


'''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__:   Tuple    =	16
A__:   Dict    =	32


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Any  )  ->      Union[str, Any]:
       return int(x / 2**20  )







class A__	:






      def __enter__(			self      :Any     )	-> Optional[int]:




             '''simple docstring'''
             gc.collect()
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
             _a   :			int					 =torch.cuda.memory_allocated()
             _a   :			List[Any]					 =psutil.Process()

             _a   :			List[str]					 =self.cpu_mem_used()
             _a   :			str					 =True
             _a   :			List[Any]					 =threading.Thread(target=self.peak_monitor_func     )
             _a   :			Dict					 =True
             peak_monitor_thread.start()
             return self






      def __UpperCAmelCase	(			self      :int     )	-> int:




             '''simple docstring'''
             return self.process.memory_info().rss






      def __UpperCAmelCase	(			self      :Optional[int]     )	-> int:




             '''simple docstring'''
             _a   :			Dict					 =-1

             while True:
                    _a   :			List[str]					 =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      :Dict					,						*SCREAMING_SNAKE_CASE      :Optional[Any]     )	-> Union[str, Any]:




             '''simple docstring'''
             _a   :			List[str]					 =False

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

             _a   :			List[Any]					 =self.cpu_mem_used()
             _a   :			Union[str, Any]					 =bamb(self.cpu_end - self.cpu_begin     )
             _a   :			List[str]					 =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__:   Tuple    =	mocked_dataloaders  # noqa: F811


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Any   ,_UpperCAmelCase     :     int  )  ->      List[Any]:
       # For testing only
       if os.environ.get("""TESTING_MOCKED_DATALOADERS"""   ,_UpperCAmelCase  ) == "1":
              _a   :			Optional[int]					 =2

       # New Code #
       # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
       _a   :			Optional[int]					 =FullyShardedDataParallelPlugin(
           state_dict_config=FullStateDictConfig(offload_to_cpu=_UpperCAmelCase   ,ranka_only=_UpperCAmelCase  )   ,optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_UpperCAmelCase   ,ranka_only=_UpperCAmelCase  )   ,)

       # Initialize accelerator
       if args.with_tracking:
              _a   :			Tuple					 =Accelerator(
                  cpu=args.cpu   ,mixed_precision=args.mixed_precision   ,log_with="""wandb"""   ,project_dir=args.logging_dir   ,fsdp_plugin=_UpperCAmelCase   ,)
       else:
              _a   :			List[Any]					 =Accelerator(fsdp_plugin=_UpperCAmelCase  )
       accelerator.print(accelerator.distributed_type  )

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

       _a   :			List[Any]					 =AutoTokenizer.from_pretrained(args.model_name_or_path  )
       _a   :			Any					 =load_dataset("""glue"""   ,"""mrpc"""  )
       _a   :			Tuple					 =evaluate.load("""glue"""   ,"""mrpc"""  )

       def tokenize_function(_UpperCAmelCase     :     Dict  ):
              # max_length=None => use the model max length (it's actually the default)
              _a   :			str					 =tokenizer(examples["""sentence1"""]   ,examples["""sentence2"""]   ,truncation=_UpperCAmelCase   ,max_length=_UpperCAmelCase  )
              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   :			Tuple					 =datasets.map(
                  _UpperCAmelCase   ,batched=_UpperCAmelCase   ,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   :			Union[str, Any]					 =tokenized_datasets.rename_column("""label"""   ,"""labels"""  )

       # If the batch size is too big we use gradient accumulation
       _a   :			List[str]					 =1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              _a   :			Tuple					 =batch_size // MAX_GPU_BATCH_SIZE
              _a   :			Tuple					 =MAX_GPU_BATCH_SIZE

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

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

       # Instantiate dataloaders.
       _a   :			Optional[Any]					 =DataLoader(
           tokenized_datasets["""train"""]   ,shuffle=_UpperCAmelCase   ,collate_fn=_UpperCAmelCase   ,batch_size=_UpperCAmelCase  )
       _a   :			List[Any]					 =DataLoader(
           tokenized_datasets["""validation"""]   ,shuffle=_UpperCAmelCase   ,collate_fn=_UpperCAmelCase   ,batch_size=_UpperCAmelCase  )

       set_seed(_UpperCAmelCase  )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _a   :			List[Any]					 =AutoModelForSequenceClassification.from_pretrained(
           args.model_name_or_path   ,return_dict=_UpperCAmelCase   ,low_cpu_mem_usage=_UpperCAmelCase  )

       # New Code #
       # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
       _a   :			str					 =accelerator.prepare(_UpperCAmelCase  )
       accelerator.print(_UpperCAmelCase  )

       # 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   :			Union[str, Any]					 =torch.optim.AdamW(params=model.parameters()   ,lr=_UpperCAmelCase   ,weight_decay=2e-4  )

       # Instantiate scheduler
       _a   :			Union[str, Any]					 =get_linear_schedule_with_warmup(
           optimizer=_UpperCAmelCase   ,num_warmup_steps=10   ,num_training_steps=(len(_UpperCAmelCase  ) * 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(
           _UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase  )

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

              if "epoch" in training_difference:
                     num_epochs -= int(training_difference.replace("""epoch_"""   ,""""""  )  )
                     _a   :			Optional[Any]					 =None
              else:
                     _a   :			List[str]					 =int(training_difference.replace("""step_"""   ,""""""  )  )
                     num_epochs -= resume_step // len(_UpperCAmelCase  )
                     # If resuming by step, we also need to know exactly how far into the DataLoader we went
                     _a   :			Any					 =(num_epochs * len(_UpperCAmelCase  )) - resume_step

