code: 
  import unittest
  from functools import partial
  
  import torch
  
  from accelerate import Accelerator, debug_launcher
  from accelerate.state import AcceleratorState, GradientState
  from accelerate.test_utils import require_cpu, require_huggingface_suite
  from accelerate.utils import GradientAccumulationPlugin
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Optional[Any]=2   ,     _snake_case   :						Tuple=True   ,     _snake_case   :						Dict=False     )       ->	Optional[Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Optional[int]			     =  Accelerator(step_scheduler_with_optimizer=_snake_case   ,     split_batches=_snake_case     )
  	__magic_name__	:	Any			     =  torch.nn.Linear(2   ,     4     )
  	__magic_name__	:	Dict			     =  torch.optim.AdamW(model.parameters()   ,     lr=1.0     )
  	__magic_name__	:	Optional[int]			     =  torch.optim.lr_scheduler.OneCycleLR(_snake_case   ,     max_lr=0.01   ,     steps_per_epoch=2   ,     epochs=1     )
  	__magic_name__     ,					__magic_name__     ,					__magic_name__	:	int			     =  accelerator.prepare(_snake_case   ,     _snake_case   ,     _snake_case     )
  
  	# Optimizer has stepped
  	scheduler.step()
  	if step_scheduler_with_optimizer or (num_processes == 1):
  		assert (
  		    scheduler.scheduler.last_epoch == num_processes
  		), F'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
  	else:
  		assert (
  		    scheduler.scheduler.last_epoch != num_processes
  		), F'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Optional[Any]=2   ,     _snake_case   :						List[Any]=True   ,     _snake_case   :						Optional[Any]=False     )       ->	int:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	List[Any]			     =  Accelerator(step_scheduler_with_optimizer=_snake_case   ,     split_batches=_snake_case     )
  	__magic_name__	:	Union[str, Any]			     =  torch.nn.Linear(2   ,     4     )
  	__magic_name__	:	str			     =  torch.optim.AdamW(model.parameters()   ,     lr=1.0     )
  	__magic_name__	:	Dict			     =  torch.optim.lr_scheduler.LambdaLR(_snake_case   ,     lr_lambda=lambda _snake_case     : 1 - n / 10     )
  	__magic_name__     ,					__magic_name__     ,					__magic_name__	:	Union[str, Any]			     =  accelerator.prepare(_snake_case   ,     _snake_case   ,     _snake_case     )
  
  	# Optimizer has stepped
  	__magic_name__	:	Optional[Any]			     =  False
  	scheduler.step()
  	__magic_name__	:	List[str]			     =  1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
  	assert (
  	    scheduler.get_last_lr()[0] == expected_lr
  	), F'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''
  
  	# Optimizer has not stepped
  	__magic_name__	:	int			     =  True
  	scheduler.step()
  	if not step_scheduler_with_optimizer:
  		__magic_name__	:	Optional[int]			     =  1 - 2 / 10
  	assert (
  	    scheduler.get_last_lr()[0] == expected_lr
  	), F'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						int = 2     )       ->	Union[str, Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	from transformers import get_linear_schedule_with_warmup
  
  	__magic_name__	:	int			     =  [1, 2, 4]
  	for num_steps in steps:
  		__magic_name__	:	List[str]			     =  GradientAccumulationPlugin(num_steps=_snake_case   ,     adjust_scheduler=num_steps > 1     )
  		__magic_name__	:	Union[str, Any]			     =  Accelerator(gradient_accumulation_plugin=_snake_case     )
  		__magic_name__	:	str			     =  torch.nn.Linear(2   ,     4     )
  		__magic_name__	:	int			     =  torch.optim.AdamW(model.parameters()   ,     lr=10.0     )
  		__magic_name__	:	List[str]			     =  get_linear_schedule_with_warmup(optimizer=_snake_case   ,     num_warmup_steps=0   ,     num_training_steps=20     )
  
  		__magic_name__     ,					__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  accelerator.prepare(_snake_case   ,     _snake_case   ,     _snake_case     )
  
  		for i in range(10 * num_steps     ):
  			with accelerator.accumulate(_snake_case     ):
  				optimizer.step()
  				scheduler.step()
  
  			if i == (10 * num_steps - 2):
  				assert (
  				    scheduler.get_last_lr()[0] != 0
  				), F'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
  		assert (
  		    scheduler.get_last_lr()[0] == 0
  		), F'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
  		GradientState._reset_state()
  
  
  
  
  
