



'''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


__lowerCAmelCase									=   16
__lowerCAmelCase									=   32



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_ = 16						,			lowerCAmelCase_ = "bert-base-cased"  )			->      str:
		_a     :      Optional[int]        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      int        =							load_dataset('glue'						,			'mrpc'  )

		def tokenize_function(lowerCAmelCase_  ):
				# max_length=None => use the model max length (it's actually the default)
				_a     :      str        =							tokenizer(examples['sentence1']						,			examples['sentence2']						,			truncation=lowerCAmelCase_						,			max_length=lowerCAmelCase_  )
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		_a     :      str        =							datasets.map(
		    lowerCAmelCase_						,			batched=lowerCAmelCase_						,			remove_columns=['idx', 'sentence1', 'sentence2']						,			load_from_cache_file=lowerCAmelCase_  )

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

		def collate_fn(lowerCAmelCase_  ):
				# 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(lowerCAmelCase_						,			padding='max_length'						,			max_length=128						,			return_tensors='pt'  )
				return tokenizer.pad(lowerCAmelCase_						,			padding='longest'						,			return_tensors='pt'  )

		# Instantiate dataloaders.
		_a     :      Optional[int]        =							DataLoader(
		    tokenized_datasets['train']						,			shuffle=lowerCAmelCase_						,			collate_fn=lowerCAmelCase_						,			batch_size=lowerCAmelCase_  )
		_a     :      int        =							DataLoader(
		    tokenized_datasets['validation']						,			shuffle=lowerCAmelCase_						,			collate_fn=lowerCAmelCase_						,			batch_size=lowerCAmelCase_  )

		return train_dataloader, eval_dataloader



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

		_a     :      List[Any]        =							metric.compute()
		return eval_metric["accuracy"]



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Any:
		# Initialize accelerator
		_a     :      Optional[int]        =							Accelerator()

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

		set_seed(lowerCAmelCase_  )
		_a       ,					_a     :      Dict        =							get_dataloaders(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_a     :      Tuple        =							AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_						,			return_dict=lowerCAmelCase_  )

		# Instantiate optimizer
		_a     :      List[str]        =							(
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		_a     :      Tuple        =							optimizer_cls(params=model.parameters()						,			lr=lowerCAmelCase_  )

		if accelerator.state.deepspeed_plugin is not None:
				_a     :      int        =							accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				_a     :      Any        =							1
		_a     :      List[Any]        =							(len(lowerCAmelCase_  ) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				_a     :      Any        =							get_linear_schedule_with_warmup(
				    optimizer=lowerCAmelCase_						,			num_warmup_steps=0						,			num_training_steps=lowerCAmelCase_						,			)
		else:
				_a     :      int        =							DummyScheduler(lowerCAmelCase_						,			total_num_steps=lowerCAmelCase_						,			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.
		_a       ,					_a       ,					_a       ,					_a       ,					_a     :      Dict        =							accelerator.prepare(
		    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )

		# We need to keep track of how many total steps we have iterated over
		_a     :      str        =							0
		# We also need to keep track of the stating epoch so files are named properly
		_a     :      Optional[Any]        =							0
		_a     :      List[Any]        =							evaluate.load('glue'						,			'mrpc'  )
		_a     :      List[Any]        =							num_epochs

		if args.partial_train_epoch is not None:
				_a     :      Dict        =							args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint  )
				_a     :      Any        =							args.resume_from_checkpoint.split('epoch_'  )[1]
				_a     :      Optional[int]        =							''
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				_a     :      Dict        =							int(lowerCAmelCase_  ) + 1
				_a     :      Dict        =							evaluation_loop(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
				accelerator.print('resumed checkpoint performance:'						,			lowerCAmelCase_  )
				accelerator.print('resumed checkpoint\'s scheduler\'s lr:'						,			lr_scheduler.get_lr()[0]  )
				accelerator.print('resumed optimizers\'s lr:'						,			optimizer.param_groups[0]['lr']  )
				with open(os.path.join(args.output_dir						,			f"""state_{starting_epoch-1}.json"""  )						,			'r'  ) as f:
						_a     :      str        =							json.load(lowerCAmelCase_  )
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