    # Now we train the model
       for epoch in range(_UpperCAmelCase  ):
              # 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   :			Dict					 =0
                     for step, batch in enumerate(_UpperCAmelCase  ):
                            # 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   :			Optional[int]					 =model(**_UpperCAmelCase  )
                            _a   :			Optional[Any]					 =outputs.loss
                            _a   :			Union[str, 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(_UpperCAmelCase  )
                            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(_UpperCAmelCase   ,_UpperCAmelCase  ):
                                   _a   :			Dict					 =F"step_{overall_step}"
                                   if overall_step % checkpointing_steps == 0:
                                          if args.output_dir is not None:
                                                 _a   :			str					 =os.path.join(args.output_dir   ,_UpperCAmelCase  )
                                          accelerator.save_state(_UpperCAmelCase  )
        # 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=_UpperCAmelCase   ,)

              # New Code #
              # context manager to track the peak memory usage during the evaluation
              with TorchTracemalloc() as tracemalloc:
                     model.eval()
                     for step, batch in enumerate(_UpperCAmelCase  ):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device  )
                            with torch.no_grad():
                                   _a   :			List[Any]					 =model(**_UpperCAmelCase  )
                            _a   :			Optional[Any]					 =outputs.logits.argmax(dim=-1  )
                            _a ,   _a   :			int					 =accelerator.gather_for_metrics((predictions, batch["""labels"""])  )
                            metric.add_batch(
                                predictions=_UpperCAmelCase   ,references=_UpperCAmelCase   ,)

                     _a   :			Optional[Any]					 =metric.compute()
                     # Use accelerator.print to print only on the main process.
                     accelerator.print(F"epoch {epoch}:"   ,_UpperCAmelCase  )
                     if args.with_tracking:
                            accelerator.log(
                                {
                                    """accuracy""": eval_metric["""accuracy"""],
                                    """f1""": eval_metric["""f1"""],
                                    """train_loss""": total_loss.item() / len(_UpperCAmelCase  ),
                                }   ,step=_UpperCAmelCase   ,)

                     if checkpointing_steps == "epoch":
                            _a   :			Tuple					 =F"epoch_{epoch}"
                            if args.output_dir is not None:
                                   _a   :			str					 =os.path.join(args.output_dir   ,_UpperCAmelCase  )
                            accelerator.save_state(_UpperCAmelCase  )
        # 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=_UpperCAmelCase   ,)

       if args.with_tracking:
              accelerator.end_training()


def      SCREAMING_SNAKE_CASE_ (  )  ->      Any:
       _a   :			Optional[Any]					 =argparse.ArgumentParser(description="""Simple example of training script."""  )
       parser.add_argument(
           """--mixed_precision"""   ,type=_UpperCAmelCase   ,default=_UpperCAmelCase   ,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=_UpperCAmelCase   ,default=_UpperCAmelCase   ,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=_UpperCAmelCase   ,default=_UpperCAmelCase   ,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=_UpperCAmelCase   ,default="""."""   ,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""   ,)
       parser.add_argument(
           """--logging_dir"""   ,type=_UpperCAmelCase   ,default="""logs"""   ,help="""Location on where to store experiment tracking logs`"""   ,)
       parser.add_argument(
           """--model_name_or_path"""   ,type=_UpperCAmelCase   ,help="""Path to pretrained model or model identifier from huggingface.co/models."""   ,required=_UpperCAmelCase   ,)
       _a   :			Optional[int]					 =parser.parse_args()
       _a   :			Any					 ={"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(_UpperCAmelCase   ,_UpperCAmelCase  )


if __name__ == "__main__":
   main()






'''simple docstring'''







from sklearn.metrics import mean_absolute_error

import datasets


A__:   Dict    =	'''\
@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}
}
'''

A__:   Union[str, Any]    =	'''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


A__:   str    =	'''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 "raw_values" : Returns a full set of errors in case of multioutput input.

                 "uniform_average" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''







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






      def __UpperCAmelCase	(			self      :List[Any]     )	-> Dict:




             '''simple docstring'''
             return datasets.MetricInfo(
                 description=_DESCRIPTION					,						citation=_CITATION					,						inputs_description=_KWARGS_DESCRIPTION					,						features=datasets.Features(self._get_feature_types()     )					,						reference_urls=[
                     """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"""
                 ]					,						)






      def __UpperCAmelCase	(			self      :Dict     )	-> List[str]:




             '''simple docstring'''
             if self.config_name == "multilist":
                    return {
                        "predictions": datasets.Sequence(datasets.Value("""float"""     )     ),
                        "references": datasets.Sequence(datasets.Value("""float"""     )     ),
                    }
             else:
                    return {
                        "predictions": datasets.Value("""float"""     ),
                        "references": datasets.Value("""float"""     ),
                    }






      def __UpperCAmelCase	(			self      :Any					,						SCREAMING_SNAKE_CASE      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=None					,						SCREAMING_SNAKE_CASE      :Any="uniform_average"     )	-> List[Any]:




             '''simple docstring'''
             _a   :			Tuple					 =mean_absolute_error(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						sample_weight=SCREAMING_SNAKE_CASE					,						multioutput=SCREAMING_SNAKE_CASE     )

             return {"mae": mae_score}


