



import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A__								=						logging.getLogger(__name__)


@dataclass
class       a							:
		__lowerCAmelCase	:    str											=					field(
		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
		__lowerCAmelCase	:    Optional[str]											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
		__lowerCAmelCase	:    Optional[str]											=					field(
		    default="""NER"""  ,							metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}				)
		__lowerCAmelCase	:    Optional[str]											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
		__lowerCAmelCase	:    bool											=					field(default=__lowerCamelCase  ,							metadata={"""help""": """Set this flag to use fast tokenization."""}				)
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		__lowerCAmelCase	:    Optional[str]											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}  ,							)


@dataclass
class       a							:
		__lowerCAmelCase	:    str											=					field(
		    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}				)
		__lowerCAmelCase	:    Optional[str]											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}  ,							)
		__lowerCAmelCase	:    int											=					field(
		    default=1_28  ,							metadata={
		        """help""": (
		            """The maximum total input sequence length after tokenization. Sequences longer """
		            """than this will be truncated, sequences shorter will be padded."""
		        )
		    }  ,							)
		__lowerCAmelCase	:    bool											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)



def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      Any     		=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
				if len(sys.argv      ) == 2 and sys.argv[1].endswith('''.json'''      ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								snake_case__		, snake_case__		, snake_case__	:      str     		=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
				else:
								snake_case__		, snake_case__		, snake_case__	:      List[Any]     		=	parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir      )
				    and os.listdir(training_args.output_dir      )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    ''' --overwrite_output_dir to overcome.'''      )

				snake_case__	:      int     		=	import_module('''tasks'''      )
				try:
								snake_case__	:      Optional[int]     		=	getattr(__lowerCAmelCase  ,  model_args.task_type      )
								snake_case__	:      TokenClassificationTask     		=	token_classification_task_clazz()
				except AttributeError:
								raise ValueError(
								    f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
								    f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""      )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''  ,  datefmt='''%m/%d/%Y %H:%M:%S'''  ,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,  )
				logger.warning(
				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''  ,  training_args.local_rank  ,  training_args.device  ,  training_args.n_gpu  ,  bool(training_args.local_rank != -1      )  ,  training_args.fpaa  ,  )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank      ):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info('''Training/evaluation parameters %s'''  ,  __lowerCAmelCase      )

				# Set seed
				set_seed(training_args.seed      )

				# Prepare CONLL-2003 task
				snake_case__	:      Optional[int]     		=	token_classification_task.get_labels(data_args.labels      )
				snake_case__	:      Dict[int, str]     		=	dict(enumerate(__lowerCAmelCase      )      )
				snake_case__	:      Optional[Any]     		=	len(__lowerCAmelCase      )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				snake_case__	:      List[str]     		=	AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,  num_labels=__lowerCAmelCase  ,  idalabel=__lowerCAmelCase  ,  labelaid={label: i for i, label in enumerate(__lowerCAmelCase      )}  ,  cache_dir=model_args.cache_dir  ,  )
				snake_case__	:      List[str]     		=	AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,  cache_dir=model_args.cache_dir  ,  use_fast=model_args.use_fast  ,  )
				snake_case__	:      Tuple     		=	AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path  ,  from_tf=bool('''.ckpt''' in model_args.model_name_or_path      )  ,  config=__lowerCAmelCase  ,  cache_dir=model_args.cache_dir  ,  )

				# Get datasets
				snake_case__	:      Dict     		=	(
				    TokenClassificationDataset(
				        token_classification_task=__lowerCAmelCase  ,  data_dir=data_args.data_dir  ,  tokenizer=__lowerCAmelCase  ,  labels=__lowerCAmelCase  ,  model_type=config.model_type  ,  max_seq_length=data_args.max_seq_length  ,  overwrite_cache=data_args.overwrite_cache  ,  mode=Split.train  ,  )
				    if training_args.do_train
				    else None
				)
				snake_case__	:      Optional[Any]     		=	(
				    TokenClassificationDataset(
				        token_classification_task=__lowerCAmelCase  ,  data_dir=data_args.data_dir  ,  tokenizer=__lowerCAmelCase  ,  labels=__lowerCAmelCase  ,  model_type=config.model_type  ,  max_seq_length=data_args.max_seq_length  ,  overwrite_cache=data_args.overwrite_cache  ,  mode=Split.dev  ,  )
				    if training_args.do_eval
				    else None
				)

