


import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__lowerCAmelCase		       =							16
__lowerCAmelCase		       =							32




def    snake_case_     (  snake_case			,							snake_case = 16			,							snake_case = "bert-base-cased"     )  ->				Union[str, Any]:
       lowercase__:   Optional[Any]  			=   AutoTokenizer.from_pretrained(snake_case     )
       lowercase__:   Any  			=   load_dataset('glue'			,							'mrpc'     )

       def tokenize_function(snake_case     ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase__:   List[str]  			=   tokenizer(examples['sentence1']			,							examples['sentence2']			,							truncation=snake_case			,							max_length=snake_case     )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       lowercase__:   Any  			=   datasets.map(
           snake_case			,							batched=snake_case			,							remove_columns=['idx', 'sentence1', 'sentence2']			,							load_from_cache_file=snake_case     )

       # 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(snake_case     ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(snake_case			,							padding='max_length'			,							max_length=1_28			,							return_tensors='pt'     )
              return tokenizer.pad(snake_case			,							padding='longest'			,							return_tensors='pt'     )

       # Instantiate dataloaders.
       lowercase__:   Optional[int]  			=   DataLoader(
           tokenized_datasets['train']			,							shuffle=snake_case			,							collate_fn=snake_case			,							batch_size=snake_case     )
       lowercase__:   Optional[int]  			=   DataLoader(
           tokenized_datasets['validation']			,							shuffle=snake_case			,							collate_fn=snake_case			,							batch_size=snake_case     )

       return train_dataloader, eval_dataloader




def    snake_case_     (  snake_case			,							snake_case     )  ->				Dict:
       # Initialize accelerator
       lowercase__:   Optional[Any]  			=   Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase__:   List[str]  			=   config['lr']
       lowercase__:   Optional[int]  			=   int(config['num_epochs']     )
       lowercase__:   str  			=   int(config['seed']     )
       lowercase__:   Dict  			=   int(config['batch_size']     )
       lowercase__:   List[Any]  			=   args.model_name_or_path

       set_seed(snake_case     )
       lowercase__			,						lowercase__:   Optional[Any]  			=   get_dataloaders(snake_case			,							snake_case			,							snake_case     )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase__:   Any  			=   AutoModelForSequenceClassification.from_pretrained(snake_case			,							return_dict=snake_case     )

       # Instantiate optimizer
       lowercase__:   Union[str, Any]  			=   (
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       lowercase__:   Optional[Any]  			=   optimizer_cls(params=model.parameters()			,							lr=snake_case     )

       if accelerator.state.deepspeed_plugin is not None:
              lowercase__:   str  			=   accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              lowercase__:   Optional[int]  			=   1
       lowercase__:   Union[str, Any]  			=   (len(snake_case     ) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              lowercase__:   Optional[int]  			=   get_linear_schedule_with_warmup(
                  optimizer=snake_case			,							num_warmup_steps=0			,							num_training_steps=snake_case			,							)
       else:
              lowercase__:   List[str]  			=   DummyScheduler(snake_case			,							total_num_steps=snake_case			,							warmup_num_steps=0     )

       # 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__:   Optional[int]  			=   accelerator.prepare(
           snake_case			,							snake_case			,							snake_case			,							snake_case			,							snake_case     )

       # We need to keep track of how many total steps we have iterated over
       lowercase__:   Tuple  			=   0
       # We also need to keep track of the stating epoch so files are named properly
       lowercase__:   Optional[Any]  			=   0

       # Now we train the model
       lowercase__:   Union[str, Any]  			=   evaluate.load('glue'			,							'mrpc'     )
       lowercase__:   Optional[Any]  			=   0
       lowercase__:   List[str]  			=   {}
       for epoch in range(snake_case			,							snake_case     ):
              model.train()
              for step, batch in enumerate(snake_case     ):
                     lowercase__:   Optional[Any]  			=   model(**snake_case     )
                     lowercase__:   List[Any]  			=   outputs.loss
                     lowercase__:   Optional[Any]  			=   loss / gradient_accumulation_steps
                     accelerator.backward(snake_case     )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

              model.eval()
              lowercase__:   Dict  			=   0
              for step, batch in enumerate(snake_case     ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device     )
                     with torch.no_grad():
                            lowercase__:   int  			=   model(**snake_case     )
                     lowercase__:   List[str]  			=   outputs.logits.argmax(dim=-1     )
                     # It is slightly faster to call this once, than multiple times
                     lowercase__			,						lowercase__:   str  			=   accelerator.gather(
                         (predictions, batch['labels'])     )  # If we are in a multiprocess environment, the last batch has duplicates
                     if accelerator.use_distributed:
                            if step == len(snake_case     ) - 1:
                                   lowercase__:   Tuple  			=   predictions[: len(eval_dataloader.dataset     ) - samples_seen]
                                   lowercase__:   Any  			=   references[: len(eval_dataloader.dataset     ) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=snake_case			,							references=snake_case			,							)

              lowercase__:   str  			=   metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f'epoch {epoch}:'			,							snake_case     )
              lowercase__:   str  			=   eval_metric['accuracy']

              if best_performance < eval_metric["accuracy"]:
                     lowercase__:   List[str]  			=   eval_metric['accuracy']

       if args.performance_lower_bound is not None:
              assert (
                  args.performance_lower_bound <= best_performance
              ), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir			,							'all_results.json'     )			,							'w'     ) as f:
                     json.dump(snake_case			,							snake_case     )




def    snake_case_     (  )  ->				Dict:
       lowercase__:   Dict  			=   argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'     )
       parser.add_argument(
           '--model_name_or_path'			,							type=snake_case			,							default='bert-base-cased'			,							help='Path to pretrained model or model identifier from huggingface.co/models.'			,							required=snake_case			,							)
       parser.add_argument(
           '--output_dir'			,							type=snake_case			,							default='.'			,							help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'			,							)
       parser.add_argument(
           '--performance_lower_bound'			,							type=snake_case			,							default=snake_case			,							help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'			,							)
       parser.add_argument(
           '--num_epochs'			,							type=snake_case			,							default=3			,							help='Number of train epochs.'			,							)
       lowercase__:   str  			=   parser.parse_args()
       lowercase__:   Union[str, Any]  			=   {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
       training_function(snake_case			,							snake_case     )


if __name__ == "__main__":
 main()






def    snake_case_     (  snake_case     )  ->				int:
       if n == 1 or not isinstance(snake_case			,							snake_case     ):
              return 0
       elif n == 2:
              return 1
       else:
              lowercase__:   Optional[Any]  			=   [0, 1]
              for i in range(2			,							n + 1     ):
                     sequence.append(sequence[i - 1] + sequence[i - 2]     )

              return sequence[n]




def    snake_case_     (  snake_case     )  ->				int:
       lowercase__:   int  			=   0
       lowercase__:   int  			=   2

       while digits < n:
              index += 1
              lowercase__:   Tuple  			=   len(str(fibonacci(snake_case     )     )     )

       return index




def    snake_case_     (  snake_case = 10_00     )  ->				int:
       return fibonacci_digits_index(snake_case     )


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


