
'''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
#
########################################################################


A							     = 1_6
A							     = 3_2
def 	snake_case_							(				_a  :				Optional[int]      ):
      return int(x / 2**20      )



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __enter__(self	:			List[Any]     ) ->							str:
        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        snake_case_    							=				torch.cuda.memory_allocated()
        snake_case_    							=				psutil.Process()

        snake_case_    							=				self.cpu_mem_used()
        snake_case_    							=				True
        snake_case_    							=				threading.Thread(target=self.peak_monitor_func     )
        snake_case_    							=				True
        peak_monitor_thread.start()
        return self

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Any:
        return self.process.memory_info().rss

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Any:
        snake_case_    							=				-1

        while True:
              snake_case_    							=				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	:			List[Any]						,			*lowerCAmelCase_	:			Union[str, Any]     ) ->							List[Any]:
        snake_case_    							=				False

        gc.collect()
        torch.cuda.empty_cache()
        snake_case_    							=				torch.cuda.memory_allocated()
        snake_case_    							=				torch.cuda.max_memory_allocated()
        snake_case_    							=				bamb(self.end - self.begin     )
        snake_case_    							=				bamb(self.peak - self.begin     )

        snake_case_    							=				self.cpu_mem_used()
        snake_case_    							=				bamb(self.cpu_end - self.cpu_begin     )
        snake_case_    							=				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

     A							     = mocked_dataloaders  # noqa: F811
def 	snake_case_							(				_a  :				Tuple			,       _a  :				List[str]      ):
      # For testing only
      if os.environ.get("TESTING_MOCKED_DATALOADERS"			,       _a      ) == "1":
            snake_case_    							=				2

      # New Code #
      # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
      snake_case_    							=				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:
            snake_case_    							=				Accelerator(
                cpu=args.cpu			,       mixed_precision=args.mixed_precision			,       log_with="wandb"			,       project_dir=args.logging_dir			,       fsdp_plugin=_a			,       )
      else:
            snake_case_    							=				Accelerator(fsdp_plugin=_a      )
      accelerator.print(accelerator.distributed_type      )

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

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

      snake_case_    							=				AutoTokenizer.from_pretrained(args.model_name_or_path      )
      snake_case_    							=				load_dataset("glue"			,       "mrpc"      )
      snake_case_    							=				evaluate.load("glue"			,       "mrpc"      )

      def tokenize_function(_a  :				str      ):
            # max_length=None => use the model max length (it's actually the default)
            snake_case_    							=				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():
            snake_case_    							=				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
      snake_case_    							=				tokenized_datasets.rename_column("label"			,       "labels"      )

      # If the batch size is too big we use gradient accumulation
      snake_case_    							=				1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            snake_case_    							=				batch_size // MAX_GPU_BATCH_SIZE
            snake_case_    							=				MAX_GPU_BATCH_SIZE

      def collate_fn(_a  :				List[str]      ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            snake_case_    							=				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":
                  snake_case_    							=				16
            elif accelerator.mixed_precision != "no":
                  snake_case_    							=				8
            else:
                  snake_case_    							=				None

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

      # Instantiate dataloaders.
      snake_case_    							=				DataLoader(
          tokenized_datasets["train"]			,       shuffle=_a			,       collate_fn=_a			,       batch_size=_a      )
      snake_case_    							=				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)
      snake_case_    							=				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
      snake_case_    							=				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
      snake_case_    							=				torch.optim.AdamW(params=model.parameters()			,       lr=_a			,       weight_decay=2E-4      )

      # Instantiate scheduler
      snake_case_    							=				get_linear_schedule_with_warmup(
          optimizer=_a			,       num_warmup_steps=10			,       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.
      snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    							=				accelerator.prepare(
          _a			,       _a			,       _a			,       _a      )

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

            if "epoch" in training_difference:
                  num_epochs -= int(training_difference.replace("epoch_"			,       ""      )      )
                  snake_case_    							=				None
            else:
                  snake_case_    							=				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
                  snake_case_    							=				(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:
                        snake_case_    							=				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      )
                        snake_case_    							=				model(**_a      )
                        snake_case_    							=				outputs.loss
                        snake_case_    							=				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      ):
                              snake_case_    							=				F'''step_{overall_step}'''
                              if overall_step % checkpointing_steps == 0:
                                    if args.output_dir is not None:
                                          snake_case_    							=				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():
                              snake_case_    							=				model(**_a      )
                        snake_case_    							=				outputs.logits.argmax(dim=-1      )
                        snake_case_							,						snake_case_    							=				accelerator.gather_for_metrics((predictions, batch["labels"])      )
                        metric.add_batch(
                            predictions=_a			,       references=_a			,       )

                  snake_case_    							=				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":
                        snake_case_    							=				F'''epoch_{epoch}'''
                        if args.output_dir is not None:
                              snake_case_    							=				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 	snake_case_							(				):
      snake_case_    							=				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			,       )
      snake_case_    							=				parser.parse_args()
      snake_case_    							=				{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
      training_function(_a			,       _a      )


if __name__ == "__main__":
     main()







"""simple docstring"""
import math






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Tuple						)    ->       Any:
 _UpperCamelCase      :	str					      =  0
 if n % 2 == 0:
  count += 1
  while n % 2 == 0:
   _UpperCamelCase      :	List[Any]					      =  int(n / 2						)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

 _UpperCamelCase      :	Tuple					      =  3

 while i <= int(math.sqrt(__lowerCAmelCase						)						):
  if n % i == 0:
   count += 1
   while n % i == 0:
    _UpperCamelCase      :	Any					      =  int(n / i						)
  _UpperCamelCase      :	Optional[Any]					      =  i + 2

 # this condition checks the prime
 # number n is greater than 2

 if n > 2:
  count += 1
 return count


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE						 =     5_1_2_4_2_1_8_3
    print(f'The number of distinct prime factors is/are {exact_prime_factor_count(n)}')
    print(f'The value of log(log(n)) is {math.log(math.log(n)):.4f}')