    # Now we train the model
		_a     :      Any        =							{}
		for epoch in range(lowerCAmelCase_						,			lowerCAmelCase_  ):
				model.train()
				for step, batch in enumerate(lowerCAmelCase_  ):
						_a     :      Optional[int]        =							model(**lowerCAmelCase_  )
						_a     :      str        =							outputs.loss
						_a     :      str        =							loss / gradient_accumulation_steps
						accelerator.backward(lowerCAmelCase_  )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				_a     :      List[Any]        =							f"""epoch_{epoch}"""
				_a     :      Union[str, Any]        =							os.path.join(args.output_dir						,			lowerCAmelCase_  )
				accelerator.save_state(lowerCAmelCase_  )
				_a     :      List[str]        =							evaluation_loop(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
				_a     :      Optional[Any]        =							accuracy
				_a     :      List[Any]        =							lr_scheduler.get_lr()[0]
				_a     :      Any        =							optimizer.param_groups[0]['lr']
				_a     :      Dict        =							epoch
				_a     :      List[Any]        =							overall_step
				accelerator.print(f"""epoch {epoch}:"""						,			lowerCAmelCase_  )

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir						,			f"""state_{epoch}.json"""  )						,			'w'  ) as f:
								json.dump(lowerCAmelCase_						,			lowerCAmelCase_  )



def      __lowerCamelCase     (      )			->      List[Any]:
		_a     :      Union[str, Any]        =							argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'  )
		parser.add_argument(
		    '--model_name_or_path'						,			type=lowerCAmelCase_						,			default='bert-base-cased'						,			help='Path to pretrained model or model identifier from huggingface.co/models.'						,			required=lowerCAmelCase_						,			)
		parser.add_argument(
		    '--output_dir'						,			type=lowerCAmelCase_						,			default='.'						,			help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'						,			)
		parser.add_argument(
		    '--resume_from_checkpoint'						,			type=lowerCAmelCase_						,			default=lowerCAmelCase_						,			help='If the training should continue from a checkpoint folder.'						,			)
		parser.add_argument(
		    '--partial_train_epoch'						,			type=lowerCAmelCase_						,			default=lowerCAmelCase_						,			help='If passed, the training will stop after this number of epochs.'						,			)
		parser.add_argument(
		    '--num_epochs'						,			type=lowerCAmelCase_						,			default=2						,			help='Number of train epochs.'						,			)
		_a     :      Tuple        =							parser.parse_args()
		_a     :      Optional[Any]        =							{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

		training_function(lowerCAmelCase_						,			lowerCAmelCase_  )


if __name__ == "__main__":
							main()












"""simple docstring"""





from __future__ import annotations

import math







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				bool:

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5		,				int(math.sqrt(_lowerCamelCase ) + 1 )		,				6 ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				list[int]:
  lowerCamelCase_         =						str(_lowerCamelCase )
  lowerCamelCase_         =						[n]
  for i in range(1		,				len(_lowerCamelCase ) ):
    list_nums.append(int(str_num[i:] ) )
    list_nums.append(int(str_num[:-i] ) )
  return list_nums







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				bool:
  if len(str(_lowerCamelCase ) ) > 3:
    if not is_prime(int(str(_lowerCamelCase )[-3:] ) ) or not is_prime(int(str(_lowerCamelCase )[:3] ) ):
      return False
  return True







def      lowerCamelCase__       (							_lowerCamelCase       :		int = 11 )      ->				list[int]:
  lowerCamelCase_         =						[]
  lowerCamelCase_         =						13
  while len(_lowerCamelCase ) != count:
    if validate(_lowerCamelCase ):
      lowerCamelCase_         =						list_truncated_nums(_lowerCamelCase )
      if all(is_prime(_lowerCamelCase ) for i in list_nums ):
        list_truncated_primes.append(_lowerCamelCase )
    num += 2
  return list_truncated_primes







def      lowerCamelCase__       (							)      ->				int:
  return sum(compute_truncated_primes(11 ) )


if __name__ == "__main__":
  print(F'''{sum(compute_truncated_primes(11)) = }''')




