





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     :     Tuple							=	16
UpperCAmelCase     :     Optional[int]							=	32
def 					_A	(		SCREAMING_SNAKE_CASE					:       Accelerator	,	SCREAMING_SNAKE_CASE					:       int = 16						):





  """simple docstring"""



  a__     : Tuple  =AutoTokenizer.from_pretrained("bert-base-cased"						)
  a__     : int  =load_dataset("glue"	,	"mrpc"						)

  def tokenize_function(SCREAMING_SNAKE_CASE					:       Optional[int]						):
    # max_length=None => use the model max length (it's actually the default)
    a__     : Any  =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():
    a__     : Union[str, Any]  =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
  a__     : Any  =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.
    a__     : List[Any]  =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__     : Tuple  =16
    elif accelerator.mixed_precision != "no":
      a__     : str  =8
    else:
      a__     : List[Any]  =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.
  a__     : Tuple  =DataLoader(
      tokenized_datasets["train"]	,	shuffle=SCREAMING_SNAKE_CASE	,	collate_fn=SCREAMING_SNAKE_CASE	,	batch_size=SCREAMING_SNAKE_CASE						)
  a__     : Any  =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     :     str							=	mocked_dataloaders  # noqa: F811
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Optional[Any]						):





  """simple docstring"""



  if os.environ.get("TESTING_MOCKED_DATALOADERS"	,	SCREAMING_SNAKE_CASE						) == "1":
    a__     : Optional[Any]  =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:
    a__     : Optional[int]  =Accelerator(
        cpu=args.cpu	,	mixed_precision=args.mixed_precision	,	log_with="all"	,	project_dir=args.project_dir						)
  else:
    a__     : Union[str, Any]  =Accelerator(cpu=args.cpu	,	mixed_precision=args.mixed_precision						)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a__     : Optional[int]  =config["lr"]
  a__     : Tuple  =int(config["num_epochs"]						)
  a__     : Optional[Any]  =int(config["seed"]						)
  a__     : Optional[Any]  =int(config["batch_size"]						)
  set_seed(SCREAMING_SNAKE_CASE						)

  a__ ,							a__     : List[Any]  =get_dataloaders(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  a__     : Dict  =evaluate.load("glue"	,	"mrpc"						)

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

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a__     : List[str]  =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).
  a__     : List[Any]  =model.to(accelerator.device						)

  # Instantiate optimizer
  a__     : List[str]  =AdamW(params=model.parameters()	,	lr=SCREAMING_SNAKE_CASE						)

  # Instantiate scheduler
  a__     : Dict  =get_linear_schedule_with_warmup(
      optimizer=SCREAMING_SNAKE_CASE	,	num_warmup_steps=100	,	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.
  a__ ,							a__ ,							a__ ,							a__ ,							a__     : List[str]  =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:
    a__     : Any  =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:
      a__     : List[Any]  =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						)
      a__     : List[Any]  =model(**SCREAMING_SNAKE_CASE						)
      a__     : Tuple  =outputs.loss
      # New Code #
      if args.with_tracking:
        total_loss += loss.detach().float()
      a__     : Dict  =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():
        a__     : Optional[Any]  =model(**SCREAMING_SNAKE_CASE						)
      a__     : int  =outputs.logits.argmax(dim=-1						)
      a__ ,							a__     : int  =accelerator.gather_for_metrics((predictions, batch["labels"])						)
      metric.add_batch(
          predictions=SCREAMING_SNAKE_CASE	,	references=SCREAMING_SNAKE_CASE	,	)

    a__     : Optional[Any]  =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	(		):





  """simple docstring"""



  a__     : Dict  =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"	,	)
  a__     : List[str]  =parser.parse_args()
  a__     : Optional[Any]  ={"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     main()













from __future__ import annotations

import math
def 					_A	(		SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  if num <= 0:
    a__     : List[str]  =f'''{num}: Invalid input, please enter a positive integer.'''
    raise ValueError(SCREAMING_SNAKE_CASE						)

  a__     : Union[str, Any]  =[True] * (num + 1)
  a__     : Union[str, Any]  =[]
  a__     : str  =2
  a__     : Any  =int(math.sqrt(SCREAMING_SNAKE_CASE						)						)

  while start <= end:
    # If start is a prime
    if sieve[start] is True:
      prime.append(SCREAMING_SNAKE_CASE						)

      # Set multiples of start be False
      for i in range(start * start	,	num + 1	,	SCREAMING_SNAKE_CASE						):
        if sieve[i] is True:
          a__     : Optional[int]  =False

    start += 1

  for j in range(end + 1	,	num + 1						):
    if sieve[j] is True:
      prime.append(SCREAMING_SNAKE_CASE						)

  return prime


if __name__ == "__main__":
     print(prime_sieve(int(input("""Enter a positive integer: """).strip())))






