


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
#
########################################################################


a_ :int  							=			16
a_ :Tuple  							=			32




def    a   (							A__      ,    A__ = 1_6							)	-> Any:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		AutoTokenizer.from_pretrained('''bert-base-cased'''							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		load_dataset('''glue'''      ,    '''mrpc'''							)

      def tokenize_function(A__							):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE__       :  int						     =		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__       :  Dict						     =		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__       :  Dict						     =		tokenized_datasets.rename_column('''label'''      ,    '''labels'''							)

      def collate_fn(A__							):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE__       :  Any						     =		1_2_8 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__       :  Dict						     =		1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE__       :  Tuple						     =		8
            else:
                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		None

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

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		DataLoader(
          tokenized_datasets['''train''']      ,    shuffle=A__      ,    collate_fn=A__      ,    batch_size=A__							)
      SCREAMING_SNAKE_CASE__       :  str						     =		DataLoader(
          tokenized_datasets['''validation''']      ,    shuffle=A__      ,    collate_fn=A__      ,    batch_size=A__							)

      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_ :Union[str, Any]  							=			mocked_dataloaders  # noqa: F811




def    a   (							A__      ,    A__							)	-> int:




      '''simple docstring'''

      if os.environ.get('''TESTING_MOCKED_DATALOADERS'''      ,    A__							) == "1":
            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		2
      # New Code #
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		int(args.gradient_accumulation_steps							)
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		int(args.local_sgd_steps							)
      # Initialize accelerator
      SCREAMING_SNAKE_CASE__       :  Any						     =		Accelerator(
          cpu=args.cpu      ,    mixed_precision=args.mixed_precision      ,    gradient_accumulation_steps=A__							)
      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
      SCREAMING_SNAKE_CASE__       :  Any						     =		config['''lr''']
      SCREAMING_SNAKE_CASE__       :  Any						     =		int(config['''num_epochs''']							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		int(config['''seed''']							)
      SCREAMING_SNAKE_CASE__       :  Dict						     =		int(config['''batch_size''']							)

      SCREAMING_SNAKE_CASE__       :  Tuple						     =		evaluate.load('''glue'''      ,    '''mrpc'''							)

      set_seed(A__							)
      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  int						     =		get_dataloaders(A__      ,    A__							)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE__       :  str						     =		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__       :  Optional[Any]						     =		model.to(accelerator.device							)

      # Instantiate optimizer
      SCREAMING_SNAKE_CASE__       :  Dict						     =		AdamW(params=model.parameters()      ,    lr=A__							)

      # Instantiate scheduler
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		get_linear_schedule_with_warmup(
          optimizer=A__      ,    num_warmup_steps=1_0_0      ,    num_training_steps=(len(A__							) * 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.
      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  str						     =		accelerator.prepare(
          A__      ,    A__      ,    A__      ,    A__      ,    A__							)

      # Now we train the model
      for epoch in range(A__							):
            model.train()
            with LocalSGD(
                accelerator=A__      ,    model=A__      ,    local_sgd_steps=A__      ,    enabled=local_sgd_steps is not None							) as local_sgd:
                  for step, batch in enumerate(A__							):
                        # 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(A__							):
                              SCREAMING_SNAKE_CASE__       :  Any						     =		model(**A__							)
                              SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		output.loss
                              accelerator.backward(A__							)
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()
                              # LocalSGD-specific line
                              local_sgd.step()

            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__       :  List[str]						     =		model(**A__							)
                  SCREAMING_SNAKE_CASE__       :  Tuple						     =		outputs.logits.argmax(dim=-1							)
                  SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
                  metric.add_batch(
                      predictions=A__      ,    references=A__      ,    )

            SCREAMING_SNAKE_CASE__       :  List[Any]						     =		metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"""epoch {epoch}:"""      ,    A__							)




def    a   (							)	-> int:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Optional[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.'''      ,    )
      # New Code #
      parser.add_argument(
          '''--gradient_accumulation_steps'''      ,    type=A__      ,    default=1      ,    help='''The number of minibatches to be ran before gradients are accumulated.'''      ,    )
      parser.add_argument(
          '''--local_sgd_steps'''      ,    type=A__      ,    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.'''							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		parser.parse_args()
      SCREAMING_SNAKE_CASE__       :  Any						     =		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
      training_function(A__      ,    A__							)


if __name__ == "__main__":
    main()











'''simple docstring'''

from __future__ import annotations







def a_     (    __UpperCAmelCase					)				->					list[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=[True] * limit
			snake_case:    Optional[int]      							=False
			snake_case:    Union[str, Any]      							=False
			snake_case:    List[Any]      							=True

			for i in range(3				,							int(limit**0.5 + 1					)				,							2					):
						snake_case:    str      							=i * 2
						while index < limit:
									snake_case:    List[Any]      							=False
									snake_case:    List[str]      							=index + i

			snake_case:    Union[str, Any]      							=[2]

			for i in range(3				,							__UpperCAmelCase				,							2					):
						if is_prime[i]:
									primes.append(__UpperCAmelCase					)

			return primes







def a_     (    __UpperCAmelCase = 1_00_00_00					)				->					int:





			"""simple docstring"""







			snake_case:    str      							=prime_sieve(__UpperCAmelCase					)
			snake_case:    str      							=0
			snake_case:    str      							=0

			for i in range(len(__UpperCAmelCase					)					):
						for j in range(i + length				,							len(__UpperCAmelCase					)					):
									snake_case:    Tuple      							=sum(primes[i:j]					)
									if sol >= ceiling:
												break

									if sol in primes:
												snake_case:    List[str]      							=j - i
												snake_case:    Optional[int]      							=sol

			return largest


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




