






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
#
########################################################################


_lowerCAmelCase							:      List[Any]						=	16
_lowerCAmelCase							:      Union[str, Any]						=	32

def      __snake_case						(					_lowerCAmelCase : int	) ->						int:
   return int(x / 2**20	)


class 					__magic_name__  :





     """simple docstring"""



     def __enter__(							self       :Dict  ):





        '''simple docstring'''




        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        A_					:				List[Any]          =			torch.cuda.memory_allocated()
        A_					:				Optional[Any]          =			psutil.Process()

        A_					:				Union[str, Any]          =			self.cpu_mem_used()
        A_					:				str          =			True
        A_					:				Tuple          =			threading.Thread(target=self.peak_monitor_func  )
        A_					:				Tuple          =			True
        peak_monitor_thread.start()
        return self



     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        return self.process.memory_info().rss



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]  ):





        '''simple docstring'''




        A_					:				Any          =			-1

        while True:
           A_					:				Optional[Any]          =			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       :Any  ):





        '''simple docstring'''




        A_					:				List[str]          =			False

        gc.collect()
        torch.cuda.empty_cache()
        A_					:				int          =			torch.cuda.memory_allocated()
        A_					:				str          =			torch.cuda.max_memory_allocated()
        A_					:				Optional[int]          =			bamb(self.end - self.begin  )
        A_					:				int          =			bamb(self.peak - self.begin  )

        A_					:				Tuple          =			self.cpu_mem_used()
        A_					:				Any          =			bamb(self.cpu_end - self.cpu_begin  )
        A_					:				Dict          =			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

 _lowerCAmelCase							:      Dict						=	mocked_dataloaders  # noqa: F811

def      __snake_case						(					_lowerCAmelCase : List[Any]				,				_lowerCAmelCase : List[str]	) ->						Optional[Any]:
   # For testing only
   if os.environ.get("TESTING_MOCKED_DATALOADERS"				,				lowercase__	) == "1":
      A_					:				Dict          =			2

   # New Code #
   # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
   A_					:				Any          =			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_					:				Any          =			Accelerator(
          cpu=args.cpu				,				mixed_precision=args.mixed_precision				,				log_with="wandb"				,				project_dir=args.logging_dir				,				fsdp_plugin=lowercase__				,				)
   else:
      A_					:				Optional[int]          =			Accelerator(fsdp_plugin=lowercase__	)
   accelerator.print(accelerator.distributed_type	)

   if hasattr(args.checkpointing_steps				,				"isdigit"	):
      if args.checkpointing_steps == "epoch":
         A_					:				List[str]          =			args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         A_					:				List[str]          =			int(args.checkpointing_steps	)
      else:
         raise ValueError(
             f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."	)
   else:
      A_					:				Optional[Any]          =			None
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   A_					:				Optional[int]          =			config["lr"]
   A_					:				Any          =			int(config["num_epochs"]	)
   A_					:				Optional[int]          =			int(config["seed"]	)
   A_					:				Union[str, Any]          =			int(config["batch_size"]	)

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      A_					:				int          =			vars(lowercase__	)
      accelerator.init_trackers("fsdp_glue_no_trainer"				,				lowercase__	)

   A_					:				Any          =			AutoTokenizer.from_pretrained(args.model_name_or_path	)
   A_					:				Optional[int]          =			load_dataset("glue"				,				"mrpc"	)
   A_					:				Union[str, Any]          =			evaluate.load("glue"				,				"mrpc"	)

   def tokenize_function(_lowerCAmelCase : Any	):
      # max_length=None => use the model max length (it's actually the default)
      A_					:				Tuple          =			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_					:				Dict          =			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_					:				List[Any]          =			tokenized_datasets.rename_column("label"				,				"labels"	)

   # If the batch size is too big we use gradient accumulation
   A_					:				Dict          =			1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      A_					:				Dict          =			batch_size // MAX_GPU_BATCH_SIZE
      A_					:				Optional[int]          =			MAX_GPU_BATCH_SIZE

   def collate_fn(_lowerCAmelCase : Optional[int]	):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      A_					:				Optional[Any]          =			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_					:				Any          =			16
      elif accelerator.mixed_precision != "no":
         A_					:				Optional[int]          =			8
      else:
         A_					:				Union[str, Any]          =			None

      return tokenizer.pad(
          lowercase__				,				padding="longest"				,				max_length=lowercase__				,				pad_to_multiple_of=lowercase__				,				return_tensors="pt"				,				)

   # Instantiate dataloaders.
   A_					:				Dict          =			DataLoader(
       tokenized_datasets["train"]				,				shuffle=lowercase__				,				collate_fn=lowercase__				,				batch_size=lowercase__	)
   A_					:				str          =			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_					:				int          =			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_					:				Union[str, Any]          =			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_					:				List[str]          =			torch.optim.AdamW(params=model.parameters()				,				lr=lowercase__				,				weight_decay=2e-4	)

   # Instantiate scheduler
   A_					:				Optional[int]          =			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_					:				Dict          =			accelerator.prepare(
       lowercase__				,				lowercase__				,				lowercase__				,				lowercase__	)

   A_					:				Union[str, Any]          =			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_					:				Dict          =			os.path.basename(args.resume_from_checkpoint	)
      else:
         # Get the most recent checkpoint
         A_					:				int          =			[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
         dirs.sort(key=os.path.getctime	)
         A_					:				Union[str, Any]          =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      A_					:				List[str]          =			os.path.splitext(lowercase__	)[0]

      if "epoch" in training_difference:
         num_epochs -= int(training_difference.replace("epoch_"				,				""	)	)
         A_					:				int          =			None
      else:
         A_					:				Tuple          =			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_					:				Tuple          =			(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_					:				Optional[Any]          =			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_					:				str          =			model(**lowercase__	)
            A_					:				str          =			outputs.loss
            A_					:				List[Any]          =			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_					:				str          =			f"step_{overall_step}"
               if overall_step % checkpointing_steps == 0:
                  if args.output_dir is not None:
                     A_					:				Tuple          =			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_					:				Any          =			model(**lowercase__	)
            A_					:				Optional[int]          =			outputs.logits.argmax(dim=-1	)
            A_      ,						A_					:				Optional[Any]          =			accelerator.gather_for_metrics((predictions, batch["labels"])	)
            metric.add_batch(
                predictions=lowercase__				,				references=lowercase__				,				)

         A_					:				Optional[int]          =			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_					:				List[Any]          =			f"epoch_{epoch}"
            if args.output_dir is not None:
               A_					:				Union[str, Any]          =			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      __snake_case						(					) ->						Union[str, Any]:
   A_					:				str          =			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_					:				Union[str, Any]          =			parser.parse_args()
   A_					:				Any          =			{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(lowercase__				,				lowercase__	)


if __name__ == "__main__":
 main()







def      __snake_case						(					_lowerCAmelCase : float				,				_lowerCAmelCase : float				,				_lowerCAmelCase : int	) ->						float:
   A_					:				List[str]          =			[1 / first_term]
   A_					:				Tuple          =			1 / first_term
   for _ in range(number_of_terms - 1	):
      first_term += common_difference
      arithmetic_progression.append(_lowerCAmelCase	)
   A_					:				List[str]          =			[1 / step for step in arithmetic_progression]
   return sum(_lowerCAmelCase	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(sum_of_harmonic_progression(1 / 2, 2, 2))
