels"].detach().cpu().numpy()

          return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					Optional[int] )		->	tuple:


          '''simple docstring'''


          snake_case       :   int							=      torch.stack([x["val_loss"] for x in outputs] ).mean().detach().cpu().item()
          snake_case       :   Optional[int]							=      np.concatenate([x["pred"] for x in outputs]    ,						axis=0 )

          if self.hparams.glue_output_mode == "classification":
                 snake_case       :   int							=      np.argmax(snake_case__    ,						axis=1 )
          elif self.hparams.glue_output_mode == "regression":
                 snake_case       :   int							=      np.squeeze(snake_case__ )

          snake_case       :   Dict							=      np.concatenate([x["target"] for x in outputs]    ,						axis=0 )
          snake_case       :   List[str]							=      [[] for _ in range(out_label_ids.shape[0] )]
          snake_case       :   Optional[int]							=      [[] for _ in range(out_label_ids.shape[0] )]

          snake_case       :   str							=      {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task    ,						snake_case__    ,						snake_case__ )}

          snake_case       :   Any							=      dict(results.items() )
          snake_case       :   List[Any]							=      results
          return ret, preds_list, out_label_list



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					list )		->	dict:


          '''simple docstring'''


          snake_case				,					snake_case				,					snake_case       :   Union[str, Any]							=      self._eval_end(snake_case__ )
          snake_case       :   List[str]							=      ret["log"]
          return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					Any )		->	dict:


          '''simple docstring'''


          snake_case				,					snake_case				,					snake_case       :   Optional[int]							=      self._eval_end(snake_case__ )
          snake_case       :   List[Any]							=      ret["log"]
          # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
          return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



   @staticmethod
   def 				_SCREAMING_SNAKE_CASE   (snake_case__		:					List[Any]    ,						snake_case__		:					List[str] )		->	str:


          '''simple docstring'''


          BaseTransformer.add_model_specific_args(snake_case__    ,						snake_case__ )
          parser.add_argument(
              "--max_seq_length"    ,						default=1_28    ,						type=snake_case__    ,						help=(
                  "The maximum total input sequence length after tokenization. Sequences longer "
                  "than this will be truncated, sequences shorter will be padded."
              )    ,						)

          parser.add_argument(
              "--task"    ,						default=""    ,						type=snake_case__    ,						required=snake_case__    ,						help="The GLUE task to run"    ,						)
          parser.add_argument(
              "--gpus"    ,						default=0    ,						type=snake_case__    ,						help="The number of GPUs allocated for this, it is by default 0 meaning none"    ,						)

          parser.add_argument(
              "--overwrite_cache"    ,						action="store_true"    ,						help="Overwrite the cached training and evaluation sets" )

          return parser



def 			UpperCamelCase					(    ):
       snake_case       :   int							=      argparse.ArgumentParser()
       add_generic_args(__lowerCamelCase				,					os.getcwd()    )
       snake_case       :   Tuple							=      GLUETransformer.add_model_specific_args(__lowerCamelCase				,					os.getcwd()    )
       snake_case       :   Dict							=      parser.parse_args()

       # If output_dir not provided, a folder will be generated in pwd
       if args.output_dir is None:
              snake_case       :   int							=      os.path.join(
                  "./results"				,					f"""{args.task}_{time.strftime('%Y%m%d_%H%M%S'    )}"""				,					)
              os.makedirs(args.output_dir    )

       snake_case       :   Optional[int]							=      GLUETransformer(__lowerCamelCase    )
       snake_case       :   str							=      generic_train(__lowerCamelCase				,					__lowerCamelCase    )

       # Optionally, predict on dev set and write to output_dir
       if args.do_predict:
              snake_case       :   str							=      sorted(glob.glob(os.path.join(args.output_dir				,					"checkpoint-epoch=*.ckpt"    )				,					recursive=__lowerCamelCase    )    )
              snake_case       :   List[str]							=      model.load_from_checkpoint(checkpoints[-1]    )
              return trainer.test(__lowerCamelCase    )


if __name__ == "__main__":
   main()










import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english


@require_tokenizers
class       a							(			__lowerCamelCase  ,							unittest.TestCase				):
		__lowerCAmelCase	:    int											=					BertTokenizer
		__lowerCAmelCase	:    int											=					BertTokenizerFast
		__lowerCAmelCase	:    int											=					True
		__lowerCAmelCase	:    List[Any]											=					True
		__lowerCAmelCase	:    Any											=					filter_non_english



		def 	__lowerCamelCase			(     self     :str  ):
						super().setUp()

