
"""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
#
########################################################################

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

def __snake_case (      UpperCamelCase						,					UpperCamelCase = 16    )       ->    Tuple:


		"""simple docstring"""





		a__   		=     AutoTokenizer.from_pretrained('''bert-base-cased'''    )
		a__   		=     load_dataset('''glue'''						,					'''mrpc'''    )

		def tokenize_function(UpperCamelCase    ):
				# max_length=None => use the model max length (it's actually the default)
				a__   		=     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():
				a__   		=     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
		a__   		=     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.
				a__   		=     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":
						a__   		=     16
				elif accelerator.mixed_precision != "no":
						a__   		=     8
				else:
						a__   		=     None

				return tokenizer.pad(
				    UpperCamelCase						,					padding='''longest'''						,					max_length=UpperCamelCase						,					pad_to_multiple_of=UpperCamelCase						,					return_tensors='''pt'''						,					)

		# Instantiate dataloaders.
		a__   		=     DataLoader(
		    tokenized_datasets['''train''']						,					shuffle=UpperCamelCase						,					collate_fn=UpperCamelCase						,					batch_size=UpperCamelCase    )
		a__   		=     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

					__lowerCAmelCase							:  Any 				=		mocked_dataloaders  # noqa: F811

def __snake_case (      UpperCamelCase						,					UpperCamelCase    )       ->    Union[str, Any]:


		"""simple docstring"""





		if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,					UpperCamelCase    ) == "1":
				a__   		=     2
		# Initialize accelerator
		a__   		=     Accelerator(cpu=args.cpu						,					mixed_precision=args.mixed_precision    )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		a__   		=     config['''lr''']
		a__   		=     int(config['''num_epochs''']    )
		a__   		=     int(config['''seed''']    )
		a__   		=     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":
						a__   		=     args.checkpointing_steps
				elif args.checkpointing_steps.isdigit():
						a__   		=     int(args.checkpointing_steps    )
				else:
						raise ValueError(
						    f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."    )
		else:
				a__   		=     None

		set_seed(UpperCamelCase    )

		a__		,	a__   		=     get_dataloaders(UpperCamelCase						,					UpperCamelCase    )
		a__   		=     evaluate.load('''glue'''						,					'''mrpc'''    )

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

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		a__   		=     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).
		a__   		=     model.to(accelerator.device    )

		# Instantiate optimizer
		a__   		=     AdamW(params=model.parameters()						,					lr=UpperCamelCase    )

		# Instantiate scheduler
		a__   		=     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.
		a__		,	a__		,	a__		,	a__		,	a__   		=     accelerator.prepare(
		    UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase    )

		# New Code #
		# We need to keep track of how many total steps we have iterated over
		a__   		=     0
		# We also need to keep track of the stating epoch so files are named properly
		a__   		=     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    )
						a__   		=     os.path.basename(args.resume_from_checkpoint    )
				else:
						# Get the most recent checkpoint
						a__   		=     [f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
						dirs.sort(key=os.path.getctime    )
						a__   		=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract `epoch_{i}` or `step_{i}`
				a__   		=     os.path.splitext(UpperCamelCase    )[0]

				if "epoch" in training_difference:
						a__   		=     int(training_difference.replace('''epoch_'''						,					''''''    )    ) + 1
						a__   		=     None
				else:
						a__   		=     int(training_difference.replace('''step_'''						,					''''''    )    )
						a__   		=     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
						a__   		=     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
						a__   		=     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    )
						a__   		=     model(**UpperCamelCase    )
						a__   		=     outputs.loss
						a__   		=     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    ):
								a__   		=     f"step_{overall_step}"
								if overall_step % checkpointing_steps == 0:
										if args.output_dir is not None:
												a__   		=     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():
								a__   		=     model(**UpperCamelCase    )
						a__   		=     outputs.logits.argmax(dim=-1    )
						a__		,	a__   		=     accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
						metric.add_batch(
						    predictions=UpperCamelCase						,					references=UpperCamelCase						,					)

				a__   		=     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":
						a__   		=     f"epoch_{epoch}"
						if args.output_dir is not None:
								a__   		=     os.path.join(args.output_dir						,					UpperCamelCase    )
						accelerator.save_state(UpperCamelCase    )

def __snake_case (      )       ->    Dict:


		"""simple docstring"""





		a__   		=     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.'''						,					)
		a__   		=     parser.parse_args()
		a__   		=     {'''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())




