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 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[int]:
   return int(x / 2**20		)
  
  
  
  
  class 			UpperCAmelCase_						:
  
  
  
  
      def __enter__(					self		: Any   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       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 							__magic_name__							(					self		: Tuple   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       return self.process.memory_info().rss
  
  
  
  
      def 							__magic_name__							(					self		: int   )   ->							List[str]:
  
  
  
  
       '''simple docstring'''
  
       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		: Any			,				*snake_case_		: Dict   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       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 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	List[Any]:
   # For testing only
   if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,				lowercase_		) == "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=lowercase_    ,				ranka_only=lowercase_		)    ,				optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowercase_    ,				ranka_only=lowercase_		)    ,				)
  
   # 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=lowercase_    ,				)
   else:
    A__ =       Accelerator(fsdp_plugin=lowercase_		)
   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(lowercase_		)
    accelerator.init_trackers("fsdp_glue_no_trainer"    ,				lowercase_		)
  
   A__ =       AutoTokenizer.from_pretrained(args.model_name_or_path		)
   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__ =       1_28 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(
       args.model_name_or_path    ,				return_dict=lowercase_    ,				low_cpu_mem_usage=lowercase_		)
  
   # New Code #
   # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
   A__ =       accelerator.prepare(lowercase_		)
   accelerator.print(lowercase_		)
  
   # 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=lowercase_    ,				weight_decay=2E-4		)
  
   # Instantiate scheduler
   A__ =       get_linear_schedule_with_warmup(
       optimizer=lowercase_    ,				num_warmup_steps=10    ,				num_training_steps=(len(lowercase_		) * 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(
       lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)
  
   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:
     num_epochs -= int(training_difference.replace("epoch_"    ,				""		)		)
     A__ =       None
    else:
     A__ =       int(training_difference.replace("step_"    ,				""		)		)
     num_epochs -= resume_step // len(lowercase_		)
     # If resuming by step, we also need to know exactly how far into the DataLoader we went
     A__ =       (num_epochs * len(lowercase_		)) - resume_step
  
      # Now we train the model
   for epoch in range(lowercase_		):
    # 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(lowercase_		):
      # 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(**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()
       # accelerator.print(lr_scheduler.get_lr())
  
      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_		)
          # 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=lowercase_    ,				)
  
    # New Code #
    # context manager to track the peak memory usage during the evaluation
    with TorchTracemalloc() as tracemalloc:
     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_		),
          }    ,				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_		)
          # 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=lowercase_    ,				)
  
   if args.with_tracking:
    accelerator.end_training()
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							)		->	Dict:
   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(
       "--logging_dir"    ,				type=lowercase_    ,				default="logs"    ,				help="Location on where to store experiment tracking logs`"    ,				)
   parser.add_argument(
       "--model_name_or_path"    ,				type=lowercase_    ,				help="Path to pretrained model or model identifier from huggingface.co/models."    ,				required=lowercase_    ,				)
   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"""
  
  
  
  from manim import *
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
  
  
  
  
      def 							__magic_name__							(					self		: Any   )   ->							Optional[int]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       Rectangle(height=0.5			,				width=0.5   )
       A__ =       Rectangle(height=0.46			,				width=0.46   ).set_stroke(width=0   )
       A__ =       Rectangle(height=0.25			,				width=0.25   )
  
       A__ =       [mem.copy() for i in range(6   )]
       A__ =       [mem.copy() for i in range(6   )]
       A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
       A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
       A__ =       VGroup(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0   )
       A__ =       Text("CPU"			,				font_size=24   )
       A__ =       Group(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0.5			,				aligned_edge=snake_case_   )
       cpu.move_to([-2.5, -0.5, 0]   )
       self.add(snake_case_   )
  
       A__ =       [mem.copy() for i in range(4   )]
       A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
       A__ =       Text("GPU"			,				font_size=24   )
       A__ =       Group(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0.5			,				aligned_edge=snake_case_   )
       gpu.move_to([-1, -1, 0]   )
       self.add(snake_case_   )
  
       A__ =       [mem.copy() for i in range(6   )]
       A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
  
