




"""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  :			List[str]        =       16
_lowerCamelCase  :			Any        =       32

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase = 16  ,      _UpperCAmelCase = "bert-base-cased"	):




	"""simple docstring"""






	A_  :				Union[str, Any]	   =		AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE	)
	A_  :				Dict	   =		load_dataset('''glue'''  ,      '''mrpc'''	)

	def tokenize_function(_UpperCAmelCase	):
		# max_length=None => use the model max length (it's actually the default)
		A_  :				Tuple	   =		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
	A_  :				Tuple	   =		datasets.map(
	    _SCREAMING_SNAKE_CASE  ,      batched=_SCREAMING_SNAKE_CASE  ,      remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,      load_from_cache_file=_SCREAMING_SNAKE_CASE	)

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

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

	# Instantiate dataloaders.
	A_  :				int	   =		DataLoader(
	    tokenized_datasets['''train''']  ,      shuffle=_SCREAMING_SNAKE_CASE  ,      collate_fn=_SCREAMING_SNAKE_CASE  ,      batch_size=_SCREAMING_SNAKE_CASE	)
	A_  :				List[str]	   =		DataLoader(
	    tokenized_datasets['''validation''']  ,      shuffle=_SCREAMING_SNAKE_CASE  ,      collate_fn=_SCREAMING_SNAKE_CASE  ,      batch_size=_SCREAMING_SNAKE_CASE	)

	return train_dataloader, eval_dataloader

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	model.eval()
	A_  :				Union[str, 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	)
		with torch.no_grad():
			A_  :				Tuple	   =		model(**_SCREAMING_SNAKE_CASE	)
		A_  :				str	   =		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(_SCREAMING_SNAKE_CASE	) - 1:
				A_  :				str	   =		predictions[: len(eval_dataloader.dataset	) - samples_seen]
				A_  :				Any	   =		references[: len(eval_dataloader.dataset	) - samples_seen]
			else:
				samples_seen += references.shape[0]
		metric.add_batch(
		    predictions=_SCREAMING_SNAKE_CASE  ,      references=_SCREAMING_SNAKE_CASE  ,      )

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

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				List[str]	   =		Accelerator()

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

	set_seed(_SCREAMING_SNAKE_CASE	)
	A_   , A_  :				Optional[Any]	   =		get_dataloaders(_SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE	)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A_  :				Union[str, Any]	   =		AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE  ,      return_dict=_SCREAMING_SNAKE_CASE	)

	# Instantiate optimizer
	A_  :				Dict	   =		(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	A_  :				List[Any]	   =		optimizer_cls(params=model.parameters()  ,      lr=_SCREAMING_SNAKE_CASE	)

	if accelerator.state.deepspeed_plugin is not None:
		A_  :				str	   =		accelerator.state.deepspeed_plugin.deepspeed_config[
		    '''gradient_accumulation_steps'''
		]
	else:
		A_  :				Optional[int]	   =		1
	A_  :				List[str]	   =		(len(_SCREAMING_SNAKE_CASE	) * 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_  :				int	   =		get_linear_schedule_with_warmup(
		    optimizer=_SCREAMING_SNAKE_CASE  ,      num_warmup_steps=0  ,      num_training_steps=_SCREAMING_SNAKE_CASE  ,      )
	else:
		A_  :				List[str]	   =		DummyScheduler(_SCREAMING_SNAKE_CASE  ,      total_num_steps=_SCREAMING_SNAKE_CASE  ,      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_  :				Optional[Any]	   =		accelerator.prepare(
	    _SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE	)

	# We need to keep track of how many total steps we have iterated over
	A_  :				Dict	   =		0
	# We also need to keep track of the stating epoch so files are named properly
	A_  :				Optional[int]	   =		0
	A_  :				Union[str, Any]	   =		evaluate.load('''glue'''  ,      '''mrpc'''	)
	A_  :				List[str]	   =		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_  :				List[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_  :				int	   =		int(_SCREAMING_SNAKE_CASE	) + 1
		A_  :				Dict	   =		evaluation_loop(_SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE	)
		accelerator.print('''resumed checkpoint performance:'''  ,      _SCREAMING_SNAKE_CASE	)
		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_  :				Dict	   =		json.load(_SCREAMING_SNAKE_CASE	)
			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(_SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE	):
		model.train()
		for step, batch in enumerate(_SCREAMING_SNAKE_CASE	):
			A_  :				int	   =		model(**_SCREAMING_SNAKE_CASE	)
			A_  :				Optional[Any]	   =		outputs.loss
			A_  :				Optional[Any]	   =		loss / gradient_accumulation_steps
			accelerator.backward(_SCREAMING_SNAKE_CASE	)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		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(_SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE	)

def 		lowercase_      (		):




	"""simple docstring"""






	A_  :				List[Any]	   =		argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''	)
	parser.add_argument(
	    '''--model_name_or_path'''  ,      type=_SCREAMING_SNAKE_CASE  ,      default='''bert-base-cased'''  ,      help='''Path to pretrained model or model identifier from huggingface.co/models.'''  ,      required=_SCREAMING_SNAKE_CASE  ,      )
	parser.add_argument(
	    '''--output_dir'''  ,      type=_SCREAMING_SNAKE_CASE  ,      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=_SCREAMING_SNAKE_CASE  ,      default=_SCREAMING_SNAKE_CASE  ,      help='''If the training should continue from a checkpoint folder.'''  ,      )
	parser.add_argument(
	    '''--partial_train_epoch'''  ,      type=_SCREAMING_SNAKE_CASE  ,      default=_SCREAMING_SNAKE_CASE  ,      help='''If passed, the training will stop after this number of epochs.'''  ,      )
	parser.add_argument(
	    '''--num_epochs'''  ,      type=_SCREAMING_SNAKE_CASE  ,      default=2  ,      help='''Number of train epochs.'''  ,      )
	A_  :				Union[str, Any]	   =		parser.parse_args()
	A_  :				int	   =		{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

	training_function(_SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
		main()












"""simple docstring"""

import random

from .binary_exp_mod import bin_exp_mod

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=1000	):




	"""simple docstring"""






	if n < 2:
		return False

	if n % 2 == 0:
		return n == 2

	# this means n is odd
	A_  :				List[Any]	   =		n - 1
	A_  :				Dict	   =		0
	while d % 2 == 0:
		d /= 2
		exp += 1

	# n - 1=d*(2**exp)
	A_  :				Optional[Any]	   =		0
	while count < prec:
		A_  :				Optional[int]	   =		random.randint(2  ,      n - 1	)
		A_  :				Any	   =		bin_exp_mod(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
		if b != 1:
			A_  :				Dict	   =		True
			for _ in range(_UpperCAmelCase	):
				if b == n - 1:
					A_  :				Optional[int]	   =		False
					break
				A_  :				Any	   =		b * b
				b %= n
			if flag:
				return False
			count += 1
	return True


if __name__ == "__main__":
		_lowerCamelCase  :			List[str]        =       abs(int(input('Enter bound : ').strip()))
		print('Here\'s the list of primes:')
		print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))





