


'''simple docstring'''






import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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       							=				16
__UpperCAmelCase       							=				32


def    _snake_case  (	A					,       A					,       A					,       A					,       A = 16			)		->       Optional[Any]:
							lowerCAmelCase__			  =      AutoTokenizer.from_pretrained('''bert-base-cased'''			)
							lowerCAmelCase__			  =      DatasetDict(
							    {
							        '''train''': dataset['''train'''].select(A			),
							        '''validation''': dataset['''train'''].select(A			),
							        '''test''': dataset['''validation'''],
							    }			)

							def tokenize_function(A			):
														# max_length=None => use the model max length (it's actually the default)
														lowerCAmelCase__			  =      tokenizer(examples['''sentence1''']					,       examples['''sentence2''']					,       truncation=A					,       max_length=A			)
														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():
														lowerCAmelCase__			  =      datasets.map(
														    A					,       batched=A					,       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
							lowerCAmelCase__			  =      tokenized_datasets.rename_column('''label'''					,       '''labels'''			)

							def collate_fn(A			):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														lowerCAmelCase__			  =      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":
																					lowerCAmelCase__			  =      16
														elif accelerator.mixed_precision != "no":
																					lowerCAmelCase__			  =      8
														else:
																					lowerCAmelCase__			  =      None

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

							# Instantiate dataloaders.
							lowerCAmelCase__			  =      DataLoader(
							    tokenized_datasets['''train''']					,       shuffle=A					,       collate_fn=A					,       batch_size=A			)
							lowerCAmelCase__			  =      DataLoader(
							    tokenized_datasets['''validation''']					,       shuffle=A					,       collate_fn=A					,       batch_size=A			)

							lowerCAmelCase__			  =      DataLoader(
							    tokenized_datasets['''test''']					,       shuffle=A					,       collate_fn=A					,       batch_size=A			)

							return train_dataloader, eval_dataloader, test_dataloader


def    _snake_case  (	A					,       A			)		->       int:
							# New Code #
							lowerCAmelCase__			  =      []
							# Download the dataset
							lowerCAmelCase__			  =      load_dataset('''glue'''					,       '''mrpc'''			)
							# Create our splits
							lowerCAmelCase__			  =      StratifiedKFold(n_splits=int(args.num_folds			)			)
							# Initialize accelerator
							lowerCAmelCase__			  =      Accelerator(cpu=args.cpu					,       mixed_precision=args.mixed_precision			)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							lowerCAmelCase__			  =      config['''lr''']
							lowerCAmelCase__			  =      int(config['''num_epochs''']			)
							lowerCAmelCase__			  =      int(config['''seed''']			)
							lowerCAmelCase__			  =      int(config['''batch_size''']			)

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

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

							set_seed(A			)

							# New Code #
							# Create our folds:
							lowerCAmelCase__			  =      kfold.split(np.zeros(datasets['''train'''].num_rows			)					,       datasets['''train''']['''label''']			)
							lowerCAmelCase__			  =      []
							# Iterate over them
							for i, (train_idxs, valid_idxs) in enumerate(A			):
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      get_fold_dataloaders(
														    A					,       A					,       A					,       A					,       )
														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														lowerCAmelCase__			  =      AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''					,       return_dict=A			)

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

														# Instantiate optimizer
														lowerCAmelCase__			  =      AdamW(params=model.parameters()					,       lr=A			)

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

														# Now we train the model
														for epoch in range(A			):
																					model.train()
																					for step, batch in enumerate(A			):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device			)
																												lowerCAmelCase__			  =      model(**A			)
																												lowerCAmelCase__			  =      outputs.loss
																												lowerCAmelCase__			  =      loss / gradient_accumulation_steps
																												accelerator.backward(A			)
																												if step % gradient_accumulation_steps == 0:
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()

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

																					lowerCAmelCase__			  =      metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(F"""epoch {epoch}:"""					,       A			)

														# New Code #
														# We also run predictions on the test set at the very end
														lowerCAmelCase__			  =      []
														for step, batch in enumerate(A			):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device			)
																					with torch.no_grad():
																												lowerCAmelCase__			  =      model(**A			)
																					lowerCAmelCase__			  =      outputs.logits
																					lowerCAmelCase__  ,   lowerCAmelCase__			  =      accelerator.gather_for_metrics((predictions, batch['''labels'''])			)
																					fold_predictions.append(predictions.cpu()			)
																					if i == 0:
																												# We need all of the test predictions
																												test_references.append(references.cpu()			)
        # Use accelerator.print to print only on the main process.
														test_predictions.append(torch.cat(A					,       dim=0			)			)
														# We now need to release all our memory and get rid of the current model, optimizer, etc
														accelerator.free_memory()
							# New Code #
							# Finally we check the accuracy of our folded results:
							lowerCAmelCase__			  =      torch.cat(A					,       dim=0			)
							lowerCAmelCase__			  =      torch.stack(A					,       dim=0			).sum(dim=0			).div(int(args.num_folds			)			).argmax(dim=-1			)
							lowerCAmelCase__			  =      metric.compute(predictions=A					,       references=A			)
							accelerator.print('''Average test metrics from all folds:'''					,       A			)


def    _snake_case  (	)		->       List[str]:
							lowerCAmelCase__			  =      argparse.ArgumentParser(description='''Simple example of training script.'''			)
							parser.add_argument(
							    '''--mixed_precision'''					,       type=A					,       default=A					,       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.'''			)
							# New Code #
							parser.add_argument('''--num_folds'''					,       type=A					,       default=3					,       help='''The number of splits to perform across the dataset'''			)
							lowerCAmelCase__			  =      parser.parse_args()
							lowerCAmelCase__			  =      {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
							training_function(A					,       A			)


if __name__ == "__main__":
		main()



'''simple docstring'''






def    _snake_case  (	A			)		->       int:
							if n == 1 or not isinstance(A					,       A			):
														return 0
							elif n == 2:
														return 1
							else:
														lowerCAmelCase__			  =      [0, 1]
														for i in range(2					,       n + 1			):
																					sequence.append(sequence[i - 1] + sequence[i - 2]			)

														return sequence[n]


def    _snake_case  (	A			)		->       int:
							lowerCAmelCase__			  =      0
							lowerCAmelCase__			  =      2

							while digits < n:
														index += 1
														lowerCAmelCase__			  =      len(str(fibonacci(A			)			)			)

							return index


def    _snake_case  (	A = 1000			)		->       int:
							return fibonacci_digits_index(A			)


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