'''simple docstring'''




import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


__lowercase        =    1_6
__lowercase        =    3_2






def 							snake_case__				(     _A:   Accelerator ,  _A:   int = 16 ,  _A:   str = "bert-base-cased"					)       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    AutoTokenizer.from_pretrained(_A					)
		lowerCAmelCase     =    load_dataset("""glue""" ,  """mrpc"""					)

		def tokenize_function(_A:   Optional[int]					):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase     =    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
		lowerCAmelCase     =    datasets.map(
		    _A ,  batched=_A ,  remove_columns=["""idx""", """sentence1""", """sentence2"""] ,  load_from_cache_file=_A					)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		lowerCAmelCase     =    tokenized_datasets.rename_column("""label""" ,  """labels"""					)

		def collate_fn(_A:   Optional[int]					):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(_A ,  padding="""max_length""" ,  max_length=128 ,  return_tensors="""pt"""					)
				return tokenizer.pad(_A ,  padding="""longest""" ,  return_tensors="""pt"""					)

		# Instantiate dataloaders.
		lowerCAmelCase     =    DataLoader(
		    tokenized_datasets["""train"""] ,  shuffle=_A ,  collate_fn=_A ,  batch_size=_A					)
		lowerCAmelCase     =    DataLoader(
		    tokenized_datasets["""validation"""] ,  shuffle=_A ,  collate_fn=_A ,  batch_size=_A					)

		return train_dataloader, eval_dataloader






def 							snake_case__				(     _A:   Optional[int] ,  _A:   Optional[Any]					)       ->      Optional[int]:

		'''simple docstring'''


		lowerCAmelCase     =    Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCAmelCase     =    config["""lr"""]
		lowerCAmelCase     =    int(config["""num_epochs"""]					)
		lowerCAmelCase     =    int(config["""seed"""]					)
		lowerCAmelCase     =    int(config["""batch_size"""]					)
		lowerCAmelCase     =    args.model_name_or_path

		set_seed(_A					)
		lowerCAmelCase   ,	lowerCAmelCase     =    get_dataloaders(_A ,  _A ,  _A					)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCAmelCase     =    AutoModelForSequenceClassification.from_pretrained(_A ,  return_dict=_A					)

		# Instantiate optimizer
		lowerCAmelCase     =    (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		lowerCAmelCase     =    optimizer_cls(params=model.parameters() ,  lr=_A					)

		if accelerator.state.deepspeed_plugin is not None:
				lowerCAmelCase     =    accelerator.state.deepspeed_plugin.deepspeed_config[
				    """gradient_accumulation_steps"""
				]
		else:
				lowerCAmelCase     =    1
		lowerCAmelCase     =    (len(_A					) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				lowerCAmelCase     =    get_linear_schedule_with_warmup(
				    optimizer=_A ,  num_warmup_steps=0 ,  num_training_steps=_A ,  )
		else:
				lowerCAmelCase     =    DummyScheduler(_A ,  total_num_steps=_A ,  warmup_num_steps=0					)

		# 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     =    accelerator.prepare(
		    _A ,  _A ,  _A ,  _A ,  _A					)

		# We need to keep track of how many total steps we have iterated over
		lowerCAmelCase     =    0
		# We also need to keep track of the stating epoch so files are named properly
		lowerCAmelCase     =    0

		# Now we train the model
		lowerCAmelCase     =    evaluate.load("""glue""" ,  """mrpc"""					)
		lowerCAmelCase     =    0
		lowerCAmelCase     =    {}
		for epoch in range(_A ,  _A					):
				model.train()
				for step, batch in enumerate(_A					):
						lowerCAmelCase     =    model(**_A					)
						lowerCAmelCase     =    outputs.loss
						lowerCAmelCase     =    loss / gradient_accumulation_steps
						accelerator.backward(_A					)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1

				model.eval()
				lowerCAmelCase     =    0
				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():
								lowerCAmelCase     =    model(**_A					)
						lowerCAmelCase     =    outputs.logits.argmax(dim=-1					)
						# It is slightly faster to call this once, than multiple times
						lowerCAmelCase   ,	lowerCAmelCase     =    accelerator.gather(
						    (predictions, batch["""labels"""])					)  # If we are in a multiprocess environment, the last batch has duplicates
						if accelerator.use_distributed:
								if step == len(_A					) - 1:
										lowerCAmelCase     =    predictions[: len(eval_dataloader.dataset					) - samples_seen]
										lowerCAmelCase     =    references[: len(eval_dataloader.dataset					) - samples_seen]
								else:
										samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=_A ,  references=_A ,  )

				lowerCAmelCase     =    metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f"epoch {epoch}:" ,  _A					)
				lowerCAmelCase     =    eval_metric["""accuracy"""]

				if best_performance < eval_metric["accuracy"]:
						lowerCAmelCase     =    eval_metric["""accuracy"""]

		if args.performance_lower_bound is not None:
				assert (
				    args.performance_lower_bound <= best_performance
				), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
				with open(os.path.join(args.output_dir ,  """all_results.json"""					) ,  """w"""					) as f:
						json.dump(_A ,  _A					)






def 							snake_case__				(     )       ->      Optional[int]:

		'''simple docstring'''


		lowerCAmelCase     =    argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""					)
		parser.add_argument(
		    """--model_name_or_path""" ,  type=_A ,  default="""bert-base-cased""" ,  help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,  required=_A ,  )
		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(
		    """--performance_lower_bound""" ,  type=_A ,  default=_A ,  help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.""" ,  )
		parser.add_argument(
		    """--num_epochs""" ,  type=_A ,  default=3 ,  help="""Number of train epochs.""" ,  )
		lowerCAmelCase     =    parser.parse_args()
		lowerCAmelCase     =    {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
		training_function(_A ,  _A					)


if __name__ == "__main__":
						main()





'''simple docstring'''




from __future__ import annotations

import math






def 							snake_case__				(     _A:   int					)       ->      list[int]:

		'''simple docstring'''


		if num <= 0:
				lowerCAmelCase     =    f"{num}: Invalid input, please enter a positive integer."
				raise ValueError(_A					)

		lowerCAmelCase     =    [True] * (num + 1)
		lowerCAmelCase     =    []
		lowerCAmelCase     =    2
		lowerCAmelCase     =    int(math.sqrt(_A					)					)

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

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

				start += 1

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

		return prime


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




