'''simple docstring'''




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 checkpointing 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
#
########################################################################

_lowercase		:					Optional[int]													=		16
_lowercase		:					Optional[int]													=		32




def 				lowerCamelCase      ( UpperCAmelCase__			:   Dict , UpperCAmelCase__			:   Dict = 16					)     -> Tuple:
							lowercase_   :     Optional[Any]									=			AutoTokenizer.from_pretrained("""bert-base-cased"""					)
							lowercase_   :     Tuple									=			load_dataset("""glue""" , """mrpc"""					)

							def tokenize_function(UpperCAmelCase__			:   Optional[int]					):
														# max_length=None => use the model max length (it's actually the default)
														lowercase_   :     Union[str, Any]									=			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
							# starting with the main process first:
							with accelerator.main_process_first():
														lowercase_   :     Any									=			datasets.map(
														    UpperCAmelCase__ , batched=UpperCAmelCase__ , 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_   :     Optional[int]									=			tokenized_datasets.rename_column("""label""" , """labels"""					)

							def collate_fn(UpperCAmelCase__			:   List[Any]					):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														lowercase_   :     str									=			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_   :     Optional[Any]									=			16
														elif accelerator.mixed_precision != "no":
																					lowercase_   :     Any									=			8
														else:
																					lowercase_   :     Any									=			None

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

							# Instantiate dataloaders.
							lowercase_   :     int									=			DataLoader(
							    tokenized_datasets["""train"""] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__					)
							lowercase_   :     Union[str, Any]									=			DataLoader(
							    tokenized_datasets["""validation"""] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__					)

							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

						_lowercase		:					Any													=		mocked_dataloaders  # noqa: F811




def 				lowerCamelCase      ( UpperCAmelCase__			:   Tuple , UpperCAmelCase__			:   Optional[Any]					)     -> Union[str, Any]:





							if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , UpperCAmelCase__					) == "1":
														lowercase_   :     Union[str, Any]									=			2
							# Initialize accelerator
							lowercase_   :     Optional[Any]									=			Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision					)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							lowercase_   :     Tuple									=			config["""lr"""]
							lowercase_   :     Optional[int]									=			int(config["""num_epochs"""]					)
							lowercase_   :     Optional[int]									=			int(config["""seed"""]					)
							lowercase_   :     Union[str, Any]									=			int(config["""batch_size"""]					)

							# New Code #
							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps , """isdigit"""					):
														if args.checkpointing_steps == "epoch":
																					lowercase_   :     Optional[Any]									=			args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					lowercase_   :     str									=			int(args.checkpointing_steps					)
														else:
																					raise ValueError(
																					    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''					)
							else:
														lowercase_   :     List[str]									=			None

							set_seed(UpperCAmelCase__					)

							lowercase_		,		lowercase_   :     Tuple									=			get_dataloaders(UpperCAmelCase__ , UpperCAmelCase__					)
							lowercase_   :     int									=			evaluate.load("""glue""" , """mrpc"""					)

							# If the batch size is too big we use gradient accumulation
							lowercase_   :     int									=			1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														lowercase_   :     List[str]									=			batch_size // MAX_GPU_BATCH_SIZE
														lowercase_   :     List[Any]									=			MAX_GPU_BATCH_SIZE

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

							# 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_   :     Optional[Any]									=			model.to(accelerator.device					)

							# Instantiate optimizer
							lowercase_   :     List[str]									=			AdamW(params=model.parameters() , lr=UpperCAmelCase__					)

							# Instantiate scheduler
							lowercase_   :     Any									=			get_linear_schedule_with_warmup(
							    optimizer=UpperCAmelCase__ , num_warmup_steps=100 , num_training_steps=(len(UpperCAmelCase__					) * 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_   :     Any									=			accelerator.prepare(
							    UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__					)

							# New Code #
							# We need to keep track of how many total steps we have iterated over
							lowercase_   :     Union[str, Any]									=			0
							# We also need to keep track of the stating epoch so files are named properly
							lowercase_   :     Any									=			0

