






'''simple docstring'''






import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
      import torch

__A     						= logging.get_logger(__name__)




def 							_SCREAMING_SNAKE_CASE	(					)     ->			Optional[Any]:







   """simple docstring"""
   return importlib.util.find_spec('deepspeed'	) is not None


if is_accelerate_available() and is_deepspeed_available():
      from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
      # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
      # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
      from builtins import object as DeepSpeedConfig




class a_     (     UpperCamelCase_				):







   def __init__(self       , __a)      ->   Optional[Any]:
      """simple docstring"""






      set_hf_deepspeed_config(self)
      dep_version_check('accelerate')
      dep_version_check('deepspeed')
      super().__init__(__a)




class a_     (     UpperCamelCase_				):







   def __init__(self       , __a)      ->   List[str]:
      """simple docstring"""






      super().__init__(__a)
      __snake_case					:   Dict  				=			None
      __snake_case					:   Optional[Any]  				=			[]







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      if self._dtype is None:
         raise ValueError('trainer_config_process() wasn\'t called yet to tell dtype')
      return self._dtype







   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   Dict  				=			self.get_value(__a)
      if val is None:
         return False
      else:
         return val == "auto"







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a=None       , __a=True)      ->   int:
      """simple docstring"""






      __snake_case				,__snake_case					:   str  				=			self.find_config_node(__a)
      if config is None:
         return

      if config.get(__a) == "auto":
         __snake_case					:   List[Any]  				=			hf_val
         return

      if not must_match:
         return

      __snake_case					:   Optional[Any]  				=			config.get(__a)
      if ds_val is not None and ds_val != hf_val:
         self.mismatches.append(F"""- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}""")

   _snake_case	   =				partialmethod(UpperCamelCase_		,   must_match=UpperCamelCase_				)







   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   str  				=			args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
      self.fill_match(
          'train_micro_batch_size_per_gpu'       , args.per_device_train_batch_size       , 'per_device_train_batch_size')
      self.fill_match('gradient_accumulation_steps'       , args.gradient_accumulation_steps       , 'gradient_accumulation_steps')
      self.fill_match('train_batch_size'       , __a       , 'train_batch_size (calculated)')
      self.fill_match('gradient_clipping'       , args.max_grad_norm       , 'max_grad_norm')

      self.fill_match('optimizer.params.lr'       , args.learning_rate       , 'learning_rate')
      self.fill_match('optimizer.params.betas'       , [args.adam_betaa, args.adam_betaa]       , 'adam_beta1+adam_beta2')
      self.fill_match('optimizer.params.eps'       , args.adam_epsilon       , 'adam_epsilon')
      self.fill_match('optimizer.params.weight_decay'       , args.weight_decay       , 'weight_decay')

      self.fill_only('scheduler.params.warmup_min_lr'       , 0)  # not a trainer arg
      self.fill_match('scheduler.params.warmup_max_lr'       , args.learning_rate       , 'learning_rate')
      # total_num_steps - will get set in trainer_config_finalize

      # fp16
      if args.fpaa or args.fpaa_full_eval:
         __snake_case					:   Optional[Any]  				=			'apex' if args.fpaa_backend == 'apex' else 'amp'
      else:
         __snake_case					:   Optional[int]  				=			None

      if args.save_on_each_node:
         # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
         __snake_case					:   Union[str, Any]  				=			self.config.get('checkpoint'       , {})
         __snake_case					:   List[str]  				=			args.save_on_each_node

      # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
      # any here unless the user did the work
      self.fill_match(
          'fp16.enabled'       , ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == 'amp')       , 'fp16|fp16_full_eval+fp16_backend(amp)'       , )

      # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
      # ZeRO features
      self.fill_match('amp.enabled'       , fpaa_backend == 'apex'       , 'fp16+fp16_backend(apex)')
      self.fill_match('amp.opt_level'       , args.fpaa_opt_level       , 'fp16_opt_level')

      self.fill_match('bf16.enabled'       , (args.bfaa or args.bfaa_full_eval)       , 'bf16|bf16_full_eval')

      # deepspeed's default mode is fp16 unless there is a config that says differently
      if self.is_true('bf16.enabled'):
         __snake_case					:   List[str]  				=			torch.bfloataa
      elif self.is_false('fp16.enabled'):
         __snake_case					:   Union[str, Any]  				=			torch.floataa
      else:
         __snake_case					:   List[str]  				=			torch.floataa







