code: 
  import argparse
  import os
  
  # New Code #
  import evaluate
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
  
  from accelerate import Accelerator
  from accelerate.utils import find_executable_batch_size
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate,
  # specifically showcasing how to combine both the gradient accumulation
  # and automatic batch size finder utilities of Accelerate to perfrom
  # automatic gradient accumulation
  #
  # This example trains a Bert base model on GLUE MRPC
  # in any of the following settings (with the same script):
  #   - single CPU or single GPU
  #   - multi GPUS (using PyTorch distributed mode)
  #   - (multi) TPUs
  #   - fp16 (mixed-precision) or fp32 (normal precision)
  #
  # New additions from the base script can be found quickly by
  # looking for the # New Code # tags
  #
  # To run it in each of these various modes, follow the instructions
  # in the readme for examples:
  # https://github.com/huggingface/accelerate/tree/main/examples
  #
  ########################################################################
  
  UpperCamelCase_	:    Optional[Any]  					=       32
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Accelerator   , _UpperCAmelCase     :      int = 16     )      -> Union[str, Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Optional[int]    			=      AutoTokenizer.from_pretrained("bert-base-cased"     )
  						_lowercase					:  int    			=      load_dataset("glue"   , "mrpc"     )
  
  						def tokenize_function(_UpperCAmelCase     :      str     ):
  												# max_length=None => use the model max length (it's actually the default)
  												_lowercase					:  Optional[int]    			=      tokenizer(examples["sentence1"]   , examples["sentence2"]   , truncation=_UpperCAmelCase   , max_length=_UpperCAmelCase     )
  												return outputs
  
  						# Apply the method we just defined to all the examples in all the splits of the dataset
  						# starting with the main process first:
  						with accelerator.main_process_first():
  												_lowercase					:  Union[str, Any]    			=      datasets.map(
  												    _UpperCAmelCase   , batched=_UpperCAmelCase   , remove_columns=["idx", "sentence1", "sentence2"]   , )
  
  						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  						# transformers library
  						_lowercase					:  Dict    			=      tokenized_datasets.rename_column("label"   , "labels"     )
  
  						def collate_fn(_UpperCAmelCase     :      Optional[Any]     ):
  												# When using mixed precision we want round multiples of 8/16
  												if accelerator.mixed_precision == "fp8":
  																		_lowercase					:  Any    			=      16
  												elif accelerator.mixed_precision != "no":
  																		_lowercase					:  Union[str, Any]    			=      8
  												else:
  																		_lowercase					:  Optional[int]    			=      None
  
  												return tokenizer.pad(
  												    _UpperCAmelCase   , padding="longest"   , pad_to_multiple_of=_UpperCAmelCase   , return_tensors="pt"   , )
  
  						# Instantiate dataloaders.
  						_lowercase					:  Dict    			=      DataLoader(
  						    tokenized_datasets["train"]   , shuffle=_UpperCAmelCase   , collate_fn=_UpperCAmelCase   , batch_size=_UpperCAmelCase     )
  						_lowercase					:  Tuple    			=      DataLoader(
  						    tokenized_datasets["validation"]   , shuffle=_UpperCAmelCase   , collate_fn=_UpperCAmelCase   , batch_size=_UpperCAmelCase     )
  
  						return train_dataloader, eval_dataloader
  
  
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
  							from accelerate.test_utils.training import mocked_dataloaders
  
  							UpperCamelCase_	:    Union[str, Any]  					=       mocked_dataloaders  # noqa: F811
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Tuple   , _UpperCAmelCase     :      Union[str, Any]     )      -> Dict:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if os.environ.get("TESTING_MOCKED_DATALOADERS"   , _UpperCAmelCase     ) == "1":
  												_lowercase					:  Optional[Any]    			=      2
  						# Initialize accelerator
  						_lowercase					:  Any    			=      Accelerator(cpu=args.cpu   , mixed_precision=args.mixed_precision     )
  						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  						_lowercase					:  Any    			=      config["lr"]
  						_lowercase					:  Dict    			=      int(config["num_epochs"]     )
  						_lowercase					:  List[str]    			=      int(config["seed"]     )
  						_lowercase					:  List[Any]    			=      int(config["batch_size"]     )
  
  						_lowercase					:  int    			=      evaluate.load("glue"   , "mrpc"     )
  
