


"""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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


snake_case__		:     int    				=				16
snake_case__		:     Any    				=				32


def      _snake_case						(						_snake_case				:			Accelerator ,   _snake_case				:			int = 16			):
      lowerCAmelCase					:	Tuple       =   AutoTokenizer.from_pretrained('''bert-base-cased'''			)
      lowerCAmelCase					:	int       =   load_dataset('''glue''' ,   '''mrpc'''			)

      def tokenize_function(_snake_case				:			Union[str, Any]			):
            # max_length=None => use the model max length (it's actually the default)
            lowerCAmelCase					:	Optional[int]       =   tokenizer(examples['''sentence1'''] ,   examples['''sentence2'''] ,   truncation=_snake_case ,   max_length=_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():
            lowerCAmelCase					:	Union[str, Any]       =   datasets.map(
                _snake_case ,   batched=_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
      lowerCAmelCase					:	List[str]       =   tokenized_datasets.rename_column('''label''' ,   '''labels'''			)

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

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

      # Instantiate dataloaders.
      lowerCAmelCase					:	Optional[Any]       =   DataLoader(
          tokenized_datasets['''train'''] ,   shuffle=_snake_case ,   collate_fn=_snake_case ,   batch_size=_snake_case			)
      lowerCAmelCase					:	Optional[int]       =   DataLoader(
          tokenized_datasets['''validation'''] ,   shuffle=_snake_case ,   collate_fn=_snake_case ,   batch_size=_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

 snake_case__		:     str    				=				mocked_dataloaders  # noqa: F811


def      _snake_case						(						_snake_case				:			Any ,   _snake_case				:			Optional[int]			):
      # For testing only
      if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,   _snake_case			) == "1":
            lowerCAmelCase					:	List[str]       =   2
      # New Code #
      lowerCAmelCase					:	Optional[int]       =   int(args.gradient_accumulation_steps			)
      lowerCAmelCase					:	str       =   int(args.local_sgd_steps			)
      # Initialize accelerator
      lowerCAmelCase					:	List[Any]       =   Accelerator(
          cpu=args.cpu ,   mixed_precision=args.mixed_precision ,   gradient_accumulation_steps=_snake_case			)
      if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
            raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''			)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCAmelCase					:	Dict       =   config['''lr''']
      lowerCAmelCase					:	List[Any]       =   int(config['''num_epochs''']			)
      lowerCAmelCase					:	List[Any]       =   int(config['''seed''']			)
      lowerCAmelCase					:	int       =   int(config['''batch_size''']			)

      lowerCAmelCase					:	str       =   evaluate.load('''glue''' ,   '''mrpc'''			)

      set_seed(_snake_case			)
      lowerCAmelCase,      lowerCAmelCase					:	int       =   get_dataloaders(_snake_case ,   _snake_case			)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCAmelCase					:	Dict       =   AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,   return_dict=_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).
      lowerCAmelCase					:	Union[str, Any]       =   model.to(accelerator.device			)

      # Instantiate optimizer
      lowerCAmelCase					:	str       =   AdamW(params=model.parameters() ,   lr=_snake_case			)

      # Instantiate scheduler
      lowerCAmelCase					:	List[Any]       =   get_linear_schedule_with_warmup(
          optimizer=_snake_case ,   num_warmup_steps=100 ,   num_training_steps=(len(_snake_case			) * num_epochs) ,   )

      # 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.
      lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	int       =   accelerator.prepare(
          _snake_case ,   _snake_case ,   _snake_case ,   _snake_case ,   _snake_case			)

      # Now we train the model
      for epoch in range(_snake_case			):
            model.train()
            with LocalSGD(
                accelerator=_snake_case ,   model=_snake_case ,   local_sgd_steps=_snake_case ,   enabled=local_sgd_steps is not None			) as local_sgd:
                  for step, batch in enumerate(_snake_case			):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device			)
                        # New code #
                        # We use the new `accumulate` context manager to perform gradient accumulation
                        # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                        with accelerator.accumulate(_snake_case			):
                              lowerCAmelCase					:	Tuple       =   model(**_snake_case			)
                              lowerCAmelCase					:	int       =   output.loss
                              accelerator.backward(_snake_case			)
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()
                              # LocalSGD-specific line
                              local_sgd.step()

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

            lowerCAmelCase					:	int       =   metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'''epoch {epoch}:''' ,   _snake_case			)


def      _snake_case						(						):
      lowerCAmelCase					:	Dict       =   argparse.ArgumentParser(description='''Simple example of training script.'''			)
      parser.add_argument(
          '''--mixed_precision''' ,   type=_snake_case ,   default=_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.''' ,   )
      # New Code #
      parser.add_argument(
          '''--gradient_accumulation_steps''' ,   type=_snake_case ,   default=1 ,   help='''The number of minibatches to be ran before gradients are accumulated.''' ,   )
      parser.add_argument(
          '''--local_sgd_steps''' ,   type=_snake_case ,   default=8 ,   help='''Number of local SGD steps or None to disable local SGD'''			)
      parser.add_argument('''--cpu''' ,   action='''store_true''' ,   help='''If passed, will train on the CPU.'''			)
      lowerCAmelCase					:	int       =   parser.parse_args()
      lowerCAmelCase					:	Optional[Any]       =   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(_snake_case ,   _snake_case			)


if __name__ == "__main__":
 main()






"""simple docstring"""





def      _snake_case						(						_snake_case				:			int = 4000000			):
      lowerCAmelCase					:	int       =   [0, 1]
      lowerCAmelCase					:	List[str]       =   0
      while fib[i] <= n:
            fib.append(fib[i] + fib[i + 1]			)
            if fib[i + 2] > n:
                  break
            i += 1
      lowerCAmelCase					:	int       =   0
      for j in range(len(_snake_case			) - 1			):
            if fib[j] % 2 == 0:
                  total += fib[j]

      return total


if __name__ == "__main__":
 print(f"""{solution() = }""")