						snake_case__	:      str     		=	[
						    '''[UNK]''',
						    '''[CLS]''',
						    '''[SEP]''',
						    '''[PAD]''',
						    '''[MASK]''',
						    '''want''',
						    '''##want''',
						    '''##ed''',
						    '''wa''',
						    '''un''',
						    '''runn''',
						    '''##ing''',
						    ''',''',
						    '''low''',
						    '''lowest''',
						]
						snake_case__	:      str     		=	os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES['''vocab_file''']  )
						with open(self.vocab_file   ,'''w'''   ,encoding='''utf-8'''  ) as vocab_writer:
										vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )



		def 	__lowerCamelCase			(     self     :List[str]   ,__lowercase     :str  ):
						snake_case__	:      List[Any]     		=	'''UNwant\u00E9d,running'''
						snake_case__	:      str     		=	'''unwanted, running'''
						return input_text, output_text



		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						snake_case__	:      str     		=	self.tokenizer_class(self.vocab_file  )

						snake_case__	:      str     		=	tokenizer.tokenize('''UNwant\u00E9d,running'''  )
						self.assertListEqual(__lowercase   ,['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']  )
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase  )   ,[9, 6, 7, 1_2, 1_0, 1_1]  )



		def 	__lowerCamelCase			(     self     :List[str]  ):
						if not self.test_rust_tokenizer:
										return

						snake_case__	:      str     		=	self.get_tokenizer()
						snake_case__	:      Dict     		=	self.get_rust_tokenizer()

						snake_case__	:      List[Any]     		=	'''UNwant\u00E9d,running'''

						snake_case__	:      Dict     		=	tokenizer.tokenize(__lowercase  )
						snake_case__	:      int     		=	rust_tokenizer.tokenize(__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )

						snake_case__	:      List[str]     		=	tokenizer.encode(__lowercase   ,add_special_tokens=__lowercase  )
						snake_case__	:      List[str]     		=	rust_tokenizer.encode(__lowercase   ,add_special_tokens=__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )

						snake_case__	:      Optional[Any]     		=	self.get_rust_tokenizer()
						snake_case__	:      List[Any]     		=	tokenizer.encode(__lowercase  )
						snake_case__	:      Dict     		=	rust_tokenizer.encode(__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )

						# With lower casing
						snake_case__	:      List[Any]     		=	self.get_tokenizer(do_lower_case=__lowercase  )
						snake_case__	:      Optional[int]     		=	self.get_rust_tokenizer(do_lower_case=__lowercase  )

						snake_case__	:      int     		=	'''UNwant\u00E9d,running'''

						snake_case__	:      List[str]     		=	tokenizer.tokenize(__lowercase  )
						snake_case__	:      Union[str, Any]     		=	rust_tokenizer.tokenize(__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )

						snake_case__	:      int     		=	tokenizer.encode(__lowercase   ,add_special_tokens=__lowercase  )
						snake_case__	:      List[str]     		=	rust_tokenizer.encode(__lowercase   ,add_special_tokens=__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )

						snake_case__	:      Tuple     		=	self.get_rust_tokenizer()
						snake_case__	:      int     		=	tokenizer.encode(__lowercase  )
						snake_case__	:      Dict     		=	rust_tokenizer.encode(__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :Tuple  ):
						snake_case__	:      Optional[Any]     		=	BasicTokenizer()

						self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz'''  )   ,['''ah''', '''\u535A''', '''\u63A8''', '''zz''']  )



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      Dict     		=	BasicTokenizer(do_lower_case=__lowercase  )

						self.assertListEqual(
						    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''  )   ,['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
						self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )   ,['''hello''']  )



		def 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__	:      Optional[int]     		=	BasicTokenizer(do_lower_case=__lowercase   ,strip_accents=__lowercase  )

						self.assertListEqual(
						    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )   ,['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
						self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )   ,['''h\u00E9llo''']  )



		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Union[str, Any]     		=	BasicTokenizer(do_lower_case=__lowercase   ,strip_accents=__lowercase  )

						self.assertListEqual(
						    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )   ,['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
						self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )   ,['''hello''']  )



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      Union[str, Any]     		=	BasicTokenizer(do_lower_case=__lowercase  )

						self.assertListEqual(
						    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )   ,['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
						self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )   ,['''hello''']  )