  						# New Code #
  						# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
  						# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
  						# half each time. From this, we can calculate the number of gradient accumulation steps needed
  						# and modify the Accelerator object as a result
  						@find_executable_batch_size(starting_batch_size=int(_UpperCAmelCase     )     )
  						def inner_training_loop(_UpperCAmelCase     :      Any     ):
  												# Since we need to modify the outside accelerator object, we need to bring it
  												# to the local scope
  												nonlocal accelerator
  
  												# We can calculate the number of gradient accumulation steps based on the current
  												# batch size vs the starting batch size
  												_lowercase					:  Dict    			=      observed_batch_size // batch_size
  
  												# And then set it in the Accelerator directly:
  												_lowercase					:  Union[str, Any]    			=      num_gradient_accumulation_steps
  
  												# Next we need to free all of the stored model references in the Accelerator each time
  												accelerator.free_memory()
  
  												# And set the seed so our results are reproducable each reset
  												set_seed(_UpperCAmelCase     )
  
  												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  												_lowercase					:  Dict    			=      AutoModelForSequenceClassification.from_pretrained("bert-base-cased"   , return_dict=_UpperCAmelCase     )
  
  												# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  												# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  												# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  												_lowercase					:  Dict    			=      model.to(accelerator.device     )
  
  												# Instantiate optimizer
  												_lowercase					:  str    			=      AdamW(params=model.parameters()   , lr=_UpperCAmelCase     )
  												_lowercase					:  List[str]    			=      get_dataloaders(_UpperCAmelCase   , _UpperCAmelCase     )
  
  												# Instantiate scheduler
  												_lowercase					:  Optional[int]    			=      get_linear_schedule_with_warmup(
  												    optimizer=_UpperCAmelCase   , num_warmup_steps=100   , num_training_steps=(len(_UpperCAmelCase     ) * num_epochs)   , )
  
  												# Prepare everything
  												# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  												# prepare method.
  												_lowercase					:  Dict    			=      accelerator.prepare(
  												    _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase     )
  
  												# Now we train the model
  												for epoch in range(_UpperCAmelCase     ):
  																		model.train()
  																		for step, batch in enumerate(_UpperCAmelCase     ):
  																								# And perform gradient accumulation
  																								with accelerator.accumulate(_UpperCAmelCase     ):
  																														# We could avoid this line since we set the accelerator with `device_placement=True`.
  																														batch.to(accelerator.device     )
  																														_lowercase					:  List[Any]    			=      model(**_UpperCAmelCase     )
  																														_lowercase					:  Optional[int]    			=      outputs.loss
  																														accelerator.backward(_UpperCAmelCase     )
  																														optimizer.step()
  																														lr_scheduler.step()
  																														optimizer.zero_grad()
  
  																		model.eval()
  																		for step, batch in enumerate(_UpperCAmelCase     ):
  																								# We could avoid this line since we set the accelerator with `device_placement=True`.
  																								batch.to(accelerator.device     )
  																								with torch.no_grad():
  																														_lowercase					:  str    			=      model(**_UpperCAmelCase     )
  																								_lowercase					:  Optional[int]    			=      outputs.logits.argmax(dim=-1     )
  																								_lowercase					:  Optional[int]    			=      accelerator.gather_for_metrics((predictions, batch["labels"])     )
  																								metric.add_batch(
  																								    predictions=_UpperCAmelCase   , references=_UpperCAmelCase   , )
  
  																		_lowercase					:  List[str]    			=      metric.compute()
  																		# Use accelerator.print to print only on the main process.
  																		accelerator.print(f"""epoch {epoch}:"""   , _UpperCAmelCase     )
  
      # New Code #
      # And call it at the end with no arguments
      # Note: You could also refactor this outside of your training loop function
  						inner_training_loop()
  
  
  
  def 		UpperCamelCase    (					)      -> Any:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Union[str, Any]    			=      argparse.ArgumentParser(description="Simple example of training script."     )
  						parser.add_argument(
  						    "--mixed_precision"   , type=_UpperCAmelCase   , default=_UpperCAmelCase   , choices=["no", "fp16", "bf16", "fp8"]   , help="Whether to use mixed precision. Choose"
  						    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
  						    "and an Nvidia Ampere GPU."   , )
  						parser.add_argument("--cpu"   , action="store_true"   , help="If passed, will train on the CPU."     )
  						_lowercase					:  Union[str, Any]    			=      parser.parse_args()
  						# New Code #
  						# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
  						_lowercase					:  Union[str, Any]    			=      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
  						training_function(_UpperCAmelCase   , _UpperCAmelCase     )
  
  
  if __name__ == "__main__":
  							main()
  
