



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


_lowerCamelCase    :		Any		      =    16
_lowerCamelCase    :		List[Any]		      =    32

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase = 16   )     ->     Union[str, Any]:
					SCREAMING_SNAKE_CASE							:     int						=				AutoTokenizer.from_pretrained('bert-base-cased'   )
					SCREAMING_SNAKE_CASE							:     List[Any]						=				load_dataset('glue'      ,	'mrpc'   )

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

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

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

					# Instantiate dataloaders.
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				DataLoader(
					    tokenized_datasets['train']      ,	shuffle=__lowerCAmelCase      ,	collate_fn=__lowerCAmelCase      ,	batch_size=__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				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    :		int		      =    mocked_dataloaders  # noqa: F811

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     List[Any]:
					# For testing only
					if os.environ.get('TESTING_MOCKED_DATALOADERS'      ,	__lowerCAmelCase   ) == "1":
										SCREAMING_SNAKE_CASE							:     int						=				2
					# Initialize accelerator
					SCREAMING_SNAKE_CASE							:     Dict						=				Accelerator(cpu=args.cpu      ,	mixed_precision=args.mixed_precision   )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					SCREAMING_SNAKE_CASE							:     List[str]						=				config['lr']
					SCREAMING_SNAKE_CASE							:     List[Any]						=				int(config['num_epochs']   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				int(config['seed']   )
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				int(config['batch_size']   )

					SCREAMING_SNAKE_CASE							:     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=__lowerCAmelCase   )
					def inner_training_loop(__lowerCAmelCase   ):
										# 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(__lowerCAmelCase   )

										# Instantiate the model (we build the model here so that the seed also control new weights initialization)
										SCREAMING_SNAKE_CASE							:     Dict						=				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).
										SCREAMING_SNAKE_CASE							:     List[str]						=				model.to(accelerator.device   )

										# Instantiate optimizer
										SCREAMING_SNAKE_CASE							:     Dict						=				AdamW(params=model.parameters()      ,	lr=__lowerCAmelCase   )
										SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[Any]						=				get_dataloaders(__lowerCAmelCase      ,	__lowerCAmelCase   )

										# Instantiate scheduler
										SCREAMING_SNAKE_CASE							:     Tuple						=				get_linear_schedule_with_warmup(
										    optimizer=__lowerCAmelCase      ,	num_warmup_steps=100      ,	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.
										SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     int						=				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   )
																				SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				model(**__lowerCAmelCase   )
																				SCREAMING_SNAKE_CASE							:     Dict						=				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():
																									SCREAMING_SNAKE_CASE							:     Any						=				model(**__lowerCAmelCase   )
																				SCREAMING_SNAKE_CASE							:     List[str]						=				outputs.logits.argmax(dim=-1   )
																				SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				accelerator.gather_for_metrics((predictions, batch['labels'])   )
																				metric.add_batch(
																				    predictions=__lowerCAmelCase      ,	references=__lowerCAmelCase      ,	)

															SCREAMING_SNAKE_CASE							:     Tuple						=				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        __a			(   )     ->     Tuple:
					SCREAMING_SNAKE_CASE							:     Union[str, 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.'   )
					SCREAMING_SNAKE_CASE							:     str						=				parser.parse_args()
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					training_function(__lowerCAmelCase      ,	__lowerCAmelCase   )


if __name__ == "__main__":
			main()




def        __a			(   __lowerCAmelCase   )     ->     List[str]:
					stooge(__lowerCAmelCase      ,	0      ,	len(__lowerCAmelCase   ) - 1   )
					return arr

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     int:
					if i >= h:
										return

					# If first element is smaller than the last then swap them
					if arr[i] > arr[h]:
										SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				arr[h], arr[i]

					# If there are more than 2 elements in the array
					if h - i + 1 > 2:
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				(int)((h - i + 1) / 3   )

										# Recursively sort first 2/3 elements
										stooge(__lowerCAmelCase      ,	__lowerCAmelCase      ,	(h - t)   )

										# Recursively sort last 2/3 elements
										stooge(__lowerCAmelCase      ,	i + t      ,	(__lowerCAmelCase)   )

										# Recursively sort first 2/3 elements
										stooge(__lowerCAmelCase      ,	__lowerCAmelCase      ,	(h - t)   )


if __name__ == "__main__":
			_lowerCamelCase    :		List[str]		      =    input("""Enter numbers separated by a comma:\n""").strip()
			_lowerCamelCase    :		List[Any]		      =    [int(item) for item in user_input.split(""",""")]
			print(stooge_sort(unsorted))