



'''simple docstring'''



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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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
#
########################################################################

lowerCAmelCase						:		Tuple						      = 3_2



def 	__lowerCAmelCase       (				lowerCamelCase :    Accelerator					,		lowerCamelCase :    int = 16       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	AutoTokenizer.from_pretrained("bert-base-cased"       )
	__lowerCAmelCase       				=	load_dataset("glue"					,		"mrpc"       )

	def tokenize_function(lowerCamelCase :    List[Any]       ):
		# max_length=None => use the model max length (it's actually the default)
		__lowerCAmelCase       				=	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():
		__lowerCAmelCase       				=	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
	__lowerCAmelCase       				=	tokenized_datasets.rename_column("label"					,		"labels"       )

	def collate_fn(lowerCamelCase :    Union[str, Any]       ):
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			__lowerCAmelCase       				=	16
		elif accelerator.mixed_precision != "no":
			__lowerCAmelCase       				=	8
		else:
			__lowerCAmelCase       				=	None

		return tokenizer.pad(
		    lowerCamelCase					,		padding="longest"					,		pad_to_multiple_of=lowerCamelCase					,		return_tensors="pt"					,		)

	# Instantiate dataloaders.
	__lowerCAmelCase       				=	DataLoader(
	    tokenized_datasets["train"]					,		shuffle=lowerCamelCase					,		collate_fn=lowerCamelCase					,		batch_size=lowerCamelCase       )
	__lowerCAmelCase       				=	DataLoader(
	    tokenized_datasets["validation"]					,		shuffle=lowerCamelCase					,		collate_fn=lowerCamelCase					,		batch_size=lowerCamelCase       )

	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

	lowerCAmelCase						:		List[Any]						      = mocked_dataloaders  # noqa: F811



def 	__lowerCAmelCase       (				lowerCamelCase :    List[str]					,		lowerCamelCase :    Dict       ):






	'''simple docstring'''




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

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

	# New Code #
	# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
	# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
	# half each time. From this, we can calculate the number of gradient accumulation steps needed
	# and modify the Accelerator object as a result
	@find_executable_batch_size(starting_batch_size=int(lowerCamelCase       )       )
	def inner_training_loop(lowerCamelCase :    List[str]       ):
		# Since we need to modify the outside accelerator object, we need to bring it
		# to the local scope
		nonlocal accelerator

		# We can calculate the number of gradient accumulation steps based on the current
		# batch size vs the starting batch size
		__lowerCAmelCase       				=	observed_batch_size // batch_size

		# And then set it in the Accelerator directly:
		__lowerCAmelCase       				=	num_gradient_accumulation_steps

		# Next we need to free all of the stored model references in the Accelerator each time
		accelerator.free_memory()

		# And set the seed so our results are reproducable each reset
		set_seed(lowerCamelCase       )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__lowerCAmelCase       				=	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).
		__lowerCAmelCase       				=	model.to(accelerator.device       )

		# Instantiate optimizer
		__lowerCAmelCase       				=	AdamW(params=model.parameters()					,		lr=lowerCamelCase       )
		__lowerCAmelCase				,							__lowerCAmelCase       				=	get_dataloaders(lowerCamelCase					,		lowerCamelCase       )

		# Instantiate scheduler
		__lowerCAmelCase       				=	get_linear_schedule_with_warmup(
		    optimizer=lowerCamelCase					,		num_warmup_steps=1_00					,		num_training_steps=(len(lowerCamelCase       ) * 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.
		__lowerCAmelCase				,							__lowerCAmelCase				,							__lowerCAmelCase				,							__lowerCAmelCase				,							__lowerCAmelCase       				=	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       ):
				# And perform gradient accumulation
				with accelerator.accumulate(lowerCamelCase       ):
					# We could avoid this line since we set the accelerator with `device_placement=True`.
					batch.to(accelerator.device       )
					__lowerCAmelCase       				=	model(**lowerCamelCase       )
					__lowerCAmelCase       				=	outputs.loss
					accelerator.backward(lowerCamelCase       )
					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():
					__lowerCAmelCase       				=	model(**lowerCamelCase       )
				__lowerCAmelCase       				=	outputs.logits.argmax(dim=-1       )
				__lowerCAmelCase				,							__lowerCAmelCase       				=	accelerator.gather_for_metrics((predictions, batch["labels"])       )
				metric.add_batch(
				    predictions=lowerCamelCase					,		references=lowerCamelCase					,		)

			__lowerCAmelCase       				=	metric.compute()
			# Use accelerator.print to print only on the main process.
			accelerator.print(f'''epoch {epoch}:'''					,		lowerCamelCase       )

    # 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 	__lowerCAmelCase       (				):






	'''simple docstring'''




	__lowerCAmelCase       				=	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."       )
	__lowerCAmelCase       				=	parser.parse_args()
	# New Code #
	# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
	__lowerCAmelCase       				=	{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 2_56}
	training_function(lowerCamelCase					,		lowerCamelCase       )


if __name__ == "__main__":
	main()




'''simple docstring'''



from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase						:		Optional[Any]						      = '''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


lowerCAmelCase						:		str						      = '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


lowerCAmelCase						:		int						      = '''
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,							_KWARGS_DESCRIPTION			)
class        UpperCAmelCase__						(		datasets.Metric			):



					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						return datasets.MetricInfo(
						    description=_DESCRIPTION      ,					citation=_CITATION      ,					inputs_description=_KWARGS_DESCRIPTION      ,					features=datasets.Features(
						        {
						            "predictions": datasets.Sequence(datasets.Value("int32"  )  ),
						            "references": datasets.Sequence(datasets.Value("int32"  )  ),
						        }
						        if self.config_name == "multilabel"
						        else {
						            "predictions": datasets.Value("int32"  ),
						            "references": datasets.Value("int32"  ),
						        }  )      ,					reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]      ,					)



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase=True      ,					UpperCamelCase=None  )  ->      List[str]:
						return {
						    "accuracy": float(
						        accuracy_score(UpperCamelCase      ,					UpperCamelCase      ,					normalize=UpperCamelCase      ,					sample_weight=UpperCamelCase  )  )
						}