





import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Dict           =   fname.split(os.path.sep					)[-1]
						return re.search(R'''^(.*)_\d+\.jpg$'''       ,			A__					).groups()[0]





class 				a			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase=None			, _lowerCAmelCase=None					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   file_names
										__SCREAMING_SNAKE_CASE:				Any           =   image_transform
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   label_to_id






				def __len__(  self					):

										"""simple docstring"""




										return len(self.file_names					)






				def __getitem__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   self.file_names[idx]
										__SCREAMING_SNAKE_CASE:				List[Any]           =   PIL.Image.open(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   raw_image.convert('''RGB'''					)
										if self.image_transform is not None:
																__SCREAMING_SNAKE_CASE:				Any           =   self.image_transform(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   extract_label(_lowerCAmelCase					)
										if self.label_to_id is not None:
																__SCREAMING_SNAKE_CASE:				Any           =   self.label_to_id[label]
										return {"image": image, "label": label}






def 		lowerCAmelCase  (	UpperCamelCase__   :   int       ,			UpperCamelCase__   :   Tuple					)      ->    Tuple:

						"""simple docstring"""





						if args.with_tracking:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   Accelerator(
												    cpu=args.cpu       ,			mixed_precision=args.mixed_precision       ,			log_with='''all'''       ,			project_dir=args.project_dir					)
						else:
												__SCREAMING_SNAKE_CASE:				str           =   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:				Union[str, Any]           =   config['''lr''']
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   int(config['''num_epochs''']					)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   int(config['''seed''']					)
						__SCREAMING_SNAKE_CASE:				Dict           =   int(config['''batch_size''']					)
						__SCREAMING_SNAKE_CASE:				int           =   config['''image_size''']
						if not isinstance(A__       ,			(list, tuple)					):
												__SCREAMING_SNAKE_CASE:				int           =   (image_size, image_size)

						# Parse out whether we are saving every epoch or after a certain number of batches
						if hasattr(args.checkpointing_steps       ,			'''isdigit'''					):
												if args.checkpointing_steps == "epoch":
																		__SCREAMING_SNAKE_CASE:				Optional[Any]           =   args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   int(args.checkpointing_steps					)
												else:
																		raise ValueError(
																		    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""					)
						else:
												__SCREAMING_SNAKE_CASE:				List[str]           =   None

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												__SCREAMING_SNAKE_CASE:				Tuple           =   os.path.split(A__					)[-1].split('''.'''					)[0]
												accelerator.init_trackers(A__       ,			A__					)

						# Grab all the image filenames
						__SCREAMING_SNAKE_CASE:				str           =   [os.path.join(args.data_dir       ,			A__					) for fname in os.listdir(args.data_dir					) if fname.endswith('''.jpg'''					)]

						# Build the label correspondences
						__SCREAMING_SNAKE_CASE:				List[Any]           =   [extract_label(A__					) for fname in file_names]
						__SCREAMING_SNAKE_CASE:				Any           =   list(set(A__					)					)
						id_to_label.sort()
						__SCREAMING_SNAKE_CASE:				Tuple           =   {lbl: i for i, lbl in enumerate(A__					)}

						# Set the seed before splitting the data.
						np.random.seed(A__					)
						torch.manual_seed(A__					)
						torch.cuda.manual_seed_all(A__					)

						# Split our filenames between train and validation
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   np.random.permutation(len(A__					)					)
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   int(0.8 * len(A__					)					)
						__SCREAMING_SNAKE_CASE:				str           =   random_perm[:cut]
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   random_perm[cut:]

						# For training we use a simple RandomResizedCrop
						__SCREAMING_SNAKE_CASE:				str           =   Compose([RandomResizedCrop(A__       ,			scale=(0.5, 1.0)					), ToTensor()]					)
						__SCREAMING_SNAKE_CASE:				int           =   PetsDataset(
						    [file_names[i] for i in train_split]       ,			image_transform=A__       ,			label_to_id=A__					)

						# For evaluation, we use a deterministic Resize
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   Compose([Resize(A__					), ToTensor()]					)
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   PetsDataset([file_names[i] for i in eval_split]       ,			image_transform=A__       ,			label_to_id=A__					)

						# Instantiate dataloaders.
						__SCREAMING_SNAKE_CASE:				Any           =   DataLoader(A__       ,			shuffle=A__       ,			batch_size=A__       ,			num_workers=4					)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   DataLoader(A__       ,			shuffle=A__       ,			batch_size=A__       ,			num_workers=4					)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   create_model('''resnet50d'''       ,			pretrained=A__       ,			num_classes=len(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:				Dict           =   model.to(accelerator.device					)

						# Freezing the base model
						for param in model.parameters():
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   False
						for param in model.get_classifier().parameters():
												__SCREAMING_SNAKE_CASE:				Dict           =   True

						# We normalize the batches of images to be a bit faster.
						__SCREAMING_SNAKE_CASE:				Any           =   torch.tensor(model.default_cfg['''mean''']					)[None, :, None, None].to(accelerator.device					)
						__SCREAMING_SNAKE_CASE:				Tuple           =   torch.tensor(model.default_cfg['''std''']					)[None, :, None, None].to(accelerator.device					)

						# Instantiate optimizer
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   torch.optim.Adam(params=model.parameters()       ,			lr=lr / 25					)

						# Instantiate learning rate scheduler
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   OneCycleLR(optimizer=A__       ,			max_lr=A__       ,			epochs=A__       ,			steps_per_epoch=len(A__					)					)

