"""simple docstring"""







import argparse

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
#
# 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             =16
__SCREAMING_SNAKE_CASE             =32



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Optional[int]	,     __SCREAMING_SNAKE_CASE				:	Optional[int] = 16 ):
     lowercase_      :							Union[str, Any]						=      AutoTokenizer.from_pretrained('bert-base-cased' )
     lowercase_      :							Union[str, Any]						=      load_dataset('glue'	,     'mrpc' )

     def tokenize_function(__SCREAMING_SNAKE_CASE				:	Optional[int] ):
          # max_length=None => use the model max length (it's actually the default)
          lowercase_      :							Optional[Any]						=      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():
          lowercase_      :							Dict						=      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
     lowercase_      :							Optional[Any]						=      tokenized_datasets.rename_column('label'	,     'labels' )

     def collate_fn(__SCREAMING_SNAKE_CASE				:	List[Any] ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          lowercase_      :							Optional[int]						=      1_28 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_      :							Dict						=      16
          elif accelerator.mixed_precision != "no":
               lowercase_      :							int						=      8
          else:
               lowercase_      :							Union[str, Any]						=      None

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

     # Instantiate dataloaders.
     lowercase_      :							List[str]						=      DataLoader(
         tokenized_datasets['train']	,     shuffle=UpperCAmelCase_	,     collate_fn=UpperCAmelCase_	,     batch_size=UpperCAmelCase_	,     drop_last=UpperCAmelCase_ )
     lowercase_      :							Optional[Any]						=      DataLoader(
         tokenized_datasets['validation']	,     shuffle=UpperCAmelCase_	,     collate_fn=UpperCAmelCase_	,     batch_size=UpperCAmelCase_	,     drop_last=(accelerator.mixed_precision == 'fp8')	,     )

     return train_dataloader, eval_dataloader



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Optional[int]	,     __SCREAMING_SNAKE_CASE				:	List[Any] ):
     # Initialize accelerator
     lowercase_      :							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_      :							List[Any]						=      config['lr']
     lowercase_      :							List[Any]						=      int(config['num_epochs'] )
     lowercase_      :							Any						=      int(config['seed'] )
     lowercase_      :							Any						=      int(config['batch_size'] )

     lowercase_      :							Union[str, Any]						=      evaluate.load('glue'	,     'mrpc' )

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

     set_seed(UpperCAmelCase_ )
     lowercase_      :							int						=      get_dataloaders(UpperCAmelCase_	,     UpperCAmelCase_ )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowercase_      :							int						=      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).
     lowercase_      :							str						=      model.to(accelerator.device )
     # Instantiate optimizer
     lowercase_      :							List[Any]						=      AdamW(params=model.parameters()	,     lr=UpperCAmelCase_ )

     # Instantiate scheduler
     lowercase_      :							List[Any]						=      get_linear_schedule_with_warmup(
         optimizer=UpperCAmelCase_	,     num_warmup_steps=1_00	,     num_training_steps=(len(UpperCAmelCase_ ) * 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.

     lowercase_      :							Any						=      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 )
               lowercase_      :							Optional[int]						=      model(**UpperCAmelCase_ )
               lowercase_      :							Tuple						=      outputs.loss
               lowercase_      :							List[Any]						=      loss / gradient_accumulation_steps
               accelerator.backward(UpperCAmelCase_ )
               if step % gradient_accumulation_steps == 0:
                    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():
                    lowercase_      :							Any						=      model(**UpperCAmelCase_ )
               lowercase_      :							Union[str, Any]						=      outputs.logits.argmax(dim=-1 )
               lowercase_      :							Optional[Any]						=      accelerator.gather_for_metrics((predictions, batch['labels']) )
               metric.add_batch(
                   predictions=UpperCAmelCase_	,     references=UpperCAmelCase_	,     )

          lowercase_      :							Optional[int]						=      metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''	,     UpperCAmelCase_ )



def      lowercase__(			):
     lowercase_      :							List[str]						=      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.'	,     )
     parser.add_argument('--cpu'	,     action='store_true'	,     help='If passed, will train on the CPU.' )
     lowercase_      :							List[str]						=      parser.parse_args()
     lowercase_      :							Dict						=      {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(UpperCAmelCase_	,     UpperCAmelCase_ )


if __name__ == "__main__":
       main()






'''simple docstring'''
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						return base * power(UpperCAmelCase_    ,    (exponent - 1)				) if exponent else 1


if __name__ == "__main__":
					print('Raise base to the power of exponent using recursion...')
					snake_case_					:      int    			=    int(input('Enter the base: ').strip())
					snake_case_					:      Optional[int]    			=    int(input('Enter the exponent: ').strip())
					snake_case_					:      Optional[int]    			=    power(base, abs(exponent))
					if exponent < 0:  # power() does not properly deal w/ negative exponents
										snake_case_					:      List[Any]    			=    1 / result
					print(F"""{base} to the power of {exponent} is {result}""")


