code: 
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import gc
  import os
  import threading
  
  import evaluate
  import psutil
  import torch
  from datasets import load_dataset
  from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
  from torch.utils.data import DataLoader
  from transformers import (
      AutoModelForSequenceClassification,
      AutoTokenizer,
      get_linear_schedule_with_warmup,
      set_seed,
  )
  
  from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin
  
  
  ########################################################################
  # 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)
  #   - FSDP
  #
  # 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
  #
  ########################################################################
  
  
  __SCREAMING_SNAKE_CASE					=						16
  __SCREAMING_SNAKE_CASE					=						32
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return int(x / 2**2_0					)
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __enter__(		self							:Dict       ):
            gc.collect()
            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
            _a          =				torch.cuda.memory_allocated()
            _a          =				psutil.Process()
  
            _a          =				self.cpu_mem_used()
            _a          =				True
            _a          =				threading.Thread(target=self.peak_monitor_func       )
            _a          =				True
            peak_monitor_thread.start()
            return self
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            return self.process.memory_info().rss
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
            _a          =				-1
  
            while True:
                   _a          =				max(self.cpu_mem_used()				,  self.cpu_peak       )
  
                   # can't sleep or will not catch the peak right (this comment is here on purpose)
                   # time.sleep(0.001) # 1msec
  
                   if not self.peak_monitoring:
                          break
  
  
  
  
     def __exit__(		self							:Optional[Any]				,  *UpperCamelCase__							:Optional[int]       ):
            _a          =				False
  
            gc.collect()
            torch.cuda.empty_cache()
            _a          =				torch.cuda.memory_allocated()
            _a          =				torch.cuda.max_memory_allocated()
            _a          =				bamb(self.end - self.begin       )
            _a          =				bamb(self.peak - self.begin       )
  
            _a          =				self.cpu_mem_used()
            _a          =				bamb(self.cpu_end - self.cpu_begin       )
            _a          =				bamb(self.cpu_peak - self.cpu_begin       )
            # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
  
  
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders
  
     __SCREAMING_SNAKE_CASE					=						mocked_dataloaders  # noqa: F811
  
  
  
  def 						__a      (  a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if os.environ.get("TESTING_MOCKED_DATALOADERS",		a					) == "1":
                _a          =				2
  
         # New Code #
         # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
         _a          =				FullyShardedDataParallelPlugin(
             state_dict_config=FullStateDictConfig(offload_to_cpu=a,		ranka_only=a					),		optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=a,		ranka_only=a					),		)
  
         # Initialize accelerator
         if args.with_tracking:
                _a          =				Accelerator(
                    cpu=args.cpu,		mixed_precision=args.mixed_precision,		log_with="wandb",		project_dir=args.logging_dir,		fsdp_plugin=a,		)
         else:
                _a          =				Accelerator(fsdp_plugin=a					)
         accelerator.print(accelerator.distributed_type					)
  
         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          =				vars(a					)
                accelerator.init_trackers("fsdp_glue_no_trainer",		a					)
  
         _a          =				AutoTokenizer.from_pretrained(args.model_name_or_path					)
         _a          =				load_dataset("glue",		"mrpc"					)
         _a          =				evaluate.load("glue",		"mrpc"					)
  
         def tokenize_function(a					):
                # max_length=None => use the model max length (it's actually the default)
                _a          =				tokenizer(examples["sentence1"],		examples["sentence2"],		truncation=a,		max_length=a					)
                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(
                    a,		batched=a,		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(a					):
                # On TPU it's best to pad everything to the same length or training will be very slow.
                _a          =				1_2_8 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          =				1_6
                elif accelerator.mixed_precision != "no":
                       _a          =				8
                else:
                       _a          =				None
  
                return tokenizer.pad(
                    a,		padding="longest",		max_length=a,		pad_to_multiple_of=a,		return_tensors="pt",		)
  
         # Instantiate dataloaders.
         _a          =				DataLoader(
             tokenized_datasets["train"],		shuffle=a,		collate_fn=a,		batch_size=a					)
         _a          =				DataLoader(
             tokenized_datasets["validation"],		shuffle=a,		collate_fn=a,		batch_size=a					)
  
         set_seed(a					)
  
         # Instantiate the model (we build the model here so that the seed also control new weights initialization)
         _a          =				AutoModelForSequenceClassification.from_pretrained(
             args.model_name_or_path,		return_dict=a,		low_cpu_mem_usage=a					)
  
         # New Code #
         # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
         _a          =				accelerator.prepare(a					)
         accelerator.print(a					)
  
         # Instantiate optimizer
         # New Code #
         # For FSDP feature, at present it doesn't support multiple parameter groups,
         # so we need to create a single parameter group for the whole model
         _a          =				torch.optim.AdamW(params=model.parameters(),		lr=a,		weight_decay=2e-4					)
  
         # Instantiate scheduler
         _a          =				get_linear_schedule_with_warmup(
             optimizer=a,		num_warmup_steps=1_0,		num_training_steps=(len(a					) * num_epochs) // gradient_accumulation_steps,		)
  
         # New Code #
         # For FSDP feature, prepare everything except the model as we have already prepared the model
         # before creating the optimizer
         # 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          =				accelerator.prepare(
             a,		a,		a,		a					)
  