  @require_cpu
  class    _snake_case      (  unittest.TestCase       ):
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				debug_launcher(partial(_a		,     num_processes=1	)		,     num_processes=1	)
  				debug_launcher(partial(_a		,     num_processes=1		,     split_batches=_a	)		,     num_processes=1	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				debug_launcher(partial(_a		,     num_processes=1	)		,     num_processes=1	)
  				debug_launcher(partial(_a		,     num_processes=1		,     split_batches=_a	)		,     num_processes=1	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				debug_launcher(partial(_a		,     num_processes=1		,     step_scheduler_with_optimizer=_a	)		,     num_processes=1	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				debug_launcher(partial(_a		,     num_processes=1		,     step_scheduler_with_optimizer=_a	)		,     num_processes=1	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				AcceleratorState._reset_state(_a	)
  				debug_launcher(_a	)
  				debug_launcher(partial(_a		,     num_processes=1		,     split_batches=_a	)		,     num_processes=1	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				AcceleratorState._reset_state(_a	)
  				debug_launcher(_a	)
  				debug_launcher(partial(_a		,     num_processes=1		,     split_batches=_a	)		,     num_processes=1	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				AcceleratorState._reset_state(_a	)
  				debug_launcher(partial(_a		,     step_scheduler_with_optimizer=_a	)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				AcceleratorState._reset_state(_a	)
  				debug_launcher(partial(_a		,     step_scheduler_with_optimizer=_a	)	)
  
  
  
  
  
  
  
  			@require_huggingface_suite
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				AcceleratorState._reset_state(_a	)
  				debug_launcher(partial(_a		,     num_processes=1	)	)
  				debug_launcher(_a	)
  
style_context: 
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
  				from .tokenization_xlm_roberta import XLMRobertaTokenizer
  else:
  				snake_case				:	Any							   =						None
  
  
  snake_case				:	Union[str, Any]							   =						logging.get_logger(__name__)
  
  snake_case				:	Any							   =						{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
  
  snake_case				:	Optional[Any]							   =						{
      "vocab_file": {
          "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
          "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
          "xlm-roberta-large-finetuned-conll02-dutch": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll02-spanish": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll03-english": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll03-german": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
          ),
      },
      "tokenizer_file": {
          "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
          "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
          "xlm-roberta-large-finetuned-conll02-dutch": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll02-spanish": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll03-english": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll03-german": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
          ),
      },
  }
  
  snake_case				:	Optional[int]							   =						{
      "xlm-roberta-base": 512,
      "xlm-roberta-large": 512,
      "xlm-roberta-large-finetuned-conll02-dutch": 512,
      "xlm-roberta-large-finetuned-conll02-spanish": 512,
      "xlm-roberta-large-finetuned-conll03-english": 512,
      "xlm-roberta-large-finetuned-conll03-german": 512,
  }
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  			UpperCamelCase__					   =					VOCAB_FILES_NAMES
  			UpperCamelCase__					   =					PRETRAINED_VOCAB_FILES_MAP
  			UpperCamelCase__					   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  			UpperCamelCase__					   =					['input_ids', 'attention_mask']
  			UpperCamelCase__					   =					XLMRobertaTokenizer
  
  
  
  
  
  
  
  			def __init__(       self		,     _a=None		,     _a=None		,     _a="<s>"		,     _a="</s>"		,     _a="</s>"		,     _a="<s>"		,     _a="<unk>"		,     _a="<pad>"		,     _a="<mask>"		,     **_a		,     ):
  				# Mask token behave like a normal word, i.e. include the space before it
  				__magic_name__	:	List[str]			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else mask_token
  
  				super().__init__(
  				    _a		,     tokenizer_file=_a		,     bos_token=_a		,     eos_token=_a		,     sep_token=_a		,     cls_token=_a		,     unk_token=_a		,     pad_token=_a		,     mask_token=_a		,     **_a		,     )
  
  				__magic_name__	:	Optional[int]			     =  vocab_file
  				__magic_name__	:	List[str]			     =  False if not self.vocab_file else True
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  
  				if token_ids_a is None:
  					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  				__magic_name__	:	Union[str, Any]			     =  [self.cls_token_id]
  				__magic_name__	:	str			     =  [self.sep_token_id]
  				return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				__magic_name__	:	List[str]			     =  [self.sep_token_id]
  				__magic_name__	:	List[str]			     =  [self.cls_token_id]
  
  				if token_ids_a is None:
  					return len(cls + token_ids_a + sep	) * [0]
  				return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				if not self.can_save_slow_tokenizer:
  					raise ValueError(
  					    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
  					    "tokenizer."	)
  
  				if not os.path.isdir(_a	):
  					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''	)
  					return
  				__magic_name__	:	Any			     =  os.path.join(
  				    _a		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
  
  				if os.path.abspath(self.vocab_file	) != os.path.abspath(_a	):
  					copyfile(self.vocab_file		,     _a	)
  
  				return (out_vocab_file,)
  