






"""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


A:     List[str]										=	1_6
A:     Dict										=	3_2

def    _snake_case		(		UpperCamelCase		:	Accelerator    ,	UpperCamelCase		:	int = 16    ,	UpperCamelCase		:	str = "bert-base-cased"		):
 UpperCAmelCase			:      Dict			=			AutoTokenizer.from_pretrained(UpperCamelCase		)
 UpperCAmelCase			:      str			=			load_dataset("""glue"""    ,	"""mrpc"""		)

 def tokenize_function(UpperCamelCase		:	List[Any]		):
  # max_length=None => use the model max length (it's actually the default)
  UpperCAmelCase			:      Tuple			=			tokenizer(examples["""sentence1"""]    ,	examples["""sentence2"""]    ,	truncation=UpperCamelCase    ,	max_length=UpperCamelCase		)
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 UpperCAmelCase			:      Dict			=			datasets.map(
     UpperCamelCase    ,	batched=UpperCamelCase    ,	remove_columns=["""idx""", """sentence1""", """sentence2"""]    ,	load_from_cache_file=UpperCamelCase		)

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

 def collate_fn(UpperCamelCase		:	Union[str, Any]		):
  # 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(UpperCamelCase    ,	padding="""max_length"""    ,	max_length=128    ,	return_tensors="""pt"""		)
  return tokenizer.pad(UpperCamelCase    ,	padding="""longest"""    ,	return_tensors="""pt"""		)

 # Instantiate dataloaders.
 UpperCAmelCase			:      List[str]			=			DataLoader(
     tokenized_datasets["""train"""]    ,	shuffle=UpperCamelCase    ,	collate_fn=UpperCamelCase    ,	batch_size=UpperCamelCase		)
 UpperCAmelCase			:      List[Any]			=			DataLoader(
     tokenized_datasets["""validation"""]    ,	shuffle=UpperCamelCase    ,	collate_fn=UpperCamelCase    ,	batch_size=UpperCamelCase		)

 return train_dataloader, eval_dataloader

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	List[str]		):
 # Initialize accelerator
 UpperCAmelCase			:      Any			=			Accelerator()

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 UpperCAmelCase			:      Any			=			config["""lr"""]
 UpperCAmelCase			:      Tuple			=			int(config["""num_epochs"""]		)
 UpperCAmelCase			:      Optional[Any]			=			int(config["""seed"""]		)
 UpperCAmelCase			:      List[str]			=			int(config["""batch_size"""]		)
 UpperCAmelCase			:      List[Any]			=			args.model_name_or_path

 set_seed(UpperCamelCase		)
 UpperCAmelCase			:      Optional[int]			=			get_dataloaders(UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCAmelCase			:      Optional[int]			=			AutoModelForSequenceClassification.from_pretrained(UpperCamelCase    ,	return_dict=UpperCamelCase		)

 # Instantiate optimizer
 UpperCAmelCase			:      str			=			(
     AdamW
     if accelerator.state.deepspeed_plugin is None
     or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
     else DummyOptim
 )
 UpperCAmelCase			:      int			=			optimizer_cls(params=model.parameters()    ,	lr=UpperCamelCase		)

 if accelerator.state.deepspeed_plugin is not None:
  UpperCAmelCase			:      Optional[int]			=			accelerator.state.deepspeed_plugin.deepspeed_config[
      """gradient_accumulation_steps"""
  ]
 else:
  UpperCAmelCase			:      List[str]			=			1
 UpperCAmelCase			:      str			=			(len(UpperCamelCase		) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  UpperCAmelCase			:      str			=			get_linear_schedule_with_warmup(
      optimizer=UpperCamelCase    ,	num_warmup_steps=0    ,	num_training_steps=UpperCamelCase    ,	)
 else:
  UpperCAmelCase			:      Any			=			DummyScheduler(UpperCamelCase    ,	total_num_steps=UpperCamelCase    ,	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.
 UpperCAmelCase			:      Optional[int]			=			accelerator.prepare(
     UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)

