
'''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()








from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase__    :      Optional[Any]       					='''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


UpperCAmelCase__    :      Optional[int]       					='''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


UpperCAmelCase__    :      List[str]       					='''
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,       _KWARGS_DESCRIPTION     )
class    __A  (    datasets.Metric     ):


				def _snake_case							(		self				):
								return datasets.MetricInfo(
								    description=_DESCRIPTION ,			citation=_CITATION ,			inputs_description=_KWARGS_DESCRIPTION ,			features=datasets.Features(
								        {
								            """predictions""": datasets.Sequence(datasets.Value("""int32"""				)				),
								            """references""": datasets.Sequence(datasets.Value("""int32"""				)				),
								        }
								        if self.config_name == """multilabel"""
								        else {
								            """predictions""": datasets.Value("""int32"""				),
								            """references""": datasets.Value("""int32"""				),
								        }				) ,			reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"""] ,			)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None				):
								return {
								    "accuracy": float(
								        accuracy_score(UpperCAmelCase_ ,			UpperCAmelCase_ ,			normalize=UpperCAmelCase_ ,			sample_weight=UpperCAmelCase_				)				)
								}





