






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
#
########################################################################


UpperCamelCase								=		16
UpperCamelCase								=		32






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE = 16				)     ->    Tuple:
							_lowercase							:   str 							=   AutoTokenizer.from_pretrained('bert-base-cased'				)
							_lowercase							:   Union[str, Any] 							=   load_dataset('glue'     ,     'mrpc'				)

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

							def collate_fn(SCREAMING_SNAKE_CASE				):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_lowercase							:   Tuple 							=   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							:   Dict 							=   16
														elif accelerator.mixed_precision != "no":
																					_lowercase							:   int 							=   8
														else:
																					_lowercase							:   Optional[int] 							=   None

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

							# Instantiate dataloaders.
							_lowercase							:   int 							=   DataLoader(
							    tokenized_datasets['train']     ,     shuffle=_lowerCAmelCase     ,     collate_fn=_lowerCAmelCase     ,     batch_size=_lowerCAmelCase     ,     drop_last=_lowerCAmelCase				)
							_lowercase							:   Union[str, Any] 							=   DataLoader(
							    tokenized_datasets['validation']     ,     shuffle=_lowerCAmelCase     ,     collate_fn=_lowerCAmelCase     ,     batch_size=_lowerCAmelCase     ,     drop_last=(accelerator.mixed_precision == 'fp8')     ,     )

							return train_dataloader, eval_dataloader






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    int:
							# Initialize accelerator
							_lowercase							:   str 							=   Accelerator(cpu=args.cpu     ,     mixed_precision=args.mixed_precision				)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_lowercase							:   int 							=   config['lr']
							_lowercase							:   Tuple 							=   int(config['num_epochs']				)
							_lowercase							:   Tuple 							=   int(config['seed']				)
							_lowercase							:   List[Any] 							=   int(config['batch_size']				)

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

							# If the batch size is too big we use gradient accumulation
							_lowercase							:   Optional[Any] 							=   1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														_lowercase							:   Tuple 							=   batch_size // MAX_GPU_BATCH_SIZE
														_lowercase							:   Optional[Any] 							=   MAX_GPU_BATCH_SIZE

							set_seed(_lowerCAmelCase				)
							_lowercase							:   Union[str, Any] 							=   get_dataloaders(_lowerCAmelCase     ,     _lowerCAmelCase				)
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							_lowercase							:   str 							=   AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,     return_dict=_lowerCAmelCase				)

							# 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							:   Optional[int] 							=   model.to(accelerator.device				)
							# Instantiate optimizer
							_lowercase							:   List[Any] 							=   AdamW(params=model.parameters()     ,     lr=_lowerCAmelCase				)

							# Instantiate scheduler
							_lowercase							:   List[Any] 							=   get_linear_schedule_with_warmup(
							    optimizer=_lowerCAmelCase     ,     num_warmup_steps=100     ,     num_training_steps=(len(_lowerCAmelCase				) * 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							:   List[str] 							=   accelerator.prepare(
							    _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase				)

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

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

														_lowercase							:   Optional[Any] 							=   metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"""epoch {epoch}:"""     ,     _lowerCAmelCase				)






def    __magic_name__    (	)     ->    List[str]:
							_lowercase							:   List[Any] 							=   argparse.ArgumentParser(description='Simple example of training script.'				)
							parser.add_argument(
							    '--mixed_precision'     ,     type=_lowerCAmelCase     ,     default=_lowerCAmelCase     ,     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							:   Optional[int] 							=   parser.parse_args()
							_lowercase							:   Union[str, Any] 							=   {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(_lowerCAmelCase     ,     _lowerCAmelCase				)


if __name__ == "__main__":
							main()









'''simple docstring'''





def 			a_	(				_lowerCAmelCase			) -> int:

  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			):
    raise TypeError('only integers accepted as input'			)
  else:
    __lowerCamelCase			:			Any       			=					str(abs(_lowerCAmelCase			)			)
    __lowerCamelCase			:			Tuple       			=					[list(_lowerCAmelCase			) for char in range(len(_lowerCAmelCase			)			)]
    for index in range(len(_lowerCAmelCase			)			):
      num_transpositions[index].pop(_lowerCAmelCase			)
    return max(
        int(''.join(list(_lowerCAmelCase			)			)			) for transposition in num_transpositions			)


if __name__ == "__main__":
   __import__('doctest').testmod()