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      str     		=	BasicTokenizer(do_lower_case=__lowercase  )

						self.assertListEqual(
						    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''  )   ,['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )



		def 	__lowerCamelCase			(     self     :Tuple  ):
						snake_case__	:      Any     		=	BasicTokenizer(do_lower_case=__lowercase   ,strip_accents=__lowercase  )

						self.assertListEqual(
						    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )   ,['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )



		def 	__lowerCamelCase			(     self     :int  ):
						snake_case__	:      str     		=	BasicTokenizer(do_lower_case=__lowercase   ,strip_accents=__lowercase  )

						self.assertListEqual(
						    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )   ,['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )



		def 	__lowerCamelCase			(     self     :Tuple  ):
						snake_case__	:      List[Any]     		=	BasicTokenizer(do_lower_case=__lowercase   ,never_split=['''[UNK]''']  )

						self.assertListEqual(
						    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]'''  )   ,['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]''']  )



		def 	__lowerCamelCase			(     self     :Dict  ):
						snake_case__	:      List[str]     		=	BasicTokenizer()
						snake_case__	:      Any     		=	'''a\n\'ll !!to?\'d of, can\'t.'''
						snake_case__	:      Optional[int]     		=	['''a''', '''\'''', '''ll''', '''!''', '''!''', '''to''', '''?''', '''\'''', '''d''', '''of''', ''',''', '''can''', '''\'''', '''t''', '''.''']
						self.assertListEqual(tokenizer.tokenize(__lowercase  )   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__	:      Any     		=	['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

						snake_case__	:      Optional[Any]     		=	{}
						for i, token in enumerate(__lowercase  ):
										snake_case__	:      Dict     		=	i
						snake_case__	:      int     		=	WordpieceTokenizer(vocab=__lowercase   ,unk_token='''[UNK]'''  )

						self.assertListEqual(tokenizer.tokenize(''''''  )   ,[]  )

						self.assertListEqual(tokenizer.tokenize('''unwanted running'''  )   ,['''un''', '''##want''', '''##ed''', '''runn''', '''##ing''']  )

						self.assertListEqual(tokenizer.tokenize('''unwantedX running'''  )   ,['''[UNK]''', '''runn''', '''##ing''']  )



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						self.assertTrue(_is_whitespace(''' '''  )  )
						self.assertTrue(_is_whitespace('''\t'''  )  )
						self.assertTrue(_is_whitespace('''\r'''  )  )
						self.assertTrue(_is_whitespace('''\n'''  )  )
						self.assertTrue(_is_whitespace('''\u00A0'''  )  )

						self.assertFalse(_is_whitespace('''A'''  )  )
						self.assertFalse(_is_whitespace('''-'''  )  )



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						self.assertTrue(_is_control('''\u0005'''  )  )

						self.assertFalse(_is_control('''A'''  )  )
						self.assertFalse(_is_control(''' '''  )  )
						self.assertFalse(_is_control('''\t'''  )  )
						self.assertFalse(_is_control('''\r'''  )  )



		def 	__lowerCamelCase			(     self     :str  ):
						self.assertTrue(_is_punctuation('''-'''  )  )
						self.assertTrue(_is_punctuation('''$'''  )  )
						self.assertTrue(_is_punctuation('''`'''  )  )
						self.assertTrue(_is_punctuation('''.'''  )  )

						self.assertFalse(_is_punctuation('''A'''  )  )
						self.assertFalse(_is_punctuation(''' '''  )  )



		def 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__	:      str     		=	self.get_tokenizer()
						snake_case__	:      List[str]     		=	self.get_rust_tokenizer()

						# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
						self.assertListEqual([tokenizer.tokenize(__lowercase  ) for t in ['''Test''', '''\xad''', '''test''']]   ,[['''[UNK]'''], [], ['''[UNK]''']]  )

						self.assertListEqual(
						    [rust_tokenizer.tokenize(__lowercase  ) for t in ['''Test''', '''\xad''', '''test''']]   ,[['''[UNK]'''], [], ['''[UNK]''']]  )



		@slow
		def 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__	:      List[str]     		=	self.tokenizer_class.from_pretrained('''bert-base-uncased'''  )

						snake_case__	:      Union[str, Any]     		=	tokenizer.encode('''sequence builders'''   ,add_special_tokens=__lowercase  )
						snake_case__	:      str     		=	tokenizer.encode('''multi-sequence build'''   ,add_special_tokens=__lowercase  )

						snake_case__	:      Tuple     		=	tokenizer.build_inputs_with_special_tokens(__lowercase  )
						snake_case__	:      int     		=	tokenizer.build_inputs_with_special_tokens(__lowercase   ,__lowercase  )