         _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(a					)[0]
  
                if "epoch" in training_difference:
                       num_epochs -= int(training_difference.replace("epoch_",		""					)					)
                       _a          =				None
                else:
                       _a          =				int(training_difference.replace("step_",		""					)					)
                       num_epochs -= resume_step // len(a					)
                       # If resuming by step, we also need to know exactly how far into the DataLoader we went
                       _a          =				(num_epochs * len(a					)) - resume_step
  
      # Now we train the model
         for epoch in range(a					):
                # New Code #
                # context manager to track the peak memory usage during the training epoch
                with TorchTracemalloc() as tracemalloc:
                       model.train()
                       if args.with_tracking:
                              _a          =				0
                       for step, batch in enumerate(a					):
                              # We need to skip steps until we reach the resumed step
                              if args.resume_from_checkpoint and epoch == 0:
                                     if resume_step is not None and step < resume_step:
                                            pass
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                              batch.to(accelerator.device					)
                              _a          =				model(**a					)
                              _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(a					)
                              if step % gradient_accumulation_steps == 0:
                                     optimizer.step()
                                     lr_scheduler.step()
                                     optimizer.zero_grad()
                                     # accelerator.print(lr_scheduler.get_lr())
  
                              overall_step += 1
  
                              if isinstance(a,		a					):
                                     _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,		a					)
                                            accelerator.save_state(a					)
          # New Code #
          # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
                accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin					)					)					)
                accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used					)					)
                accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked					)					)
                accelerator.print(
                    "Total Peak Memory consumed during the train (max): {}".format(
                        tracemalloc.peaked + bamb(tracemalloc.begin					)					)					)
                # Logging the peak memory usage of the GPU to the tracker
                if args.with_tracking:
                       accelerator.log(
                           {
                               "train_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin					),
                           },		step=a,		)
  
                # New Code #
                # context manager to track the peak memory usage during the evaluation
                with TorchTracemalloc() as tracemalloc:
                       model.eval()
                       for step, batch in enumerate(a					):
                              # We could avoid this line since we set the accelerator with `device_placement=True`.
                              batch.to(accelerator.device					)
                              with torch.no_grad():
                                     _a          =				model(**a					)
                              _a          =				outputs.logits.argmax(dim=-1					)
                              _a   , _a          =				accelerator.gather_for_metrics((predictions, batch["labels"])					)
                              metric.add_batch(
                                  predictions=a,		references=a,		)
  
                       _a          =				metric.compute()
                       # Use accelerator.print to print only on the main process.
                       accelerator.print(F'epoch {epoch}:',		a					)
                       if args.with_tracking:
                              accelerator.log(
                                  {
                                      "accuracy": eval_metric["accuracy"],
                                      "f1": eval_metric["f1"],
                                      "train_loss": total_loss.item() / len(a					),
                                  },		step=a,		)
  
                       if checkpointing_steps == "epoch":
                              _a          =				F'epoch_{epoch}'
                              if args.output_dir is not None:
                                     _a          =				os.path.join(args.output_dir,		a					)
                              accelerator.save_state(a					)
          # New Code #
          # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
                accelerator.print("Memory before entering the eval : {}".format(bamb(tracemalloc.begin					)					)					)
                accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used					)					)
                accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked					)					)
                accelerator.print(
                    "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + bamb(tracemalloc.begin					)					)					)
                # Logging the peak memory usage of the GPU to the tracker
                if args.with_tracking:
                       accelerator.log(
                           {
                               "eval_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin					),
                           },		step=a,		)
  
         if args.with_tracking:
                accelerator.end_training()
  
  
  
  def 						__a      (  ):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				argparse.ArgumentParser(description="Simple example of training script."					)
         parser.add_argument(
             "--mixed_precision",		type=a,		default=a,		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=a,		default=a,		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=a,		default=a,		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=a,		default=".",		help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",		)
         parser.add_argument(
             "--logging_dir",		type=a,		default="logs",		help="Location on where to store experiment tracking logs`",		)
         parser.add_argument(
             "--model_name_or_path",		type=a,		help="Path to pretrained model or model identifier from huggingface.co/models.",		required=a,		)
         _a          =				parser.parse_args()
         _a          =				{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
         training_function(a,		a					)
  
  
  if __name__ == "__main__":
     main()
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  
  
  def 						__a      (  a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         assert len(a					) == len(a					)
  
         _a          =				len(a					)
         _a          =				list(range(a					)					)
         _a          =				[-1] * n  # who the donor has donated to
         _a          =				[-1] * n  # who the recipient has received from
         _a          =				[0] * n
  
         while unmatched_donors:
                _a          =				unmatched_donors[0]
                _a          =				donor_pref[donor]
                _a          =				donor_preference[num_donations[donor]]
                num_donations[donor] += 1
                _a          =				recipient_pref[recipient]
                _a          =				rec_record[recipient]
  
                if prev_donor != -1:
                       if rec_preference.index(a					) > rec_preference.index(a					):
                              _a          =				donor
                              _a          =				recipient
                              unmatched_donors.append(a					)
                              unmatched_donors.remove(a					)
                else:
                       _a          =				donor
                       _a          =				recipient
                       unmatched_donors.remove(a					)
         return donor_record
  