       A__ =       Text("Model"			,				font_size=24   )
       A__ =       Group(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0.5			,				aligned_edge=snake_case_   )
       model.move_to([3, -1.0, 0]   )
       self.add(snake_case_   )
  
       A__ =       []
       A__ =       []
  
       for i, rect in enumerate(snake_case_   ):
        rect.set_stroke(snake_case_   )
  
        A__ =       Rectangle(height=0.46 / 4			,				width=0.46 / 3   ).set_stroke(width=0.0   ).set_fill(snake_case_			,				opacity=0.7   )
  
        if i == 0:
         cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT   )			,				buff=0.02			,				direction=snake_case_   )
         cpu_target.set_x(cpu_target.get_x() + 0.1   )
        elif i == 3:
         cpu_target.next_to(model_cpu_arr[0]			,				direction=snake_case_			,				buff=0.0   )
        else:
         cpu_target.next_to(model_cpu_arr[i - 1]			,				direction=snake_case_			,				buff=0.0   )
        self.add(snake_case_   )
        model_cpu_arr.append(snake_case_   )
  
       self.add(*snake_case_			,				*snake_case_   )
  
       A__ =       [meta_mem.copy() for i in range(6   )]
       A__ =       [meta_mem.copy() for i in range(6   )]
       A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
       A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
       A__ =       VGroup(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0   )
       A__ =       Text("Disk"			,				font_size=24   )
       A__ =       Group(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0.5			,				aligned_edge=snake_case_   )
       disk.move_to([-4.0, -1.25, 0]   )
       self.add(snake_case_			,				snake_case_   )
  
       A__ =       []
  
       for i in range(6   ):
        A__ =       fill.copy().set_fill(snake_case_			,				opacity=0.8   )
        target.move_to(disk_left_col_base[i]   ).scale(0.5   )
        cpu_disk_arr.append(snake_case_   )
  
       self.add(*snake_case_   )
  
       A__ =       Square(side_length=2.2   )
       key.move_to([-5, 2, 0]   )
  
       A__ =       MarkupText(
           F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""			,				font_size=18			,				)
  
       key_text.move_to([-5, 2.4, 0]   )
  
       self.add(snake_case_			,				snake_case_   )
  
       A__ =       MarkupText(
           F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""			,				font_size=18			,				)
  
       blue_text.next_to(snake_case_			,				DOWN * 2.4			,				aligned_edge=key_text.get_left()   )
       self.add(snake_case_   )
  
       A__ =       MarkupText(F"""The offloaded weights are all sent to the CPU."""			,				font_size=24   )
       step_a.move_to([2, 2, 0]   )
  
       self.play(Write(snake_case_			,				run_time=3   )   )
  
       for i in range(6   ):
        A__ =       cpu_disk_arr[i]
        A__ =       rect.copy().set_fill(snake_case_			,				opacity=0.8   ).scale(2.0   )
        cpa.generate_target()
        cpa.target.move_to(model_base[i]   )
  
        if i == 0:
         rect.set_fill(snake_case_			,				opacity=0.8   )
         rect.generate_target()
         rect.target.move_to(cpu_left_col_base[0]   ).scale(2.0   )
  
         self.remove(
             *snake_case_			,				*snake_case_			,				)
  
        else:
         rect.generate_target()
         rect.target.move_to(cpu_left_col_base[i]   ).scale(2.0   )
        self.play(MoveToTarget(snake_case_   )			,				MoveToTarget(snake_case_   )			,				model_base[i].animate.set_stroke(snake_case_   )   )
       self.play(FadeOut(snake_case_   )   )
  
       A__ =       MarkupText(
           F"""Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed."""			,				font_size=24			,				)
       step_a.move_to([2, 2, 0]   )
  
       self.play(Write(snake_case_			,				run_time=3   )   )
  
       A__ =       []
       A__ =       []
       for i in range(6   ):
        A__ =       Arrow(start=snake_case_			,				end=snake_case_			,				color=snake_case_			,				buff=0.5   )
        a.next_to(model_base[i].get_left()			,				snake_case_			,				buff=0.2   )
        arrows.append(snake_case_   )
        animations.append(Write(snake_case_   )   )
       self.play(*snake_case_   )
       self.wait()
  
  
  
  
  
  