

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


SCREAMING_SNAKE_CASE_					:	Tuple							  =						1_6
SCREAMING_SNAKE_CASE_					:	str							  =						3_2





def  _snake_case     (    UpperCAmelCase_  :					Accelerator					,   UpperCAmelCase_  :					int = 16			):
     A__										=  AutoTokenizer.from_pretrained("""bert-base-cased"""			)
     A__										=  load_dataset("""glue"""					,   """mrpc"""			)

     def tokenize_function(UpperCAmelCase_  :					Tuple			):
          # 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_  :					int			):
          # 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

 SCREAMING_SNAKE_CASE_					:	Dict							  =						mocked_dataloaders  # noqa: F811





def  _snake_case     (    UpperCAmelCase_  :					Union[str, Any]					,   UpperCAmelCase_  :					Dict			):
     # For testing only
     if os.environ.get("""TESTING_MOCKED_DATALOADERS"""					,   UpperCAmelCase_			) == "1":
          A__										=  2
     # New Code #
     A__										=  int(args.gradient_accumulation_steps			)
     # Initialize accelerator
     A__										=  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
     A__										=  config["""lr"""]
     A__										=  int(config["""num_epochs"""]			)
     A__										=  int(config["""seed"""]			)
     A__										=  int(config["""batch_size"""]			)

     A__										=  evaluate.load("""glue"""					,   """mrpc"""			)

     set_seed(UpperCAmelCase_			)
     A__			,				A__										=  get_dataloaders(UpperCAmelCase_					,   UpperCAmelCase_			)
     # 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)					,   )

     # 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_			)

     # 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_			):
                    A__										=  model(**UpperCAmelCase_			)
                    A__										=  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():
                    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_			)





def  _snake_case     (    ):
     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."""					,   )
     # 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."""			)
     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_  :					int			):
     A__										=  1
     for i in range(1					,   num + 1			):
          fact *= i
     return fact





def  _snake_case     (    UpperCAmelCase_  :					int			):
     A__										=  0
     while number > 0:
          A__										=  number % 10
          sum_of_digits += last_digit
          A__										=  number // 10  # Removing the last_digit from the given number
     return sum_of_digits





def  _snake_case     (    UpperCAmelCase_  :					int = 100			):
     A__										=  factorial(UpperCAmelCase_			)
     A__										=  split_and_add(UpperCAmelCase_			)
     return result


if __name__ == "__main__":
 print(solution(int(input('Enter the Number: ').strip())))






