


import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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__			:    List[Any]			      =		16
a__			:    Union[str, Any]			      =		32

def     UpperCAmelCase_						(						_UpperCAmelCase       :Accelerator     ,	_UpperCAmelCase       :int = 16							)  ->							Any:
     '''simple docstring'''


     A_					      =					AutoTokenizer.from_pretrained('''bert-base-cased'''							)
     A_					      =					load_dataset('''glue'''     ,	'''mrpc'''							)

     def tokenize_function(_UpperCAmelCase       :List[Any]							):
          # 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'''							)

     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_					      =					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_					      =					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_							)

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      a__			:    Optional[Any]			      =		mocked_dataloaders  # noqa: F811

def     UpperCAmelCase_						(						_UpperCAmelCase       :str     ,	_UpperCAmelCase       :str							)  ->							int:
     '''simple docstring'''


     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,	lowercase_							) == "1":
          A_					      =					2
     # Initialize accelerator
     A_					      =					Accelerator(cpu=args.cpu     ,	mixed_precision=args.mixed_precision							)
     # 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''']							)

     # New Code #
     # Parse out whether we are saving every epoch or after a certain number of batches
     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

     set_seed(lowercase_							)

     A_					      =					get_dataloaders(lowercase_     ,	lowercase_							)
     A_					      =					evaluate.load('''glue'''     ,	'''mrpc'''							)

     # 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

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     A_					      =					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     ,	return_dict=lowercase_							)

     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     A_					      =					model.to(accelerator.device							)

     # Instantiate optimizer
     A_					      =					AdamW(params=model.parameters()     ,	lr=lowercase_							)

     # Instantiate scheduler
     A_					      =					get_linear_schedule_with_warmup(
         optimizer=lowercase_     ,	num_warmup_steps=100     ,	num_training_steps=(len(lowercase_							) * num_epochs) // gradient_accumulation_steps     ,	)

     # Prepare everything
     # 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_					      =					accelerator.prepare(
         lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_							)

     # New Code #
     # We need to keep track of how many total steps we have iterated over
     A_					      =					0
     # We also need to keep track of the stating epoch so files are named properly
     A_					      =					0

     # We need to load the checkpoint back in before training here with `load_state`
     # The total number of epochs is adjusted based on where the state is being loaded from,
     # as we assume continuation of the same training script
     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:
               A_					      =					int(training_difference.replace('''epoch_'''     ,	''''''							)							) + 1
               A_					      =					None
          else:
               A_					      =					int(training_difference.replace('''step_'''     ,	''''''							)							)
               A_					      =					resume_step // len(lowercase_							)
               resume_step -= starting_epoch * len(lowercase_							)

    # Now we train the model
     for epoch in range(lowercase_     ,	lowercase_							):
          model.train()
          # New Code #
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               A_					      =					accelerator.skip_first_batches(lowercase_     ,	lowercase_							)
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               A_					      =					train_dataloader
          for step, batch in enumerate(lowercase_							):
               # 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
               accelerator.backward(lowercase_							)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
               # New Code #
               overall_step += 1

               # New Code #
               # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
               # These are saved to folders named `step_{overall_step}`
               # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
               # If mixed precision was used, will also save a "scalar.bin" file
               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_							)

          model.eval()
          for step, batch in enumerate(lowercase_							):
               # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
               batch.to(accelerator.device							)
               with torch.no_grad():
                    A_					      =					model(**lowercase_							)
               A_					      =					outputs.logits.argmax(dim=-1							)
               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_							)

          # New Code #
          # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
          # These are saved to folders named `epoch_{epoch}`
          # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
          # If mixed precision was used, will also save a "scalar.bin" file
          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_							)

def     UpperCAmelCase_						(						)  ->							int:
     '''simple docstring'''


     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(
         '''--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(
         '''--resume_from_checkpoint'''     ,	type=lowercase_     ,	default=lowercase_     ,	help='''If the training should continue from a checkpoint folder.'''     ,	)
     A_					      =					parser.parse_args()
     A_					      =					{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(lowercase_     ,	lowercase_							)


if __name__ == "__main__":
      main()










from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments

def     UpperCAmelCase_						(						)  ->							Optional[int]:
     '''simple docstring'''


     A_					      =					HfArgumentParser(_UpperCAmelCase							)
     try:
          A_					      =					parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          A_					      =					'''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          A_					      =					''' '''.join(str(_UpperCAmelCase							).split(''' '''							)[:-1]							)
          A_					      =					''''''
          A_					      =					eval(str(_UpperCAmelCase							).split(''' '''							)[-1]							)
          A_					      =					[]
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]							)
               else:
                    wrong_args.append(_UpperCAmelCase							)
          if len(_UpperCAmelCase							) > 0:
               A_					      =					full_error_msg + begin_error_msg + str(_UpperCAmelCase							)
          raise ValueError(_UpperCAmelCase							)

     A_					      =					PyTorchBenchmark(args=_UpperCAmelCase							)
     benchmark.run()


if __name__ == "__main__":
      main()



