





"""simple docstring"""
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
#
# 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)
#
# 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_          =	1_6
lowerCamelCase_          =	3_2



def   __lowerCamelCase     (							a_  :	Union[str, Any]				,     a_  :	Union[str, Any]							)    ->   List[str]:
      # Initialize accelerator
      if args.with_tracking:
            __SCREAMING_SNAKE_CASE     :Any	       =						Accelerator(
                cpu=args.cpu				,     mixed_precision=args.mixed_precision				,     log_with='''all'''				,     project_dir=args.project_dir							)
      else:
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision							)

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

      # We need to initialize the trackers we use, and also store our configuration
      if args.with_tracking:
            __SCREAMING_SNAKE_CASE     :Any	       =						os.path.split(a_							)[-1].split('''.'''							)[0]
            accelerator.init_trackers(a_				,     a_							)

      __SCREAMING_SNAKE_CASE     :int	       =						AutoTokenizer.from_pretrained('''bert-base-cased'''							)
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						load_dataset('''glue'''				,     '''mrpc'''							)
      __SCREAMING_SNAKE_CASE     :Tuple	       =						evaluate.load('''glue'''				,     '''mrpc'''							)

      def tokenize_function(a_  :	str							):
            # max_length=None => use the model max length (it's actually the default)
            __SCREAMING_SNAKE_CASE     :str	       =						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():
            __SCREAMING_SNAKE_CASE     :int	       =						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
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						tokenized_datasets.rename_column('''label'''				,     '''labels'''							)

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

      def collate_fn(a_  :	Any							):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						1_28 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  __SCREAMING_SNAKE_CASE     :List[str]	       =						16
            elif accelerator.mixed_precision != "no":
                  __SCREAMING_SNAKE_CASE     :int	       =						8
            else:
                  __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						None

            return tokenizer.pad(
                a_				,     padding='''longest'''				,     max_length=a_				,     pad_to_multiple_of=a_				,     return_tensors='''pt'''				,     )

      # Instantiate dataloaders.
      __SCREAMING_SNAKE_CASE     :int	       =						DataLoader(
          tokenized_datasets['''train''']				,     shuffle=a_				,     collate_fn=a_				,     batch_size=a_							)
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						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)
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,     return_dict=a_							)

      # 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).
      __SCREAMING_SNAKE_CASE     :int	       =						model.to(accelerator.device							)

      # Instantiate optimizer
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						AdamW(params=model.parameters()				,     lr=a_							)

      # Instantiate scheduler
      __SCREAMING_SNAKE_CASE     :int	       =						get_linear_schedule_with_warmup(
          optimizer=a_				,     num_warmup_steps=1_00				,     num_training_steps=(len(a_							) * 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.
      __SCREAMING_SNAKE_CASE     :int	       =						accelerator.prepare(
          a_				,     a_				,     a_				,     a_				,     a_							)

      # We need to keep track of how many total steps we have iterated over
      __SCREAMING_SNAKE_CASE     :int	       =						0
      # We also need to keep track of the stating epoch so files are named properly
      __SCREAMING_SNAKE_CASE     :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							)
                  __SCREAMING_SNAKE_CASE     :Optional[int]	       =						os.path.basename(args.resume_from_checkpoint							)
            else:
                  # Get the most recent checkpoint
                  __SCREAMING_SNAKE_CASE     :Dict	       =						[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
                  dirs.sort(key=os.path.getctime							)
                  __SCREAMING_SNAKE_CASE     :Dict	       =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            __SCREAMING_SNAKE_CASE     :Any	       =						os.path.splitext(a_							)[0]

            if "epoch" in training_difference:
                  __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						int(training_difference.replace('''epoch_'''				,     ''''''							)							) + 1
                  __SCREAMING_SNAKE_CASE     :Dict	       =						None
            else:
                  __SCREAMING_SNAKE_CASE     :int	       =						int(training_difference.replace('''step_'''				,     ''''''							)							)
                  __SCREAMING_SNAKE_CASE     :str	       =						resume_step // len(a_							)
                  resume_step -= starting_epoch * len(a_							)

    # Now we train the model
      for epoch in range(a_				,     a_							):
            model.train()
            if args.with_tracking:
                  __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						0
            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
                  __SCREAMING_SNAKE_CASE     :List[str]	       =						accelerator.skip_first_batches(a_				,     a_							)
                  overall_step += resume_step
            else:
                  # After the first iteration though, we need to go back to the original dataloader
                  __SCREAMING_SNAKE_CASE     :Any	       =						train_dataloader
            for step, batch in enumerate(a_							):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device							)
                  __SCREAMING_SNAKE_CASE     :List[Any]	       =						model(**a_							)
                  __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						outputs.loss
                  __SCREAMING_SNAKE_CASE     :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(a_							)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1

                  if isinstance(a_				,     a_							):
                        __SCREAMING_SNAKE_CASE     :Optional[int]	       =						f'''step_{overall_step}'''
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    __SCREAMING_SNAKE_CASE     :int	       =						os.path.join(args.output_dir				,     a_							)
                              accelerator.save_state(a_							)

            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():
                        __SCREAMING_SNAKE_CASE     :Any	       =						model(**a_							)
                  __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						outputs.logits.argmax(dim=-1							)
                  __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
                  metric.add_batch(
                      predictions=a_				,     references=a_				,     )

            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						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_							),
                          '''epoch''': epoch,
                      }				,     step=a_				,     )

            if checkpointing_steps == "epoch":
                  __SCREAMING_SNAKE_CASE     :Dict	       =						f'''epoch_{epoch}'''
                  if args.output_dir is not None:
                        __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						os.path.join(args.output_dir				,     a_							)
                  accelerator.save_state(a_							)

      if args.with_tracking:
            accelerator.end_training()



def   __lowerCamelCase     (							)    ->   Optional[int]:
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						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(
          '''--project_dir'''				,     type=a_				,     default='''logs'''				,     help='''Location on where to store experiment tracking logs` and relevent project information'''				,     )
      __SCREAMING_SNAKE_CASE     :str	       =						parser.parse_args()
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(a_				,     a_							)


if __name__ == "__main__":
      main()






"""simple docstring"""
def   __lowerCamelCase     (							a_  :	str							)    ->   int:
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						0
      for plain_chr in plain_text:
            __SCREAMING_SNAKE_CASE     :List[str]	       =						(
                ord(a_							) + (hash_value << 6) + (hash_value << 16) - hash_value
            )
      return hash_value