"""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, 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
#
########################################################################


SCREAMING_SNAKE_CASE__					  =     16
SCREAMING_SNAKE_CASE__					  =     32





def       lowerCAmelCase__			(	_UpperCamelCase				: Accelerator       ,	_UpperCamelCase				: int = 1_6    )    -> Tuple:





						"""simple docstring"""






						snake_case	    =	AutoTokenizer.from_pretrained('bert-base-cased'    )
						snake_case	    =	load_dataset('glue'       ,	'mrpc'    )

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

						def collate_fn(_UpperCamelCase				: Tuple    ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												snake_case	    =	1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		snake_case	    =	1_6
												elif accelerator.mixed_precision != "no":
																		snake_case	    =	8
												else:
																		snake_case	    =	None

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

						# Instantiate dataloaders.
						snake_case	    =	DataLoader(
						    tokenized_datasets['train']       ,	shuffle=_snake_case       ,	collate_fn=_snake_case       ,	batch_size=_snake_case    )
						snake_case	    =	DataLoader(
						    tokenized_datasets['validation']       ,	shuffle=_snake_case       ,	collate_fn=_snake_case       ,	batch_size=_snake_case    )

						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__					  =     mocked_dataloaders  # noqa: F811





def       lowerCAmelCase__			(	_UpperCamelCase				: List[str]       ,	_UpperCamelCase				: List[str]    )    -> Union[str, Any]:





						"""simple docstring"""






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

						snake_case	    =	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=_snake_case    )
						def inner_training_loop(_UpperCamelCase				: int    ):
												# 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(_snake_case    )

												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												snake_case	    =	AutoModelForSequenceClassification.from_pretrained('bert-base-cased'       ,	return_dict=_snake_case    )

												# 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).
												snake_case	    =	model.to(accelerator.device    )

												# Instantiate optimizer
												snake_case	    =	AdamW(params=model.parameters()       ,	lr=_snake_case    )
												snake_case	    =	get_dataloaders(_snake_case       ,	_snake_case    )

												# Instantiate scheduler
												snake_case	    =	get_linear_schedule_with_warmup(
												    optimizer=_snake_case       ,	num_warmup_steps=1_0_0       ,	num_training_steps=(len(_snake_case    ) * 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.
												snake_case	    =	accelerator.prepare(
												    _snake_case       ,	_snake_case       ,	_snake_case       ,	_snake_case       ,	_snake_case    )

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

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

																		snake_case	    =	metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(f"""epoch {epoch}:"""       ,	_snake_case    )

    # 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__			(	)    -> List[Any]:





						"""simple docstring"""






						snake_case	    =	argparse.ArgumentParser(description='Simple example of training script.'    )
						parser.add_argument(
						    '--mixed_precision'       ,	type=_snake_case       ,	default=_snake_case       ,	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.'    )
						snake_case	    =	parser.parse_args()
						snake_case	    =	{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
						training_function(_snake_case       ,	_snake_case    )


if __name__ == "__main__":
					main()




def   lowerCAmelCase_						(					_snake_case      :  list[list[int | float]]   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Any	   =     len(_snake_case   )
	__magic_name__		:		Optional[Any]	   =     len(matrix[0]   )
	__magic_name__		:		Union[str, Any]	   =     min(_snake_case      ,   _snake_case   )

	for row in range(_snake_case   ):
		# Check if diagonal element is not zero
		if matrix[row][row] != 0:
			# Eliminate all the elements below the diagonal
			for col in range(row + 1      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     matrix[col][row] / matrix[row][row]
				for i in range(_snake_case      ,   _snake_case   ):
					matrix[col][i] -= multiplier * matrix[row][i]
		else:
			# Find a non-zero diagonal element to swap rows
			__magic_name__		:		str	   =     True
			for i in range(row + 1      ,   _snake_case   ):
				if matrix[i][row] != 0:
					__magic_name__   ,    __magic_name__		:		List[str]	   =     matrix[i], matrix[row]
					__magic_name__		:		Union[str, Any]	   =     False
					break
			if reduce:
				rank -= 1
				for i in range(_snake_case   ):
					__magic_name__		:		Any	   =     matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
			row -= 1

	return rank


if __name__ == "__main__":
				import doctest

				doctest.testmod()