							# We need to load the checkpoint back in before training here with `load_state`
							# The total number of epochs is adjusted based on where the state is being loaded from,
							# as we assume continuation of the same training script
							if args.resume_from_checkpoint:
														if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																					accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''					)
																					accelerator.load_state(args.resume_from_checkpoint					)
																					lowercase_   :     Optional[int]									=			os.path.basename(args.resume_from_checkpoint					)
														else:
																					# Get the most recent checkpoint
																					lowercase_   :     Any									=			[f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
																					dirs.sort(key=os.path.getctime					)
																					lowercase_   :     Any									=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														lowercase_   :     int									=			os.path.splitext(UpperCAmelCase__					)[0]

														if "epoch" in training_difference:
																					lowercase_   :     Tuple									=			int(training_difference.replace("""epoch_""" , """"""					)					) + 1
																					lowercase_   :     List[Any]									=			None
														else:
																					lowercase_   :     Tuple									=			int(training_difference.replace("""step_""" , """"""					)					)
																					lowercase_   :     Optional[Any]									=			resume_step // len(UpperCAmelCase__					)
																					resume_step -= starting_epoch * len(UpperCAmelCase__					)

    # Now we train the model
							for epoch in range(UpperCAmelCase__ , UpperCAmelCase__					):
														model.train()
														# New Code #
														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																					# We need to skip steps until we reach the resumed step
																					lowercase_   :     Union[str, Any]									=			accelerator.skip_first_batches(UpperCAmelCase__ , UpperCAmelCase__					)
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					lowercase_   :     Optional[Any]									=			train_dataloader
														for step, batch in enumerate(UpperCAmelCase__					):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device					)
																					lowercase_   :     Optional[int]									=			model(**UpperCAmelCase__					)
																					lowercase_   :     List[Any]									=			outputs.loss
																					lowercase_   :     List[str]									=			loss / gradient_accumulation_steps
																					accelerator.backward(UpperCAmelCase__					)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()
																					# New Code #
																					overall_step += 1

																					# New Code #
																					# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																					# These are saved to folders named `step_{overall_step}`
																					# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																					# If mixed precision was used, will also save a "scalar.bin" file
																					if isinstance(UpperCAmelCase__ , UpperCAmelCase__					):
																												lowercase_   :     Any									=			F'''step_{overall_step}'''
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										lowercase_   :     Dict									=			os.path.join(args.output_dir , UpperCAmelCase__					)
																																			accelerator.save_state(UpperCAmelCase__					)

														model.eval()
														for step, batch in enumerate(UpperCAmelCase__					):
																					# 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_   :     Optional[Any]									=			model(**UpperCAmelCase__					)
																					lowercase_   :     Union[str, Any]									=			outputs.logits.argmax(dim=-1					)
																					lowercase_		,		lowercase_   :     Optional[Any]									=			accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
																					metric.add_batch(
																					    predictions=UpperCAmelCase__ , references=UpperCAmelCase__ , )

														lowercase_   :     Optional[int]									=			metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}:''' , UpperCAmelCase__					)

														# New Code #
														# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
														# These are saved to folders named `epoch_{epoch}`
														# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
														# If mixed precision was used, will also save a "scalar.bin" file
														if checkpointing_steps == "epoch":
																					lowercase_   :     Optional[Any]									=			F'''epoch_{epoch}'''
																					if args.output_dir is not None:
																												lowercase_   :     Any									=			os.path.join(args.output_dir , UpperCAmelCase__					)
																					accelerator.save_state(UpperCAmelCase__					)




def 				lowerCamelCase      ( )     -> Dict:
							lowercase_   :     List[str]									=			argparse.ArgumentParser(description="""Simple example of training script."""					)
							parser.add_argument(
							    """--mixed_precision""" , type=UpperCAmelCase__ , default=UpperCAmelCase__ , 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(
							    """--checkpointing_steps""" , type=UpperCAmelCase__ , default=UpperCAmelCase__ , help="""Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.""" , )
							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(
							    """--resume_from_checkpoint""" , type=UpperCAmelCase__ , default=UpperCAmelCase__ , help="""If the training should continue from a checkpoint folder.""" , )
							lowercase_   :     str									=			parser.parse_args()
							lowercase_   :     List[Any]									=			{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(UpperCAmelCase__ , UpperCAmelCase__					)


if __name__ == "__main__":
						main()







"""simple docstring"""




def __snake_case (      UpperCamelCase = 10**6    )       ->    int:


		"""simple docstring"""





		if n <= 0:
				raise ValueError('''Please enter an integer greater than 0'''    )

		a__   		=     list(range(n + 1    )    )
		for number in range(2						,					n + 1    ):
				if phi[number] == number:
						phi[number] -= 1
						for multiple in range(number * 2						,					n + 1						,					UpperCamelCase    ):
								a__   		=     (phi[multiple] // number) * (number - 1)

		a__   		=     1
		for number in range(1						,					n + 1    ):
				if (answer / phi[answer]) < (number / phi[number]):
						a__   		=     number

		return answer


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




