


"""simple docstring"""




import argparse
import os

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,
# specifically showcasing the experiment tracking capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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     lowercase			(SCREAMING_SNAKE_CASE_		:				Accelerator ,			SCREAMING_SNAKE_CASE_		:				int = 16  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					AutoTokenizer.from_pretrained('bert-base-cased'  )
						SCREAMING_SNAKE_CASE													=					load_dataset('glue' ,			'mrpc'  )

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

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

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

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE													=					DataLoader(
						    tokenized_datasets['train'] ,			shuffle=SCREAMING_SNAKE_CASE_ ,			collate_fn=SCREAMING_SNAKE_CASE_ ,			batch_size=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					DataLoader(
						    tokenized_datasets['validation'] ,			shuffle=SCREAMING_SNAKE_CASE_ ,			collate_fn=SCREAMING_SNAKE_CASE_ ,			batch_size=SCREAMING_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

	__UpperCamelCase						=      mocked_dataloaders  # noqa: F811


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						List[Any]:
						# For testing only
						if os.environ.get('TESTING_MOCKED_DATALOADERS' ,			SCREAMING_SNAKE_CASE_  ) == "1":
												SCREAMING_SNAKE_CASE													=					2
						# Initialize Accelerator

						# New Code #
						# We pass in "all" to `log_with` to grab all available trackers in the environment
						# Note: If using a custom `Tracker` class, should be passed in here such as:
						# >>> log_with = ["all", MyCustomTrackerClassInstance()]
						if args.with_tracking:
												SCREAMING_SNAKE_CASE													=					Accelerator(
												    cpu=args.cpu ,			mixed_precision=args.mixed_precision ,			log_with='all' ,			project_dir=args.project_dir  )
						else:
												SCREAMING_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
						SCREAMING_SNAKE_CASE													=					config['lr']
						SCREAMING_SNAKE_CASE													=					int(config['num_epochs']  )
						SCREAMING_SNAKE_CASE													=					int(config['seed']  )
						SCREAMING_SNAKE_CASE													=					int(config['batch_size']  )
						set_seed(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					get_dataloaders(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					evaluate.load('glue' ,			'mrpc'  )

						# If the batch size is too big we use gradient accumulation
						SCREAMING_SNAKE_CASE													=					1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												SCREAMING_SNAKE_CASE													=					batch_size // MAX_GPU_BATCH_SIZE
												SCREAMING_SNAKE_CASE													=					MAX_GPU_BATCH_SIZE

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

						# Instantiate optimizer
						SCREAMING_SNAKE_CASE													=					AdamW(params=model.parameters() ,			lr=SCREAMING_SNAKE_CASE_  )

						# Instantiate scheduler
						SCREAMING_SNAKE_CASE													=					get_linear_schedule_with_warmup(
						    optimizer=SCREAMING_SNAKE_CASE_ ,			num_warmup_steps=1_00 ,			num_training_steps=(len(SCREAMING_SNAKE_CASE_  ) * 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.
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					accelerator.prepare(
						    SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )

						# New Code #
						# We need to initialize the trackers we use. Overall configurations can also be stored
						if args.with_tracking:
												SCREAMING_SNAKE_CASE													=					os.path.split(SCREAMING_SNAKE_CASE_  )[-1].split('.'  )[0]
												accelerator.init_trackers(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )

						# Now we train the model
						for epoch in range(SCREAMING_SNAKE_CASE_  ):
												model.train()
												# New Code #
												# For our tracking example, we will log the total loss of each epoch
												if args.with_tracking:
																		SCREAMING_SNAKE_CASE													=					0
												for step, batch in enumerate(SCREAMING_SNAKE_CASE_  ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device  )
																		SCREAMING_SNAKE_CASE													=					model(**SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					outputs.loss
																		# New Code #
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		SCREAMING_SNAKE_CASE													=					loss / gradient_accumulation_steps
																		accelerator.backward(SCREAMING_SNAKE_CASE_  )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

												SCREAMING_SNAKE_CASE													=					metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'epoch {epoch}:' ,			SCREAMING_SNAKE_CASE_  )

												# New Code #
												# To actually log, we call `Accelerator.log`
												# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        'accuracy': eval_metric['accuracy'],
																		        'f1': eval_metric['f1'],
																		        'train_loss': total_loss.item() / len(SCREAMING_SNAKE_CASE_  ),
																		        'epoch': epoch,
																		    } ,			step=SCREAMING_SNAKE_CASE_ ,			)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
						if args.with_tracking:
												accelerator.end_training()


def     lowercase			()   ->						List[Any]:
						SCREAMING_SNAKE_CASE													=					argparse.ArgumentParser(description='Simple example of training script.'  )
						parser.add_argument(
						    '--mixed_precision' ,			type=SCREAMING_SNAKE_CASE_ ,			default=SCREAMING_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.'  )
						parser.add_argument(
						    '--with_tracking' ,			action='store_true' ,			help='Whether to load in all available experiment trackers from the environment and use them for logging.' ,			)
						parser.add_argument(
						    '--project_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			default='logs' ,			help='Location on where to store experiment tracking logs` and relevent project information' ,			)
						SCREAMING_SNAKE_CASE													=					parser.parse_args()
						SCREAMING_SNAKE_CASE													=					{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
	main()






"""simple docstring"""




def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						str:
						SCREAMING_SNAKE_CASE													=					int(SCREAMING_SNAKE_CASE_  )
						if decimal in (0, 1):  # Exit cases for the recursion
												return str(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					divmod(SCREAMING_SNAKE_CASE_ ,			2  )
						return binary_recursive(SCREAMING_SNAKE_CASE_  ) + str(SCREAMING_SNAKE_CASE_  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str  )   ->						str:
						SCREAMING_SNAKE_CASE													=					str(SCREAMING_SNAKE_CASE_  ).strip()
						if not number:
												raise ValueError('No input value was provided'  )
						SCREAMING_SNAKE_CASE													=					'-' if number.startswith('-'  ) else ''
						SCREAMING_SNAKE_CASE													=					number.lstrip('-'  )
						if not number.isnumeric():
												raise ValueError('Input value is not an integer'  )
						return F'{negative}0b{binary_recursive(int(SCREAMING_SNAKE_CASE_  )  )}'


if __name__ == "__main__":
	from doctest import testmod

	testmod()


