



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


_UpperCAmelCase            =		16
_UpperCAmelCase            =		32




def     UpperCamelCase						(	__lowercase						:			Optional[int]						,__lowercase						:			List[str] = 16     ):
		'''simple docstring'''




		A_		:    List[str]         =				AutoTokenizer.from_pretrained('bert-base-cased'     )
		A_		:    Optional[int]         =				load_dataset('glue'						,'mrpc'     )

		def tokenize_function(__lowercase						:			int     ):
				# max_length=None => use the model max length (it's actually the default)
				A_		:    List[Any]         =				tokenizer(examples['sentence1']						,examples['sentence2']						,truncation=a_						,max_length=a_     )
				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_		:    List[str]         =				datasets.map(
				    a_						,batched=a_						,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_		:    Dict         =				tokenized_datasets.rename_column('label'						,'labels'     )

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

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

		# Instantiate dataloaders.
		A_		:    List[Any]         =				DataLoader(
		    tokenized_datasets['train']						,shuffle=a_						,collate_fn=a_						,batch_size=a_     )
		A_		:    Any         =				DataLoader(
		    tokenized_datasets['validation']						,shuffle=a_						,collate_fn=a_						,batch_size=a_     )

		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

	_UpperCAmelCase            =		mocked_dataloaders  # noqa: F811




def     UpperCamelCase						(	__lowercase						:			List[Any]						,__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




		if os.environ.get('TESTING_MOCKED_DATALOADERS'						,a_     ) == "1":
				A_		:    Optional[Any]         =				2
		# Initialize accelerator
		A_		:    int         =				Accelerator(cpu=args.cpu						,mixed_precision=args.mixed_precision     )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		A_		:    Any         =				config['lr']
		A_		:    Any         =				int(config['num_epochs']     )
		A_		:    List[str]         =				int(config['seed']     )
		A_		:    Tuple         =				int(config['batch_size']     )

		A_		:    Union[str, 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=a_     )
		def inner_training_loop(__lowercase						:			List[str]     ):
				# 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(a_     )

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

				# 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_		:    int         =				model.to(accelerator.device     )

				# Instantiate optimizer
				A_		:    List[str]         =				AdamW(params=model.parameters()						,lr=a_     )
				A_       ,      A_		:    Dict         =				get_dataloaders(a_						,a_     )

				# Instantiate scheduler
				A_		:    List[Any]         =				get_linear_schedule_with_warmup(
				    optimizer=a_						,num_warmup_steps=1_00						,num_training_steps=(len(a_     ) * 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_		:    List[Any]         =				accelerator.prepare(
				    a_						,a_						,a_						,a_						,a_     )

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

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

						A_		:    Union[str, Any]         =				metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''						,a_     )

    # 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     UpperCamelCase						(	):
		'''simple docstring'''




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


if __name__ == "__main__":
	main()







from ..utils import DummyObject, requires_backends





class UpperCAmelCase						( metaclass=__A  ):


				'''simple docstring'''






				lowerCamelCase_    = ['''onnx''']
				def __init__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(self		,     ['onnx'] )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )







				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )






