



"""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
#
########################################################################

lowerCAmelCase__		:					Optional[int]							=16
lowerCAmelCase__		:					Any							=32






def      __lowercase       (			a__							,       a__ = 16       )    ->			Dict:
							__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained('bert-base-cased'       )
							__SCREAMING_SNAKE_CASE									= load_dataset('glue'							,       'mrpc'       )

							def tokenize_function(a__       ):
														# max_length=None => use the model max length (it's actually the default)
														__SCREAMING_SNAKE_CASE									= 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():
														__SCREAMING_SNAKE_CASE									= 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
							__SCREAMING_SNAKE_CASE									= tokenized_datasets.rename_column('label'							,       'labels'       )

							def collate_fn(a__       ):
														# 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(
														    a__							,       padding='longest'							,       max_length=a__							,       pad_to_multiple_of=a__							,       return_tensors='pt'							,       )

							# Instantiate dataloaders.
							__SCREAMING_SNAKE_CASE									= DataLoader(
							    tokenized_datasets['train']							,       shuffle=a__							,       collate_fn=a__							,       batch_size=a__       )
							__SCREAMING_SNAKE_CASE									= 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

			lowerCAmelCase__		:					Any							=mocked_dataloaders  # noqa: F811






def      __lowercase       (			a__							,       a__       )    ->			Optional[int]:
							# For testing only
							if os.environ.get('TESTING_MOCKED_DATALOADERS'							,       a__       ) == "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(a__       )

							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= get_dataloaders(a__							,       a__       )
							__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=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).
							__SCREAMING_SNAKE_CASE									= model.to(accelerator.device       )

							# Instantiate optimizer
							__SCREAMING_SNAKE_CASE									= AdamW(params=model.parameters()							,       lr=a__       )

							# Instantiate scheduler
							__SCREAMING_SNAKE_CASE									= get_linear_schedule_with_warmup(
							    optimizer=a__							,       num_warmup_steps=1_00							,       num_training_steps=(len(a__       ) * 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(
							    a__							,       a__							,       a__							,       a__							,       a__       )

							# 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(a__       )[-1].split('.'       )[0]
														accelerator.init_trackers(a__							,       a__       )

							# Now we train the model
							for epoch in range(a__       ):
														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(a__       ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device       )
																					__SCREAMING_SNAKE_CASE									= model(**a__       )
																					__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(a__       )
																					if step % gradient_accumulation_steps == 0:
																												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` (the default).
																					batch.to(accelerator.device       )
																					with torch.no_grad():
																												__SCREAMING_SNAKE_CASE									= model(**a__       )
																					__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=a__							,       references=a__							,       )

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

														# 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(a__       ),
																					        'epoch': epoch,
																					    }							,       step=a__							,       )

    # 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       (			)    ->			Optional[int]:
							__SCREAMING_SNAKE_CASE									= 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.'       )
							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=a__							,       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(a__							,       a__       )


if __name__ == "__main__":
			main()











def      __lowercase       (			a__ = 10_00       )    ->			int:
							__SCREAMING_SNAKE_CASE									= -1
							__SCREAMING_SNAKE_CASE									= 0
							for a in range(1							,       n // 3       ):
														# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
														__SCREAMING_SNAKE_CASE									= (n * n - 2 * a * n) // (2 * n - 2 * a)
														__SCREAMING_SNAKE_CASE									= n - a - b
														if c * c == (a * a + b * b):
																					__SCREAMING_SNAKE_CASE									= a * b * c
																					if candidate >= product:
																												__SCREAMING_SNAKE_CASE									= candidate
							return product


if __name__ == "__main__":
			print(F'''{solution() = }''')