						assert encoded_sentence == [1_0_1] + text + [1_0_2]
						assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
														snake_case__	:      str     		=	self.rust_tokenizer_class.from_pretrained(__lowercase   ,**__lowercase  )

														snake_case__	:      List[str]     		=	F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
														snake_case__	:      List[Any]     		=	tokenizer_r.encode_plus(
														    __lowercase   ,return_attention_mask=__lowercase   ,return_token_type_ids=__lowercase   ,return_offsets_mapping=__lowercase   ,add_special_tokens=__lowercase   ,)

														snake_case__	:      List[str]     		=	tokenizer_r.do_lower_case if hasattr(__lowercase   ,'''do_lower_case'''  ) else False
														snake_case__	:      Optional[int]     		=	(
														    [
														        ((0, 0), tokenizer_r.cls_token),
														        ((0, 1), '''A'''),
														        ((1, 2), ''','''),
														        ((3, 5), '''na'''),
														        ((5, 6), '''##ï'''),
														        ((6, 8), '''##ve'''),
														        ((9, 1_5), tokenizer_r.mask_token),
														        ((1_6, 2_1), '''Allen'''),
														        ((2_1, 2_3), '''##NL'''),
														        ((2_3, 2_4), '''##P'''),
														        ((2_5, 3_3), '''sentence'''),
														        ((3_3, 3_4), '''.'''),
														        ((0, 0), tokenizer_r.sep_token),
														    ]
														    if not do_lower_case
														    else [
														        ((0, 0), tokenizer_r.cls_token),
														        ((0, 1), '''a'''),
														        ((1, 2), ''','''),
														        ((3, 8), '''naive'''),
														        ((9, 1_5), tokenizer_r.mask_token),
														        ((1_6, 2_1), '''allen'''),
														        ((2_1, 2_3), '''##nl'''),
														        ((2_3, 2_4), '''##p'''),
														        ((2_5, 3_3), '''sentence'''),
														        ((3_3, 3_4), '''.'''),
														        ((0, 0), tokenizer_r.sep_token),
														    ]
														)

														self.assertEqual(
														    [e[1] for e in expected_results]   ,tokenizer_r.convert_ids_to_tokens(tokens['''input_ids''']  )  )
														self.assertEqual([e[0] for e in expected_results]   ,tokens['''offset_mapping''']  )






		def 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__	:      str     		=	['''的''', '''人''', '''有''']
						snake_case__	:      Optional[int]     		=	''''''.join(__lowercase  )
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
														snake_case__	:      Optional[Any]     		=	True
														snake_case__	:      int     		=	self.tokenizer_class.from_pretrained(__lowercase   ,**__lowercase  )
														snake_case__	:      Tuple     		=	self.rust_tokenizer_class.from_pretrained(__lowercase   ,**__lowercase  )

														snake_case__	:      Union[str, Any]     		=	tokenizer_p.encode(__lowercase   ,add_special_tokens=__lowercase  )
														snake_case__	:      List[str]     		=	tokenizer_r.encode(__lowercase   ,add_special_tokens=__lowercase  )

														snake_case__	:      List[str]     		=	tokenizer_r.convert_ids_to_tokens(__lowercase  )
														snake_case__	:      Optional[Any]     		=	tokenizer_p.convert_ids_to_tokens(__lowercase  )

														# it is expected that each Chinese character is not preceded by "##"
														self.assertListEqual(__lowercase   ,__lowercase  )
														self.assertListEqual(__lowercase   ,__lowercase  )

														snake_case__	:      str     		=	False
														snake_case__	:      Dict     		=	self.rust_tokenizer_class.from_pretrained(__lowercase   ,**__lowercase  )
														snake_case__	:      int     		=	self.tokenizer_class.from_pretrained(__lowercase   ,**__lowercase  )

														snake_case__	:      int     		=	tokenizer_r.encode(__lowercase   ,add_special_tokens=__lowercase  )
														snake_case__	:      Tuple     		=	tokenizer_p.encode(__lowercase   ,add_special_tokens=__lowercase  )

														snake_case__	:      List[Any]     		=	tokenizer_r.convert_ids_to_tokens(__lowercase  )
														snake_case__	:      Union[str, Any]     		=	tokenizer_p.convert_ids_to_tokens(__lowercase  )

														# it is expected that only the first Chinese character is not preceded by "##".
														snake_case__	:      Optional[int]     		=	[
														    F"""##{token}""" if idx != 0 else token for idx, token in enumerate(__lowercase  )
														]
														self.assertListEqual(__lowercase   ,__lowercase  )
														self.assertListEqual(__lowercase   ,__lowercase  )

