





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 experiment tracking 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
#
########################################################################

UpperCAmelCase_           =	16
UpperCAmelCase_           =	32
def      A__       (    SCREAMING_SNAKE_CASE_   :      Accelerator				,	SCREAMING_SNAKE_CASE_   :      int = 16						)  ->  Tuple:






   """simple docstring"""







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

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

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

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

   # Instantiate dataloaders.
   _UpperCAmelCase           =   DataLoader(
       tokenized_datasets['''train''']				,	shuffle=SCREAMING_SNAKE_CASE_				,	collate_fn=SCREAMING_SNAKE_CASE_				,	batch_size=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   DataLoader(
       tokenized_datasets['''validation''']				,	shuffle=SCREAMING_SNAKE_CASE_				,	collate_fn=SCREAMING_SNAKE_CASE_				,	batch_size=SCREAMING_SNAKE_CASE_						)

   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

   UpperCAmelCase_           =	mocked_dataloaders  # noqa: F811
def      A__       (    SCREAMING_SNAKE_CASE_   :      Optional[int]				,	SCREAMING_SNAKE_CASE_   :      Union[str, Any]						)  ->  Union[str, Any]:






   """simple docstring"""







   if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,	SCREAMING_SNAKE_CASE_						) == "1":
      _UpperCAmelCase           =   2
   # Initialize Accelerator

   # New Code #
   # We pass in "all" to `log_with` to grab all available trackers in the environment
   # Note: If using a custom `Tracker` class, should be passed in here such as:
   # >>> log_with = ["all", MyCustomTrackerClassInstance()]
   if args.with_tracking:
      _UpperCAmelCase           =   Accelerator(
          cpu=args.cpu				,	mixed_precision=args.mixed_precision				,	log_with='''all'''				,	project_dir=args.project_dir						)
   else:
      _UpperCAmelCase           =   Accelerator(cpu=args.cpu				,	mixed_precision=args.mixed_precision						)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _UpperCAmelCase           =   config['''lr''']
   _UpperCAmelCase           =   int(config['''num_epochs''']						)
   _UpperCAmelCase           =   int(config['''seed''']						)
   _UpperCAmelCase           =   int(config['''batch_size''']						)
   set_seed(SCREAMING_SNAKE_CASE_						)

   _UpperCAmelCase       ,      _UpperCAmelCase           =   get_dataloaders(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   evaluate.load('''glue'''				,	'''mrpc'''						)

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

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

   # 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).
   _UpperCAmelCase           =   model.to(accelerator.device						)

   # Instantiate optimizer
   _UpperCAmelCase           =   AdamW(params=model.parameters()				,	lr=SCREAMING_SNAKE_CASE_						)

   # Instantiate scheduler
   _UpperCAmelCase           =   get_linear_schedule_with_warmup(
       optimizer=SCREAMING_SNAKE_CASE_				,	num_warmup_steps=1_00				,	num_training_steps=(len(SCREAMING_SNAKE_CASE_						) * 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.
   _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase           =   accelerator.prepare(
       SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)

   # New Code #
   # We need to initialize the trackers we use. Overall configurations can also be stored
   if args.with_tracking:
      _UpperCAmelCase           =   os.path.split(SCREAMING_SNAKE_CASE_						)[-1].split('''.'''						)[0]
      accelerator.init_trackers(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)

   # Now we train the model
   for epoch in range(SCREAMING_SNAKE_CASE_						):
      model.train()
      # New Code #
      # For our tracking example, we will log the total loss of each epoch
      if args.with_tracking:
         _UpperCAmelCase           =   0
      for step, batch in enumerate(SCREAMING_SNAKE_CASE_						):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device						)
         _UpperCAmelCase           =   model(**SCREAMING_SNAKE_CASE_						)
         _UpperCAmelCase           =   outputs.loss
         # New Code #
         if args.with_tracking:
            total_loss += loss.detach().float()
         _UpperCAmelCase           =   loss / gradient_accumulation_steps
         accelerator.backward(SCREAMING_SNAKE_CASE_						)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(SCREAMING_SNAKE_CASE_						):
         # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
         batch.to(accelerator.device						)
         with torch.no_grad():
            _UpperCAmelCase           =   model(**SCREAMING_SNAKE_CASE_						)
         _UpperCAmelCase           =   outputs.logits.argmax(dim=-1						)
         _UpperCAmelCase       ,      _UpperCAmelCase           =   accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
         metric.add_batch(
             predictions=SCREAMING_SNAKE_CASE_				,	references=SCREAMING_SNAKE_CASE_				,	)

      _UpperCAmelCase           =   metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''				,	SCREAMING_SNAKE_CASE_						)

      # New Code #
      # To actually log, we call `Accelerator.log`
      # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
      if args.with_tracking:
         accelerator.log(
             {
                 '''accuracy''': eval_metric['''accuracy'''],
                 '''f1''': eval_metric['''f1'''],
                 '''train_loss''': total_loss.item() / len(SCREAMING_SNAKE_CASE_						),
                 '''epoch''': epoch,
             }				,	step=SCREAMING_SNAKE_CASE_				,	)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
   if args.with_tracking:
      accelerator.end_training()
def      A__       (    )  ->  List[Any]:






   """simple docstring"""







   _UpperCAmelCase           =   argparse.ArgumentParser(description='''Simple example of training script.'''						)
   parser.add_argument(
       '''--mixed_precision'''				,	type=SCREAMING_SNAKE_CASE_				,	default=SCREAMING_SNAKE_CASE_				,	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(
       '''--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(
       '''--project_dir'''				,	type=SCREAMING_SNAKE_CASE_				,	default='''logs'''				,	help='''Location on where to store experiment tracking logs` and relevent project information'''				,	)
   _UpperCAmelCase           =   parser.parse_args()
   _UpperCAmelCase           =   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
   training_function(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
   main()






def      A__       (    SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   if not isinstance(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						):
      _UpperCAmelCase           =   F'''Input value of [number={number}] must be an integer'''
      raise TypeError(SCREAMING_SNAKE_CASE_						)
   if number < 0:
      return False
   _UpperCAmelCase           =   number * number
   while number > 0:
      if number % 10 != number_square % 10:
         return False
      number //= 10
      number_square //= 10
   return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()