code: 
  
  """simple docstring"""
  
  
  
  
  import argparse
  import os
  
  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, DistributedType
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate
  #
  # 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)
  #
  # This example also demonstrates the checkpointing and sharding capabilities
  #
  # 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
  #
  ########################################################################
  
  
  __A  :					Tuple												=	16
  __A  :					List[str]												=	32
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					# Initialize accelerator
  					if args.with_tracking:
  										A    =      Accelerator(
  										    cpu=args.cpu    ,							mixed_precision=args.mixed_precision    ,							log_with="all"    ,							project_dir=args.project_dir   )
  					else:
  										A    =      Accelerator(cpu=args.cpu    ,							mixed_precision=args.mixed_precision   )
  
  					if hasattr(args.checkpointing_steps    ,							"isdigit"   ):
  										if args.checkpointing_steps == "epoch":
  															A    =      args.checkpointing_steps
  										elif args.checkpointing_steps.isdigit():
  															A    =      int(args.checkpointing_steps   )
  										else:
  															raise ValueError(
  															    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
  					else:
  										A    =      None
  					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  					A    =      config["lr"]
  					A    =      int(config["num_epochs"]   )
  					A    =      int(config["seed"]   )
  					A    =      int(config["batch_size"]   )
  
  					# We need to initialize the trackers we use, and also store our configuration
  					if args.with_tracking:
  										A    =      os.path.split(lowercase__   )[-1].split("."   )[0]
  										accelerator.init_trackers(lowercase__    ,							lowercase__   )
  
  					A    =      AutoTokenizer.from_pretrained("bert-base-cased"   )
  					A    =      load_dataset("glue"    ,							"mrpc"   )
  					A    =      evaluate.load("glue"    ,							"mrpc"   )
  
  					def tokenize_function(lowercase__   ):
  										# max_length=None => use the model max length (it's actually the default)
  										A    =      tokenizer(examples["sentence1"]    ,							examples["sentence2"]    ,							truncation=lowercase__    ,							max_length=lowercase__   )
  										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():
  										A    =      datasets.map(
  										    lowercase__    ,							batched=lowercase__    ,							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
  					A    =      tokenized_datasets.rename_column("label"    ,							"labels"   )
  
  					# If the batch size is too big we use gradient accumulation
  					A    =      1
  					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  										A    =      batch_size // MAX_GPU_BATCH_SIZE
  										A    =      MAX_GPU_BATCH_SIZE
  
  					def collate_fn(lowercase__   ):
  										# On TPU it's best to pad everything to the same length or training will be very slow.
  										A    =      128 if accelerator.distributed_type == DistributedType.TPU else None
  										# When using mixed precision we want round multiples of 8/16
  										if accelerator.mixed_precision == "fp8":
  															A    =      16
  										elif accelerator.mixed_precision != "no":
  															A    =      8
  										else:
  															A    =      None
  
  										return tokenizer.pad(
  										    lowercase__    ,							padding="longest"    ,							max_length=lowercase__    ,							pad_to_multiple_of=lowercase__    ,							return_tensors="pt"    ,							)
  
  					# Instantiate dataloaders.
  					A    =      DataLoader(
  					    tokenized_datasets["train"]    ,							shuffle=lowercase__    ,							collate_fn=lowercase__    ,							batch_size=lowercase__   )
  					A    =      DataLoader(
  					    tokenized_datasets["validation"]    ,							shuffle=lowercase__    ,							collate_fn=lowercase__    ,							batch_size=lowercase__   )
  
  					set_seed(lowercase__   )
  
  					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  					A    =      AutoModelForSequenceClassification.from_pretrained("bert-base-cased"    ,							return_dict=lowercase__   )
  
  					# 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).
  					A    =      model.to(accelerator.device   )
  
  					# Instantiate optimizer
  					A    =      AdamW(params=model.parameters()    ,							lr=lowercase__   )
  
  					# Instantiate scheduler
  					A    =      get_linear_schedule_with_warmup(
  					    optimizer=lowercase__    ,							num_warmup_steps=100    ,							num_training_steps=(len(lowercase__   ) * num_epochs) // gradient_accumulation_steps    ,							)
  
  					# 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.
  					A    ,					A    ,					A    ,					A    ,					A    =      accelerator.prepare(
  					    lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__   )
  
  					# We need to keep track of how many total steps we have iterated over
  					A    =      0
  					# We also need to keep track of the stating epoch so files are named properly
  					A    =      0
  
  					# Potentially load in the weights and states from a previous save
  					if args.resume_from_checkpoint:
  										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
  															accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""   )
  															accelerator.load_state(args.resume_from_checkpoint   )
  															A    =      os.path.basename(args.resume_from_checkpoint   )
  										else:
  															# Get the most recent checkpoint
  															A    =      [f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
  															dirs.sort(key=os.path.getctime   )
  															A    =      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  										# Extract `epoch_{i}` or `step_{i}`
  										A    =      os.path.splitext(lowercase__   )[0]
  
  										if "epoch" in training_difference:
  															A    =      int(training_difference.replace("epoch_"    ,							""   )   ) + 1
  															A    =      None
  										else:
  															A    =      int(training_difference.replace("step_"    ,							""   )   )
  															A    =      resume_step // len(lowercase__   )
  															resume_step -= starting_epoch * len(lowercase__   )
  
