'''simple docstring'''




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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
					lowercase_					:       Optional[Any]     				=					fname.split(os.path.sep							)[-1]
					return re.search(r'^(.*)_\d+\.jpg$'  ,       SCREAMING_SNAKE_CASE_							).groups()[0]


class       UpperCAmelCase__  (       _snake_case			):






			"""simple docstring"""
			def __init__(self       ,				_a       ,				_a=None       ,				_a=None				)    ->     List[str]:
								lowercase_					:       Any     				=					file_names
								lowercase_					:       int     				=					image_transform
								lowercase_					:       Optional[Any]     				=					label_to_id
			def __len__(self				)    ->     List[str]:
								return len(self.file_names				)





			def __getitem__(self       ,				_a				)    ->     int:
								lowercase_					:       List[str]     				=					self.file_names[idx]
								lowercase_					:       Optional[Any]     				=					PIL.Image.open(_a				)
								lowercase_					:       Dict     				=					raw_image.convert('RGB'				)
								if self.image_transform is not None:
													lowercase_					:       List[str]     				=					self.image_transform(_a				)
								lowercase_					:       List[Any]     				=					extract_label(_a				)
								if self.label_to_id is not None:
													lowercase_					:       Any     				=					self.label_to_id[label]
								return {"image": image, "label": label}



def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
					# Initialize accelerator
					if args.with_tracking:
										lowercase_					:       List[str]     				=					Accelerator(
										    cpu=args.cpu  ,       mixed_precision=args.mixed_precision  ,       log_with='all'  ,       project_dir=args.project_dir							)
					else:
										lowercase_					:       Union[str, Any]     				=					Accelerator(cpu=args.cpu  ,       mixed_precision=args.mixed_precision							)

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					lowercase_					:       List[Any]     				=					config['lr']
					lowercase_					:       str     				=					int(config['num_epochs']							)
					lowercase_					:       Optional[int]     				=					int(config['seed']							)
					lowercase_					:       Tuple     				=					int(config['batch_size']							)
					lowercase_					:       Optional[int]     				=					config['image_size']
					if not isinstance(SCREAMING_SNAKE_CASE_  ,       (list, tuple)							):
										lowercase_					:       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":
															lowercase_					:       str     				=					args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															lowercase_					:       List[Any]     				=					int(args.checkpointing_steps							)
										else:
															raise ValueError(
															    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
					else:
										lowercase_					:       Union[str, Any]     				=					None

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

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

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

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

					# Split our filenames between train and validation
					lowercase_					:       str     				=					np.random.permutation(len(SCREAMING_SNAKE_CASE_							)							)
					lowercase_					:       Optional[int]     				=					int(0.8 * len(SCREAMING_SNAKE_CASE_							)							)
					lowercase_					:       List[Any]     				=					random_perm[:cut]
					lowercase_					:       str     				=					random_perm[cut:]

					# For training we use a simple RandomResizedCrop
					lowercase_					:       List[str]     				=					Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE_  ,       scale=(0.5, 1.0)							), ToTensor()]							)
					lowercase_					:       Optional[Any]     				=					PetsDataset(
					    [file_names[i] for i in train_split]  ,       image_transform=SCREAMING_SNAKE_CASE_  ,       label_to_id=SCREAMING_SNAKE_CASE_							)

					# For evaluation, we use a deterministic Resize
					lowercase_					:       int     				=					Compose([Resize(SCREAMING_SNAKE_CASE_							), ToTensor()]							)
					lowercase_					:       List[str]     				=					PetsDataset([file_names[i] for i in eval_split]  ,       image_transform=SCREAMING_SNAKE_CASE_  ,       label_to_id=SCREAMING_SNAKE_CASE_							)

					# Instantiate dataloaders.
					lowercase_					:       Dict     				=					DataLoader(SCREAMING_SNAKE_CASE_  ,       shuffle=SCREAMING_SNAKE_CASE_  ,       batch_size=SCREAMING_SNAKE_CASE_  ,       num_workers=4							)
					lowercase_					:       List[str]     				=					DataLoader(SCREAMING_SNAKE_CASE_  ,       shuffle=SCREAMING_SNAKE_CASE_  ,       batch_size=SCREAMING_SNAKE_CASE_  ,       num_workers=4							)

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

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

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

					# Instantiate optimizer
					lowercase_					:       str     				=					torch.optim.Adam(params=model.parameters()  ,       lr=lr / 25							)