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a)      ->   Any:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			[
          'zero_optimization.reduce_bucket_size',
          'zero_optimization.stage3_prefetch_bucket_size',
          'zero_optimization.stage3_param_persistence_threshold',
      ]
      __snake_case					:   int  				=			[x for x in hidden_size_based_keys if self.is_auto(__a)]

      if len(__a) > 0:
         if hasattr(model.config       , 'hidden_size'):
            __snake_case					:   Optional[int]  				=			model.config.hidden_size
         elif hasattr(model.config       , 'hidden_sizes'):
            # if there are many hidden sizes pick the largest one
            __snake_case					:   Tuple  				=			max(model.config.hidden_sizes)
         else:
            raise ValueError(
                'The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '
                'therefore it\'s not possible to automatically fill out the following `auto` entries '
                F"""in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing """
                '`auto` values for these keys with an integer value of your choice.')

         self.fill_only('zero_optimization.reduce_bucket_size'       , hidden_size * hidden_size)
         if self.is_zeroa():
            # automatically assign the optimal config values based on model config
            self.fill_only('zero_optimization.stage3_prefetch_bucket_size'       , 0.9 * hidden_size * hidden_size)
            self.fill_only('zero_optimization.stage3_param_persistence_threshold'       , 1_0 * hidden_size)

        # scheduler
      self.fill_match('scheduler.params.total_num_steps'       , __a       , 'num_training_steps (calculated)')
      self.fill_match('scheduler.params.warmup_num_steps'       , args.get_warmup_steps(__a)       , 'warmup_steps')

      if len(self.mismatches) > 0:
         __snake_case					:   Any  				=			'\n'.join(self.mismatches)
         raise ValueError(
             'Please correct the following DeepSpeed config values that mismatch TrainingArguments'
             F""" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'.""")


__A     						= None




def 							_SCREAMING_SNAKE_CASE	(					A	:							Dict	)     ->			Union[str, Any]:







   """simple docstring"""
   # this is a special weakref global object to allow us to get to Deepspeed config from APIs
   # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
   global _hf_deepspeed_config_weak_ref
   # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
   __snake_case					:   List[Any]  				=			weakref.ref(A	)




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







   """simple docstring"""
   # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
   global _hf_deepspeed_config_weak_ref
   __snake_case					:   Optional[Any]  				=			None




def 							_SCREAMING_SNAKE_CASE	(					)     ->			str:







   """simple docstring"""
   if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
      return _hf_deepspeed_config_weak_ref().is_zeroa()
   else:
      return False




def 							_SCREAMING_SNAKE_CASE	(					)     ->			List[Any]:







   """simple docstring"""
   if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
      return _hf_deepspeed_config_weak_ref().config
   else:
      return None




def 							_SCREAMING_SNAKE_CASE	(					A	:							Dict	,		A	:							int	,		A	:							Optional[Any]	,		A	:							Optional[Any]	,		A	:							str	)     ->			Union[str, Any]:







   """simple docstring"""
   from accelerate.utils import DummyOptim, DummyScheduler

   __snake_case					:   Tuple  				=			hf_deepspeed_config.config

   # Optimizer + Scheduler
   # Currently supported combos:
   # 1. DS scheduler + DS optimizer: Yes
   # 2. HF scheduler + HF optimizer: Yes
   # 3. DS scheduler + HF optimizer: Yes
   # 4. HF scheduler + DS optimizer: No
   #
   # Unless Offload is enabled in which case it's:
   # 1. DS scheduler + DS optimizer: Yes
   # 2. HF scheduler + HF optimizer: Mostly*
   # 3. DS scheduler + HF optimizer: Mostly*
   # 4. HF scheduler + DS optimizer: No
   #
   # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

   __snake_case					:   Any  				=			None
   if "optimizer" in config:
      if args.adafactor:
         raise ValueError(
             '--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '
             'Only one optimizer can be configured.'	)
      __snake_case					:   Union[str, Any]  				=			DummyOptim(params=A	)
   else:
      if hf_deepspeed_config.is_offload():
         logger.info(
             'Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'
             ' custom optimizer has both CPU and GPU implementation (except LAMB)'	)

      # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
      # But trainer uses AdamW by default.
      __snake_case					:   Optional[int]  				=			trainer.create_optimizer()
      # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
      __snake_case					:   Dict  				=			True