      # Now we train the model
  					for epoch in range(lowercase__    ,							lowercase__   ):
  										model.train()
  										if args.with_tracking:
  															A    =      0
  										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
  															# We need to skip steps until we reach the resumed step
  															A    =      accelerator.skip_first_batches(lowercase__    ,							lowercase__   )
  															overall_step += resume_step
  										else:
  															# After the first iteration though, we need to go back to the original dataloader
  															A    =      train_dataloader
  										for step, batch in enumerate(lowercase__   ):
  															# We could avoid this line since we set the accelerator with `device_placement=True`.
  															batch.to(accelerator.device   )
  															A    =      model(**lowercase__   )
  															A    =      outputs.loss
  															A    =      loss / gradient_accumulation_steps
  															# We keep track of the loss at each epoch
  															if args.with_tracking:
  																				total_loss += loss.detach().float()
  															accelerator.backward(lowercase__   )
  															if step % gradient_accumulation_steps == 0:
  																				optimizer.step()
  																				lr_scheduler.step()
  																				optimizer.zero_grad()
  
  															overall_step += 1
  
  															if isinstance(lowercase__    ,							lowercase__   ):
  																				A    =      F"""step_{overall_step}"""
  																				if overall_step % checkpointing_steps == 0:
  																									if args.output_dir is not None:
  																														A    =      os.path.join(args.output_dir    ,							lowercase__   )
  																									accelerator.save_state(lowercase__   )
  
  										model.eval()
  										for step, batch in enumerate(lowercase__   ):
  															# We could avoid this line since we set the accelerator with `device_placement=True`.
  															batch.to(accelerator.device   )
  															with torch.no_grad():
  																				A    =      model(**lowercase__   )
  															A    =      outputs.logits.argmax(dim=-1   )
  															A    ,					A    =      accelerator.gather_for_metrics((predictions, batch["labels"])   )
  															metric.add_batch(
  															    predictions=lowercase__    ,							references=lowercase__    ,							)
  
  										A    =      metric.compute()
  										# Use accelerator.print to print only on the main process.
  										accelerator.print(F"""epoch {epoch}:"""    ,							lowercase__   )
  										if args.with_tracking:
  															accelerator.log(
  															    {
  															        "accuracy": eval_metric["accuracy"],
  															        "f1": eval_metric["f1"],
  															        "train_loss": total_loss.item() / len(lowercase__   ),
  															        "epoch": epoch,
  															    }    ,							step=lowercase__    ,							)
  
  										if checkpointing_steps == "epoch":
  															A    =      F"""epoch_{epoch}"""
  															if args.output_dir is not None:
  																				A    =      os.path.join(args.output_dir    ,							lowercase__   )
  															accelerator.save_state(lowercase__   )
  
  					if args.with_tracking:
  										accelerator.end_training()
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					A    =      argparse.ArgumentParser(description="Simple example of training script."   )
  					parser.add_argument(
  					    "--mixed_precision"    ,							type=lowercase__    ,							default=lowercase__    ,							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."   )
  					parser.add_argument(
  					    "--checkpointing_steps"    ,							type=lowercase__    ,							default=lowercase__    ,							help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."    ,							)
  					parser.add_argument(
  					    "--resume_from_checkpoint"    ,							type=lowercase__    ,							default=lowercase__    ,							help="If the training should continue from a checkpoint folder."    ,							)
  					parser.add_argument(
  					    "--with_tracking"    ,							action="store_true"    ,							help="Whether to load in all available experiment trackers from the environment and use them for logging."    ,							)
  					parser.add_argument(
  					    "--output_dir"    ,							type=lowercase__    ,							default="."    ,							help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,							)
  					parser.add_argument(
  					    "--project_dir"    ,							type=lowercase__    ,							default="logs"    ,							help="Location on where to store experiment tracking logs` and relevent project information"    ,							)
  					A    =      parser.parse_args()
  					A    =      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  					training_function(lowercase__    ,							lowercase__   )
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if not isinstance(lowercase__    ,							lowercase__   ):
  										A    =      (
  										    "operation can not be conducted on a object of type "
  										    F"""{type(lowercase__   ).__name__}"""
  										)
  										raise TypeError(lowercase__   )
  					A    =      ""
  					for _ in range(0    ,							32   ):
  										bit_string += str(number % 2   )
  										A    =      number >> 1
  					return bit_string
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if number < 0:
  										raise ValueError("the value of input must be positive"   )
  					elif isinstance(lowercase__    ,							lowercase__   ):
  										raise TypeError("Input value must be a 'int' type"   )
  					elif isinstance(lowercase__    ,							lowercase__   ):
  										raise TypeError("'<' not supported between instances of 'str' and 'int'"   )
  					A    =      0
  					# iterator over [1 to 32],since we are dealing with 32 bit integer
  					for _ in range(1    ,							33   ):
  										# left shift the bits by unity
  										A    =      result << 1
  										# get the end bit
  										A    =      number % 2
  										# right shift the bits by unity
  										A    =      number >> 1
  										# add that bit to our ans
  										A    =      result | end_bit
  					return get_reverse_bit_string(lowercase__   )
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  