				def align_predictions(__lowerCAmelCase  ,  __lowerCAmelCase      ) -> Tuple[List[int], List[int]]:
								snake_case__	:      Any     		=	np.argmax(__lowerCAmelCase  ,  axis=2      )

								snake_case__		, snake_case__	:      List[Any]     		=	preds.shape

								snake_case__	:      List[Any]     		=	[[] for _ in range(__lowerCAmelCase      )]
								snake_case__	:      int     		=	[[] for _ in range(__lowerCAmelCase      )]

								for i in range(__lowerCAmelCase      ):
												for j in range(__lowerCAmelCase      ):
																if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																				out_label_list[i].append(label_map[label_ids[i][j]]      )
																				preds_list[i].append(label_map[preds[i][j]]      )

								return preds_list, out_label_list

				def compute_metrics(__lowerCAmelCase      ) -> Dict:
								snake_case__		, snake_case__	:      List[str]     		=	align_predictions(p.predictions  ,  p.label_ids      )
								return {
								    "accuracy_score": accuracy_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								    "precision": precision_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								    "recall": recall_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								    "f1": fa_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								}

				# Data collator
				snake_case__	:      Any     		=	DataCollatorWithPadding(__lowerCAmelCase  ,  pad_to_multiple_of=8      ) if training_args.fpaa else None

				# Initialize our Trainer
				snake_case__	:      Tuple     		=	Trainer(
				    model=__lowerCAmelCase  ,  args=__lowerCAmelCase  ,  train_dataset=__lowerCAmelCase  ,  eval_dataset=__lowerCAmelCase  ,  compute_metrics=__lowerCAmelCase  ,  data_collator=__lowerCAmelCase  ,  )

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_process_zero():
												tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
				snake_case__	:      Any     		=	{}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''      )

								snake_case__	:      Tuple     		=	trainer.evaluate()

								snake_case__	:      Optional[Any]     		=	os.path.join(training_args.output_dir  ,  '''eval_results.txt'''      )
								if trainer.is_world_process_zero():
												with open(__lowerCAmelCase  ,  '''w'''      ) as writer:
																logger.info('''***** Eval results *****'''      )
																for key, value in result.items():
																				logger.info('''  %s = %s'''  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )
																				writer.write('''%s = %s\n''' % (key, value)      )

												results.update(__lowerCAmelCase      )

    # Predict
				if training_args.do_predict:
								snake_case__	:      Optional[Any]     		=	TokenClassificationDataset(
								    token_classification_task=__lowerCAmelCase  ,  data_dir=data_args.data_dir  ,  tokenizer=__lowerCAmelCase  ,  labels=__lowerCAmelCase  ,  model_type=config.model_type  ,  max_seq_length=data_args.max_seq_length  ,  overwrite_cache=data_args.overwrite_cache  ,  mode=Split.test  ,  )

								snake_case__		, snake_case__		, snake_case__	:      List[str]     		=	trainer.predict(__lowerCAmelCase      )
								snake_case__		, snake_case__	:      int     		=	align_predictions(__lowerCAmelCase  ,  __lowerCAmelCase      )

								snake_case__	:      Optional[int]     		=	os.path.join(training_args.output_dir  ,  '''test_results.txt'''      )
								if trainer.is_world_process_zero():
												with open(__lowerCAmelCase  ,  '''w'''      ) as writer:
																for key, value in metrics.items():
																				logger.info('''  %s = %s'''  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )
																				writer.write('''%s = %s\n''' % (key, value)      )

        # Save predictions
								snake_case__	:      Any     		=	os.path.join(training_args.output_dir  ,  '''test_predictions.txt'''      )
								if trainer.is_world_process_zero():
												with open(__lowerCAmelCase  ,  '''w'''      ) as writer:
																with open(os.path.join(data_args.data_dir  ,  '''test.txt'''      )  ,  '''r'''      ) as f:
																				token_classification_task.write_predictions_to_file(__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )

				return results



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Union[str, Any]:


				"""simple docstring"""

				main()


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  )