   __snake_case					:   List[Any]  				=			None
   if "scheduler" in config:
      __snake_case					:   Optional[int]  				=			DummyScheduler(A	)
   else:
      if isinstance(A	,		A	):
         raise ValueError(
             'Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '
             'Please configure a scheduler in the DeepSpeed config.'	)
      __snake_case					:   int  				=			trainer.create_scheduler(num_training_steps=A	,		optimizer=A	)

   return optimizer, lr_scheduler




def 							_SCREAMING_SNAKE_CASE	(					A	:							Dict	,		A	:							List[Any]	,		A	:							str=False	)     ->			Union[str, Any]:







   """simple docstring"""
   from deepspeed.utils import logger as ds_logger

   __snake_case					:   int  				=			trainer.model
   __snake_case					:   Any  				=			trainer.args

   __snake_case					:   List[str]  				=			trainer.accelerator.state.deepspeed_plugin.hf_ds_config

   # resume config update - some bits like `model` and `num_training_steps` only become available during train
   hf_deepspeed_config.trainer_config_finalize(A	,		A	,		A	)

   # set the Deepspeed log level consistent with the Trainer
   ds_logger.setLevel(args.get_process_log_level()	)

   if inference:
      # only Z3 makes sense for the inference
      if not hf_deepspeed_config.is_zeroa():
         raise ValueError('ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'	)

      # in case the training config is re-used for inference
      hf_deepspeed_config.del_config_sub_tree('optimizer'	)
      hf_deepspeed_config.del_config_sub_tree('lr_scheduler'	)
      __snake_case				,__snake_case					:   Any  				=			None, None
      __snake_case					:   int  				=			None
   else:
      __snake_case					:   str  				=			None  # important for when deepspeed_init is used as re-init
      __snake_case					:   Optional[int]  				=			list(filter(lambda A	: p.requires_grad	,		model.parameters()	)	)
      __snake_case				,__snake_case					:   List[Any]  				=			deepspeed_optim_sched(
          A	,		A	,		A	,		A	,		A	)

   # keep for quick debug:
   # from pprint import pprint; pprint(config)

   return optimizer, lr_scheduler





def 							_SCREAMING_SNAKE_CASE	(					A	:							Any	,		A	:							int	)     ->			List[Any]:







   """simple docstring"""
   # it's possible that the user is trying to resume from model_path, which doesn't necessarily
   # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
   # a resume from a checkpoint and not just a local pretrained weight. So we check here if the
   # path contains what looks like a deepspeed checkpoint
   import glob

   __snake_case					:   int  				=			sorted(glob.glob(F"""{checkpoint_path}/global_step*"""	)	)

   if len(A	) > 0:
      logger.info(F"""Attempting to resume from {checkpoint_path}"""	)
      # this magically updates self.optimizer and self.lr_scheduler
      __snake_case				,__snake_case					:   Optional[Any]  				=			deepspeed_engine.load_checkpoint(
          A	,		load_optimizer_states=A	,		load_lr_scheduler_states=A	)
      if load_path is None:
         raise ValueError(F"""[deepspeed] failed to resume from checkpoint {checkpoint_path}"""	)
   else:
      raise ValueError(F"""Can't find a valid checkpoint at {checkpoint_path}"""	)







'''simple docstring'''






import os
import unittest

from transformers import ElectraTokenizerFast
from transformers.models.electra.tokenization_electra import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    ElectraTokenizer,
    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_     (     UpperCamelCase_		,   unittest.TestCase				):
   _snake_case	   =				ElectraTokenizer
   _snake_case	   =				ElectraTokenizerFast
   _snake_case	   =				True
   _snake_case	   =				True
   _snake_case	   =				filter_non_english







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      super().setUp()

      __snake_case					:   List[Any]  				=			[
          '[UNK]',
          '[CLS]',
          '[SEP]',
          '[PAD]',
          '[MASK]',
          'want',
          '##want',
          '##ed',
          'wa',
          'un',
          'runn',
          '##ing',
          ',',
          'low',
          'lowest',
      ]
      __snake_case					:   int  				=			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      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			'UNwant\u00E9d,running'
      __snake_case					:   Tuple  				=			'unwanted, running'
      return input_text, output_text







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			self.tokenizer_class(self.vocab_file)

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      if not self.test_rust_tokenizer:
         return

      __snake_case					:   Union[str, Any]  				=			self.get_tokenizer()
      __snake_case					:   Dict  				=			self.get_rust_tokenizer()