					# Instantiate learning rate scheduler
					lowercase_					:       Tuple     				=					OneCycleLR(optimizer=SCREAMING_SNAKE_CASE_  ,       max_lr=SCREAMING_SNAKE_CASE_  ,       epochs=SCREAMING_SNAKE_CASE_  ,       steps_per_epoch=len(SCREAMING_SNAKE_CASE_							)							)

					# 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.
					lowercase_					:       int     				=					accelerator.prepare(
					    SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
					# We need to keep track of how many total steps we have iterated over
					lowercase_					:       List[str]     				=					0
					# We also need to keep track of the starting epoch so files are named properly
					lowercase_					:       str     				=					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							)
															lowercase_					:       Any     				=					os.path.basename(args.resume_from_checkpoint							)
										else:
															# Get the most recent checkpoint
															lowercase_					:       Tuple     				=					[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
															dirs.sort(key=os.path.getctime							)
															lowercase_					:       Optional[int]     				=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										lowercase_					:       Optional[Any]     				=					os.path.splitext(SCREAMING_SNAKE_CASE_							)[0]

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

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

										lowercase_					:       Optional[int]     				=					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(SCREAMING_SNAKE_CASE_							),
															        'epoch': epoch,
															    }  ,       step=SCREAMING_SNAKE_CASE_  ,       )
										if checkpointing_steps == "epoch":
															lowercase_					:       List[str]     				=					f'''epoch_{epoch}'''
															if args.output_dir is not None:
																				lowercase_					:       Union[str, Any]     				=					os.path.join(args.output_dir  ,       SCREAMING_SNAKE_CASE_							)
															accelerator.save_state(SCREAMING_SNAKE_CASE_							)

					if args.with_tracking:
										accelerator.end_training()

def _UpperCamelCase   (       ):
					lowercase_					:       Any     				=					argparse.ArgumentParser(description='Simple example of training script.'							)
					parser.add_argument('--data_dir'  ,       required=SCREAMING_SNAKE_CASE_  ,       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=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(
					    '--checkpointing_steps'  ,       type=SCREAMING_SNAKE_CASE_  ,       default=SCREAMING_SNAKE_CASE_  ,       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=SCREAMING_SNAKE_CASE_  ,       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=SCREAMING_SNAKE_CASE_  ,       default=SCREAMING_SNAKE_CASE_  ,       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=SCREAMING_SNAKE_CASE_  ,       default='logs'  ,       help='Location on where to store experiment tracking logs` and relevent project information'  ,       )
					lowercase_					:       Optional[Any]     				=					parser.parse_args()
					lowercase_					:       Union[str, Any]     				=					{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
					training_function(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
	main()





'''simple docstring'''




import os

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					len(grid[0]							)
     lowercase_					:       Union[str, Any]     				=					len(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Union[str, Any]     				=					0
     lowercase_					:       int     				=					0
     lowercase_					:       Union[str, Any]     				=					0

     # Check vertically, horizontally, diagonally at the same time (only works
     # for nxn grid)
     for i in range(SCREAMING_SNAKE_CASE_							):
          for j in range(n_rows - 3							):
               lowercase_					:       Dict     				=					grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
               lowercase_					:       Optional[Any]     				=					grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]

               # Left-to-right diagonal (\) product
               if i < n_columns - 3:
                    lowercase_					:       Tuple     				=					(
                        grid[i][j]
                        * grid[i + 1][j + 1]
                        * grid[i + 2][j + 2]
                        * grid[i + 3][j + 3]
                    )

               # Right-to-left diagonal(/) product
               if i > 2:
                    lowercase_					:       int     				=					(
                        grid[i][j]
                        * grid[i - 1][j + 1]
                        * grid[i - 2][j + 2]
                        * grid[i - 3][j + 3]
                    )

               lowercase_					:       Optional[int]     				=					max(
                   SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
               if max_product > largest:
                    lowercase_					:       List[Any]     				=					max_product

     return largest

def _UpperCamelCase   (       ):
     lowercase_					:       int     				=					[]
     with open(os.path.dirname(SCREAMING_SNAKE_CASE_							) + '/grid.txt'							) as file:
          for line in file:
               grid.append(line.strip('\n'							).split(' '							)							)

     lowercase_					:       Dict     				=					[[int(SCREAMING_SNAKE_CASE_							) for i in grid[j]] for j in range(len(SCREAMING_SNAKE_CASE_							)							)]

     return largest_product(SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
 print(solution())