						# 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:				Optional[Any]           =   accelerator.prepare(
						    A__       ,			A__       ,			A__       ,			A__       ,			A__					)
						# We need to keep track of how many total steps we have iterated over
						__SCREAMING_SNAKE_CASE:				List[str]           =   0
						# We also need to keep track of the starting epoch so files are named properly
						__SCREAMING_SNAKE_CASE:				int           =   0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""					)
																		accelerator.load_state(args.resume_from_checkpoint					)
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   os.path.basename(args.resume_from_checkpoint					)
												else:
																		# Get the most recent checkpoint
																		__SCREAMING_SNAKE_CASE:				Tuple           =   [f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
																		dirs.sort(key=os.path.getctime					)
																		__SCREAMING_SNAKE_CASE:				int           =   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												__SCREAMING_SNAKE_CASE:				Dict           =   os.path.splitext(A__					)[0]

												if "epoch" in training_difference:
																		__SCREAMING_SNAKE_CASE:				List[str]           =   int(training_difference.replace('''epoch_'''       ,			''''''					)					) + 1
																		__SCREAMING_SNAKE_CASE:				Any           =   None
												else:
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   int(training_difference.replace('''step_'''       ,			''''''					)					)
																		__SCREAMING_SNAKE_CASE:				List[str]           =   resume_step // len(A__					)
																		resume_step -= starting_epoch * len(A__					)

    # Now we train the model
						for epoch in range(A__       ,			A__					):
												model.train()
												if args.with_tracking:
																		__SCREAMING_SNAKE_CASE:				int           =   0
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		__SCREAMING_SNAKE_CASE:				Dict           =   accelerator.skip_first_batches(A__       ,			A__					)
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		__SCREAMING_SNAKE_CASE:				Optional[Any]           =   train_dataloader
												for batch in active_dataloader:
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   {k: v.to(accelerator.device					) for k, v in batch.items()}
																		__SCREAMING_SNAKE_CASE:				Dict           =   (batch['''image'''] - mean) / std
																		__SCREAMING_SNAKE_CASE:				Tuple           =   model(A__					)
																		__SCREAMING_SNAKE_CASE:				str           =   torch.nn.functional.cross_entropy(A__       ,			batch['''label''']					)
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(A__					)
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()
																		overall_step += 1
																		if isinstance(A__       ,			A__					):
																								__SCREAMING_SNAKE_CASE:				List[Any]           =   F"""step_{overall_step}"""
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				__SCREAMING_SNAKE_CASE:				Any           =   os.path.join(args.output_dir       ,			A__					)
																														accelerator.save_state(A__					)
												model.eval()
												__SCREAMING_SNAKE_CASE:				Any           =   0
												__SCREAMING_SNAKE_CASE:				List[str]           =   0
												for step, batch in enumerate(A__					):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		__SCREAMING_SNAKE_CASE:				List[str]           =   {k: v.to(accelerator.device					) for k, v in batch.items()}
																		__SCREAMING_SNAKE_CASE:				Dict           =   (batch['''image'''] - mean) / std
																		with torch.no_grad():
																								__SCREAMING_SNAKE_CASE:				Any           =   model(A__					)
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   outputs.argmax(dim=-1					)
																		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Tuple           =   accelerator.gather_for_metrics((predictions, batch['''label'''])					)
																		__SCREAMING_SNAKE_CASE:				Any           =   predictions == references
																		num_elems += accurate_preds.shape[0]
																		accurate += accurate_preds.long().sum()

												__SCREAMING_SNAKE_CASE:				Any           =   accurate.item() / num_elems
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""					)
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''accuracy''': 100 * eval_metric,
																		        '''train_loss''': total_loss.item() / len(A__					),
																		        '''epoch''': epoch,
																		    }       ,			step=A__       ,			)
												if checkpointing_steps == "epoch":
																		__SCREAMING_SNAKE_CASE:				List[Any]           =   F"""epoch_{epoch}"""
																		if args.output_dir is not None:
																								__SCREAMING_SNAKE_CASE:				Dict           =   os.path.join(args.output_dir       ,			A__					)
																		accelerator.save_state(A__					)

						if args.with_tracking:
												accelerator.end_training()






def 		lowerCAmelCase  (	)      ->    List[str]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   argparse.ArgumentParser(description='''Simple example of training script.'''					)
						parser.add_argument('''--data_dir'''       ,			required=A__       ,			help='''The data folder on disk.'''					)
						parser.add_argument('''--fp16'''       ,			action='''store_true'''       ,			help='''If passed, will use FP16 training.'''					)
						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(
						    '''--checkpointing_steps'''       ,			type=A__       ,			default=A__       ,			help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''       ,			)
						parser.add_argument(
						    '''--output_dir'''       ,			type=A__       ,			default='''.'''       ,			help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''       ,			)
						parser.add_argument(
						    '''--resume_from_checkpoint'''       ,			type=A__       ,			default=A__       ,			help='''If the training should continue from a checkpoint folder.'''       ,			)
						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:				str           =   parser.parse_args()
						__SCREAMING_SNAKE_CASE:				List[str]           =   {'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
						training_function(A__       ,			A__					)


if __name__ == "__main__":
							main()






import sys

lowercase_   							=  (
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)
def 	a (     A__      :  str = N	)       -> int:





      """simple docstring"""



      _lowercase      			=-sys.maxsize - 1
      for i in range(len(A__	) - 12	):
            _lowercase      			=1
            for j in range(13	):
                  product *= int(n[i + j]	)
            if product > largest_product:
                  _lowercase      			=product
      return largest_product


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