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

 # Now we train the model
 UpperCAmelCase			:      Tuple			=			evaluate.load("""glue"""    ,	"""mrpc"""		)
 UpperCAmelCase			:      Any			=			0
 UpperCAmelCase			:      Optional[int]			=			{}
 for epoch in range(UpperCamelCase    ,	UpperCamelCase		):
  model.train()
  for step, batch in enumerate(UpperCamelCase		):
   UpperCAmelCase			:      Union[str, Any]			=			model(**UpperCamelCase		)
   UpperCAmelCase			:      Dict			=			outputs.loss
   UpperCAmelCase			:      Tuple			=			loss / gradient_accumulation_steps
   accelerator.backward(UpperCamelCase		)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1

  model.eval()
  UpperCAmelCase			:      Optional[int]			=			0
  for step, batch in enumerate(UpperCamelCase		):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device		)
   with torch.no_grad():
    UpperCAmelCase			:      Optional[int]			=			model(**UpperCamelCase		)
   UpperCAmelCase			:      Any			=			outputs.logits.argmax(dim=-1		)
   # It is slightly faster to call this once, than multiple times
   UpperCAmelCase			:      Optional[Any]			=			accelerator.gather(
       (predictions, batch["""labels"""])		)  # If we are in a multiprocess environment, the last batch has duplicates
   if accelerator.use_distributed:
    if step == len(UpperCamelCase		) - 1:
     UpperCAmelCase			:      Union[str, Any]			=			predictions[: len(eval_dataloader.dataset		) - samples_seen]
     UpperCAmelCase			:      Any			=			references[: len(eval_dataloader.dataset		) - samples_seen]
    else:
     samples_seen += references.shape[0]
   metric.add_batch(
       predictions=UpperCamelCase    ,	references=UpperCamelCase    ,	)

  UpperCAmelCase			:      Tuple			=			metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"epoch {epoch}:"    ,	UpperCamelCase		)
  UpperCAmelCase			:      List[str]			=			eval_metric["""accuracy"""]

  if best_performance < eval_metric["accuracy"]:
   UpperCAmelCase			:      Any			=			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(UpperCamelCase    ,	UpperCamelCase		)

def    _snake_case		(		):
 UpperCAmelCase			:      int			=			argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""		)
 parser.add_argument(
     """--model_name_or_path"""    ,	type=UpperCamelCase    ,	default="""bert-base-cased"""    ,	help="""Path to pretrained model or model identifier from huggingface.co/models."""    ,	required=UpperCamelCase    ,	)
 parser.add_argument(
     """--output_dir"""    ,	type=UpperCamelCase    ,	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=UpperCamelCase    ,	default=UpperCamelCase    ,	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=UpperCamelCase    ,	default=3    ,	help="""Number of train epochs."""    ,	)
 UpperCAmelCase			:      List[str]			=			parser.parse_args()
 UpperCAmelCase			:      Optional[Any]			=			{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
 training_function(UpperCamelCase    ,	UpperCamelCase		)


if __name__ == "__main__":
      main()





"""simple docstring"""
from __future__ import annotations

from collections.abc import Generator

def    _snake_case		(		):
 UpperCAmelCase			:      dict[int, int]			=			{}
 UpperCAmelCase			:      str			=			2
 while True:
  UpperCAmelCase			:      List[str]			=			factor_map.pop(UpperCamelCase    ,	UpperCamelCase		)
  if factor:
   UpperCAmelCase			:      Dict			=			factor + prime
   while x in factor_map:
    x += factor
   UpperCAmelCase			:      Tuple			=			factor
  else:
   UpperCAmelCase			:      Optional[int]			=			prime
   yield prime
  prime += 1

def    _snake_case		(		UpperCamelCase		:	float = 1e1_0		):
 UpperCAmelCase			:      Tuple			=			sieve()

 UpperCAmelCase			:      Optional[Any]			=			1
 while True:
  UpperCAmelCase			:      List[str]			=			next(UpperCamelCase		)
  if (2 * prime * n) > limit:
   return n
  # Ignore the next prime as the reminder will be 2.
  next(UpperCamelCase		)
  n += 2


if __name__ == "__main__":
      print(solution())
