






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      :      Optional[Any]     						=						16
__UpperCamelCase      :      Tuple     						=						32





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase = 16					):




	'''simple docstring'''







	__lowercase             =  AutoTokenizer.from_pretrained("""bert-base-cased"""					)
	__lowercase             =  load_dataset("""glue"""				,  """mrpc"""					)

	def tokenize_function(lowerCamelCase					):
		# max_length=None => use the model max length (it's actually the default)
		__lowercase             =  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
	# starting with the main process first:
	with accelerator.main_process_first():
		__lowercase             =  datasets.map(
		    lowerCamelCase				,  batched=lowerCamelCase				,  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
	__lowercase             =  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.
		__lowercase             =  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":
			__lowercase             =  16
		elif accelerator.mixed_precision != "no":
			__lowercase             =  8
		else:
			__lowercase             =  None

		return tokenizer.pad(
		    lowerCamelCase				,  padding="""longest"""				,  max_length=lowerCamelCase				,  pad_to_multiple_of=lowerCamelCase				,  return_tensors="""pt"""				,  )

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

	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      :      Optional[int]     						=						mocked_dataloaders  # noqa: F811





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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

	__lowercase       ,						__lowercase             =  get_dataloaders(lowerCamelCase				,  lowerCamelCase					)
	__lowercase             =  evaluate.load("""glue"""				,  """mrpc"""					)

	# If the batch size is too big we use gradient accumulation
	__lowercase             =  1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		__lowercase             =  batch_size // MAX_GPU_BATCH_SIZE
		__lowercase             =  MAX_GPU_BATCH_SIZE

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__lowercase             =  AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""				,  return_dict=lowerCamelCase					)

	# 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).
	__lowercase             =  model.to(accelerator.device					)

	# Instantiate optimizer
	__lowercase             =  AdamW(params=model.parameters()				,  lr=lowerCamelCase					)

	# Instantiate scheduler
	__lowercase             =  get_linear_schedule_with_warmup(
	    optimizer=lowerCamelCase				,  num_warmup_steps=100				,  num_training_steps=(len(lowerCamelCase					) * 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.
	__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase             =  accelerator.prepare(
	    lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)

	# New Code #
	# We need to initialize the trackers we use. Overall configurations can also be stored
	if args.with_tracking:
		__lowercase             =  os.path.split(lowerCamelCase					)[-1].split("""."""					)[0]
		accelerator.init_trackers(lowerCamelCase				,  lowerCamelCase					)

	# Now we train the model
	for epoch in range(lowerCamelCase					):
		model.train()
		# New Code #
		# For our tracking example, we will log the total loss of each epoch
		if args.with_tracking:
			__lowercase             =  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					)
			__lowercase             =  model(**lowerCamelCase					)
			__lowercase             =  outputs.loss
			# New Code #
			if args.with_tracking:
				total_loss += loss.detach().float()
			__lowercase             =  loss / gradient_accumulation_steps
			accelerator.backward(lowerCamelCase					)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		for step, batch in enumerate(lowerCamelCase					):
			# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
			batch.to(accelerator.device					)
			with torch.no_grad():
				__lowercase             =  model(**lowerCamelCase					)
			__lowercase             =  outputs.logits.argmax(dim=-1					)
			__lowercase       ,						__lowercase             =  accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
			metric.add_batch(
			    predictions=lowerCamelCase				,  references=lowerCamelCase				,  )

		__lowercase             =  metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'epoch {epoch}:'				,  lowerCamelCase					)

		# 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(lowerCamelCase					),
			        """epoch""": epoch,
			    }				,  step=lowerCamelCase				,  )

    # 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 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  argparse.ArgumentParser(description="""Simple example of training script."""					)
	parser.add_argument(
	    """--mixed_precision"""				,  type=lowerCamelCase				,  default=lowerCamelCase				,  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=lowerCamelCase				,  default="""logs"""				,  help="""Location on where to store experiment tracking logs` and relevent project information"""				,  )
	__lowercase             =  parser.parse_args()
	__lowercase             =  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(lowerCamelCase				,  lowerCamelCase					)


if __name__ == "__main__":
						main()









from math import sqrt





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	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(sqrt(lowerCamelCase					) + 1					)				,  6					):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def 				snake_case  (     lowerCamelCase = 10_001					):




	'''simple docstring'''







	__lowercase             =  0
	__lowercase             =  1
	while count != nth and number < 3:
		number += 1
		if is_prime(lowerCamelCase					):
			count += 1
	while count != nth:
		number += 2
		if is_prime(lowerCamelCase					):
			count += 1
	return number


if __name__ == "__main__":
						print(F'''{solution() = }''')

