

import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


snake_case_        =			16
snake_case_        =			32

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Accelerator     ,					SCREAMING_SNAKE_CASE_		:   int = 16      ):



     '''simple docstring'''





     lowercase__   :       Any				=	AutoTokenizer.from_pretrained('bert-base-cased'      )
     lowercase__   :       int				=	load_dataset('glue'     ,					'mrpc'      )

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

     def collate_fn(SCREAMING_SNAKE_CASE_		:   Optional[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__   :       Tuple				=	16
          elif accelerator.mixed_precision != "no":
               lowercase__   :       Tuple				=	8
          else:
               lowercase__   :       Optional[Any]				=	None

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

     # Instantiate dataloaders.
     lowercase__   :       List[str]				=	DataLoader(
         tokenized_datasets['train']     ,					shuffle=SCREAMING_SNAKE_CASE_     ,					collate_fn=SCREAMING_SNAKE_CASE_     ,					batch_size=SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Dict				=	DataLoader(
         tokenized_datasets['validation']     ,					shuffle=SCREAMING_SNAKE_CASE_     ,					collate_fn=SCREAMING_SNAKE_CASE_     ,					batch_size=SCREAMING_SNAKE_CASE_      )

     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

       snake_case_        =			mocked_dataloaders  # noqa: F811

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Dict      ):



     '''simple docstring'''





     if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,					SCREAMING_SNAKE_CASE_      ) == "1":
          lowercase__   :       int				=	2
     # Initialize accelerator
     lowercase__   :       List[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__   :       str				=	config['lr']
     lowercase__   :       Union[str, Any]				=	int(config['num_epochs']      )
     lowercase__   :       Optional[Any]				=	int(config['seed']      )
     lowercase__   :       List[str]				=	int(config['batch_size']      )

     lowercase__   :       List[Any]				=	evaluate.load('glue'     ,					'mrpc'      )

     # New Code #
     # We now can define an inner training loop function. It should take a batch size as the only parameter,
     # and build the dataloaders in there.
     # It also gets our decorator
     @find_executable_batch_size(starting_batch_size=SCREAMING_SNAKE_CASE_      )
     def inner_training_loop(SCREAMING_SNAKE_CASE_		:   int      ):
          # And now just move everything below under this function
          # We need to bring in the Accelerator object from earlier
          nonlocal accelerator
          # And reset all of its attributes that could hold onto any memory:
          accelerator.free_memory()

          # Then we can declare the model, optimizer, and everything else:
          set_seed(SCREAMING_SNAKE_CASE_      )

          # Instantiate the model (we build the model here so that the seed also control new weights initialization)
          lowercase__   :       Optional[Any]				=	AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,					return_dict=SCREAMING_SNAKE_CASE_      )

          # 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__   :       Union[str, Any]				=	model.to(accelerator.device      )

          # Instantiate optimizer
          lowercase__   :       Tuple				=	AdamW(params=model.parameters()     ,					lr=SCREAMING_SNAKE_CASE_      )
          lowercase__   :       Optional[int]				=	get_dataloaders(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

          # Instantiate scheduler
          lowercase__   :       str				=	get_linear_schedule_with_warmup(
              optimizer=SCREAMING_SNAKE_CASE_     ,					num_warmup_steps=100     ,					num_training_steps=(len(SCREAMING_SNAKE_CASE_      ) * 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.
          lowercase__   :       Optional[Any]				=	accelerator.prepare(
              SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

          # Now we train the model
          for epoch in range(SCREAMING_SNAKE_CASE_      ):
               model.train()
               for step, batch in enumerate(SCREAMING_SNAKE_CASE_      ):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device      )
                    lowercase__   :       Optional[Any]				=	model(**SCREAMING_SNAKE_CASE_      )
                    lowercase__   :       Optional[int]				=	outputs.loss
                    accelerator.backward(SCREAMING_SNAKE_CASE_      )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               model.eval()
               for step, batch in enumerate(SCREAMING_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__   :       List[str]				=	model(**SCREAMING_SNAKE_CASE_      )
                    lowercase__   :       List[str]				=	outputs.logits.argmax(dim=-1      )
                    lowercase__   :       Optional[Any]				=	accelerator.gather_for_metrics((predictions, batch['labels'])      )
                    metric.add_batch(
                        predictions=SCREAMING_SNAKE_CASE_     ,					references=SCREAMING_SNAKE_CASE_     ,					)

               lowercase__   :       Any				=	metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(f"""epoch {epoch}:"""     ,					SCREAMING_SNAKE_CASE_      )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
     inner_training_loop()

def      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       Optional[Any]				=	argparse.ArgumentParser(description='Simple example of training script.'      )
     parser.add_argument(
         '--mixed_precision'     ,					type=SCREAMING_SNAKE_CASE_     ,					default=SCREAMING_SNAKE_CASE_     ,					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.'      )
     lowercase__   :       Any				=	parser.parse_args()
     lowercase__   :       Tuple				=	{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
       main()




def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     return 1 if digit in (0, 1) else (digit * factorial(digit - 1      ))

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	0
     lowercase__   :       Optional[int]				=	number
     while duplicate > 0:
          lowercase__				,			lowercase__   :       Tuple				=	divmod(SCREAMING_SNAKE_CASE_     ,					10      )
          fact_sum += factorial(SCREAMING_SNAKE_CASE_      )
     return fact_sum == number


if __name__ == "__main__":
       print('''Program to check whether a number is a Krisnamurthy Number or not.''')
       snake_case_        =			int(input('''Enter number: ''').strip())
       print(
           F'''{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number.'''
       )