      __snake_case					:   Dict  				=			'UNwant\u00E9d,running'

      __snake_case					:   Tuple  				=			tokenizer.tokenize(__a)
      __snake_case					:   List[Any]  				=			rust_tokenizer.tokenize(__a)
      self.assertListEqual(__a       , __a)

      __snake_case					:   Any  				=			tokenizer.encode(__a       , add_special_tokens=__a)
      __snake_case					:   int  				=			rust_tokenizer.encode(__a       , add_special_tokens=__a)
      self.assertListEqual(__a       , __a)

      __snake_case					:   List[str]  				=			self.get_rust_tokenizer()
      __snake_case					:   Optional[int]  				=			tokenizer.encode(__a)
      __snake_case					:   Dict  				=			rust_tokenizer.encode(__a)
      self.assertListEqual(__a       , __a)

      # With lower casing
      __snake_case					:   Union[str, Any]  				=			self.get_tokenizer(do_lower_case=__a)
      __snake_case					:   List[Any]  				=			self.get_rust_tokenizer(do_lower_case=__a)

      __snake_case					:   List[str]  				=			'UNwant\u00E9d,running'

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

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

      __snake_case					:   List[Any]  				=			self.get_rust_tokenizer()
      __snake_case					:   Optional[Any]  				=			tokenizer.encode(__a)
      __snake_case					:   Tuple  				=			rust_tokenizer.encode(__a)
      self.assertListEqual(__a       , __a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   int  				=			BasicTokenizer()

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			BasicTokenizer(do_lower_case=__a)

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   Any  				=			BasicTokenizer(do_lower_case=__a       , strip_accents=__a)

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			BasicTokenizer(do_lower_case=__a       , strip_accents=__a)

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      __snake_case					:   Tuple  				=			BasicTokenizer(do_lower_case=__a)

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   Dict  				=			BasicTokenizer(do_lower_case=__a)

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			BasicTokenizer(do_lower_case=__a       , strip_accents=__a)

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			BasicTokenizer(do_lower_case=__a       , strip_accents=__a)

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			BasicTokenizer(do_lower_case=__a       , never_split=['[UNK]'])

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

      __snake_case					:   List[str]  				=			{}
      for i, token in enumerate(__a):
         __snake_case					:   str  				=			i
      __snake_case					:   List[str]  				=			WordpieceTokenizer(vocab=__a       , 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      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      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      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      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      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      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      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   Dict  				=			self.get_tokenizer()
      __snake_case					:   Dict  				=			self.get_rust_tokenizer()

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

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







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   str  				=			self.tokenizer_class.from_pretrained('google/electra-base-discriminator')

      __snake_case					:   Optional[Any]  				=			tokenizer.encode('sequence builders'       , add_special_tokens=__a)
      __snake_case					:   List[Any]  				=			tokenizer.encode('multi-sequence build'       , add_special_tokens=__a)

      __snake_case					:   Tuple  				=			tokenizer.build_inputs_with_special_tokens(__a)
      __snake_case					:   int  				=			tokenizer.build_inputs_with_special_tokens(__a       , __a)

      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      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      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(__a       , **__a)

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

            __snake_case					:   int  				=			tokenizer_r.do_lower_case if hasattr(__a       , 'do_lower_case') else False
            __snake_case					:   Any  				=			(
                [
                    ((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      SCREAMING_SNAKE_CASE__       (self)      ->   Any:
      """simple docstring"""






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

            __snake_case					:   List[str]  				=			tokenizer_p.encode(__a       , add_special_tokens=__a)
            __snake_case					:   Optional[Any]  				=			tokenizer_r.encode(__a       , add_special_tokens=__a)

            __snake_case					:   Union[str, Any]  				=			tokenizer_r.convert_ids_to_tokens(__a)
            __snake_case					:   List[Any]  				=			tokenizer_p.convert_ids_to_tokens(__a)

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

            __snake_case					:   Optional[int]  				=			False
            __snake_case					:   Any  				=			self.rust_tokenizer_class.from_pretrained(__a       , **__a)
            __snake_case					:   List[str]  				=			self.tokenizer_class.from_pretrained(__a       , **__a)

            __snake_case					:   List[str]  				=			tokenizer_r.encode(__a       , add_special_tokens=__a)
            __snake_case					:   Optional[Any]  				=			tokenizer_p.encode(__a       , add_special_tokens=__a)

            __snake_case					:   List[str]  				=			tokenizer_r.convert_ids_to_tokens(__a)
            __snake_case					:   Optional[int]  				=			tokenizer_p.convert_ids_to_tokens(__a)

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