





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
# and perform gradient accumulation
#
# 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 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 							=       16
_lowercase 							=       32





def   lowerCamelCase__       (       UpperCamelCase__     :    Accelerator    ,      UpperCamelCase__     :    int = 16  )     ->	Optional[int]:



					'''simple docstring'''




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

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

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

					# Instantiate dataloaders.
					_snake_case													=			DataLoader(
					    tokenized_datasets['train']    ,      shuffle=UpperCamelCase__    ,      collate_fn=UpperCamelCase__    ,      batch_size=UpperCamelCase__  )
					_snake_case													=			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 							=       mocked_dataloaders  # noqa: F811





def   lowerCamelCase__       (       UpperCamelCase__     :    Union[str, Any]    ,      UpperCamelCase__     :    str  )     ->	List[Any]:



					'''simple docstring'''




					if os.environ.get('TESTING_MOCKED_DATALOADERS'    ,      UpperCamelCase__  ) == "1":
										_snake_case													=			2
					# New Code #
					_snake_case													=			int(args.gradient_accumulation_steps  )
					# Initialize accelerator
					_snake_case													=			Accelerator(
					    cpu=args.cpu    ,      mixed_precision=args.mixed_precision    ,      gradient_accumulation_steps=UpperCamelCase__  )
					if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
										raise NotImplementedError(
										    'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'  )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_snake_case													=			config['lr']
					_snake_case													=			int(config['num_epochs']  )
					_snake_case													=			int(config['seed']  )
					_snake_case													=			int(config['batch_size']  )

					_snake_case													=			evaluate.load('glue'    ,      'mrpc'  )

					set_seed(UpperCamelCase__  )
					_snake_case							,       _snake_case													=			get_dataloaders(UpperCamelCase__    ,      UpperCamelCase__  )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					_snake_case													=			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).
					_snake_case													=			model.to(accelerator.device  )

					# Instantiate optimizer
					_snake_case													=			AdamW(params=model.parameters()    ,      lr=UpperCamelCase__  )

					# Instantiate scheduler
					_snake_case													=			get_linear_schedule_with_warmup(
					    optimizer=UpperCamelCase__    ,      num_warmup_steps=100    ,      num_training_steps=(len(UpperCamelCase__  ) * num_epochs)    ,      )

					# 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.
					_snake_case							,       _snake_case							,       _snake_case							,       _snake_case							,       _snake_case													=			accelerator.prepare(
					    UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  )

					# Now we train the model
					for epoch in range(UpperCamelCase__  ):
										model.train()
										for step, batch in enumerate(UpperCamelCase__  ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device  )
															# New code #
															# We use the new `accumulate` context manager to perform gradient accumulation
															# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
															with accelerator.accumulate(UpperCamelCase__  ):
																				_snake_case													=			model(**UpperCamelCase__  )
																				_snake_case													=			output.loss
																				accelerator.backward(UpperCamelCase__  )
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

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

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





def   lowerCamelCase__       (       )     ->	Dict:



					'''simple docstring'''




					_snake_case													=			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.'    ,      )
					# New Code #
					parser.add_argument(
					    '--gradient_accumulation_steps'    ,      type=UpperCamelCase__    ,      default=1    ,      help='The number of minibatches to be ran before gradients are accumulated.'    ,      )
					parser.add_argument('--cpu'    ,      action='store_true'    ,      help='If passed, will train on the CPU.'  )
					_snake_case													=			parser.parse_args()
					_snake_case													=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					training_function(UpperCamelCase__    ,      UpperCamelCase__  )


if __name__ == "__main__":
					main()










def   lowerCamelCase__       (       )     ->	int:



					'''simple docstring'''




					return 1





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




					return 0 if x < 0 else two_pence(x - 2  ) + one_pence()





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




					return 0 if x < 0 else five_pence(x - 5  ) + two_pence(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




					return 0 if x < 0 else ten_pence(x - 10  ) + five_pence(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




					return 0 if x < 0 else twenty_pence(x - 20  ) + ten_pence(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




					return 0 if x < 0 else fifty_pence(x - 50  ) + twenty_pence(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




					return 0 if x < 0 else one_pound(x - 100  ) + fifty_pence(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	int:



					'''simple docstring'''




					return 0 if x < 0 else two_pound(x - 200  ) + one_pound(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    int = 200  )     ->	int:



					'''simple docstring'''




					return two_pound(UpperCamelCase__  )


if __name__ == "__main__":
					print(solution(int(input().strip())))