  
  
  
  
style_context: 
  from dataclasses import dataclass
  
  UpperCamelCase_	:    List[str]  					=       """0.1"""
  UpperCamelCase_	:    Optional[Any]  					=       """Lucia Harcekova"""
  
  
  
  
  @dataclass
  class      __lowercase	:
  	_A   			=      42
  	_A   			=      42
  	_A   			=      42
  
  
  
  
  
  
  	def __repr__(self							:   List[str]						)   -> str:
  							return F"""({self.offset}, {self.length}, {self.indicator})"""
  
  
  
  
  class      __lowercase	:
  
  
  
  
  
  
  	def __init__(self							:   Union[str, Any]			,  snake_case							:   int = 13			,  snake_case							:   int = 6						)   -> None:
  							_lowercase					:  int    			=      window_size
  							_lowercase					:  List[Any]    			=      lookahead_buffer_size
  							_lowercase					:  str    			=      self.window_size - self.lookahead_buffer_size
  
  
  
  
  
  
  	def 			_a(self							:   List[str]			,  snake_case							:   str						)   -> list[Token]:
  							_lowercase					:  List[Any]    			=      []
  							_lowercase					:  str    			=      ""
  
  							# while there are still characters in text to compress
  							while text:
  													# find the next encoding phrase
  													# - triplet with offset, length, indicator (the next encoding character)
  													_lowercase					:  Union[str, Any]    			=      self._find_encoding_token(snake_case			,  snake_case						)
  
  													# update the search buffer:
  													# - add new characters from text into it
  													# - check if size exceed the max search buffer size, if so, drop the
  													#   oldest elements
  													search_buffer += text[: token.length + 1]
  													if len(snake_case						) > self.search_buffer_size:
  																			_lowercase					:  Optional[int]    			=      search_buffer[-self.search_buffer_size :]
  
  													# update the text
  													_lowercase					:  str    			=      text[token.length + 1 :]
  
  													# append the token to output
  													output.append(snake_case						)
  
  							return output
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]			,  snake_case							:   list[Token]						)   -> str:
  							_lowercase					:  Optional[int]    			=      ""
  
  							for token in tokens:
  													for _ in range(token.length						):
  																			output += output[-token.offset]
  													output += token.indicator
  
  							return output
  
  
  
  
  
  
  	def 			_a(self							:   Dict			,  snake_case							:   str			,  snake_case							:   str						)   -> Token:
  
  							if not text:
  													raise ValueError("We need some text to work with."						)
  
  							# Initialise result parameters to default values
  							_lowercase			,						_lowercase					:  Tuple    			=      0, 0
  
  							if not search_buffer:
  													return Token(snake_case			,  snake_case			,  text[length]						)
  
  							for i, character in enumerate(snake_case						):
  													_lowercase					:  Tuple    			=      len(snake_case						) - i
  													if character == text[0]:
  																			_lowercase					:  List[str]    			=      self._match_length_from_index(snake_case			,  snake_case			,  0			,  snake_case						)
  																			# if the found length is bigger than the current or if it's equal,
  																			# which means it's offset is smaller: update offset and length
  																			if found_length >= length:
  																									_lowercase			,						_lowercase					:  Optional[Any]    			=      found_offset, found_length
  
  							return Token(snake_case			,  snake_case			,  text[length]						)
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]			,  snake_case							:   str			,  snake_case							:   str			,  snake_case							:   int			,  snake_case							:   int						)   -> int:
  							if not text or text[text_index] != window[window_index]:
  													return 0
  							return 1 + self._match_length_from_index(
  							    snake_case			,  window + text[text_index]			,  text_index + 1			,  window_index + 1						)
  
  
  if __name__ == "__main__":
  							from doctest import testmod
  
  							testmod()
  							# Initialize compressor class
  							UpperCamelCase_	:    Optional[int]  					=       LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
  							# Example
  							UpperCamelCase_	:    Optional[int]  					=       """cabracadabrarrarrad"""
  							UpperCamelCase_	:    Tuple  					=       lzaa_compressor.compress(TEXT)
  							print(lzaa_compressor.compress("""ababcbababaa"""))
  							UpperCamelCase_	:    Union[str, Any]  					=       lzaa_compressor.decompress(compressed_text)
  							assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
  
  
  