




'''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 			SCREAMING_SNAKE_CASE_			(							__A    :			List[str]  )			->			List[str]:
   _SCREAMING_SNAKE_CASE	=				fname.split(os.path.sep  )[-1]
   return re.search(R"^(.*)_\d+\.jpg$"	,					__A  ).groups()[0]







class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     int  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     int=None  ,				__lowerCamelCase :     List[Any]=None   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				file_names
      _SCREAMING_SNAKE_CASE	=				image_transform
      _SCREAMING_SNAKE_CASE	=				label_to_id



   def __len__(   self :     str   ):

      """simple docstring"""
      return len(self.file_names   )



   def __getitem__(   self :     List[str]  ,				__lowerCamelCase :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.file_names[idx]
      _SCREAMING_SNAKE_CASE	=				PIL.Image.open(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				raw_image.convert("RGB"   )
      if self.image_transform is not None:
         _SCREAMING_SNAKE_CASE	=				self.image_transform(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				extract_label(__lowerCamelCase   )
      if self.label_to_id is not None:
         _SCREAMING_SNAKE_CASE	=				self.label_to_id[label]
      return {"image": image, "label": label}


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			Optional[Any]  )			->			Union[str, Any]:
   # Initialize accelerator
   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"]  )
   _SCREAMING_SNAKE_CASE	=				config["image_size"]
   if not isinstance(__A	,					(list, tuple)  ):
      _SCREAMING_SNAKE_CASE	=				(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	=				args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         _SCREAMING_SNAKE_CASE	=				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	=				None

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

   # Grab all the image filenames
   _SCREAMING_SNAKE_CASE	=				[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	=				[extract_label(__A  ) for fname in file_names]
   _SCREAMING_SNAKE_CASE	=				list(set(__A  )  )
   id_to_label.sort()
   _SCREAMING_SNAKE_CASE	=				{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	=				np.random.permutation(len(__A  )  )
   _SCREAMING_SNAKE_CASE	=				int(0.8 * len(__A  )  )
   _SCREAMING_SNAKE_CASE	=				random_perm[:cut]
   _SCREAMING_SNAKE_CASE	=				random_perm[cut:]

   # For training we use a simple RandomResizedCrop
   _SCREAMING_SNAKE_CASE	=				Compose([RandomResizedCrop(__A	,					scale=(0.5, 1.0)  ), ToTensor()]  )
   _SCREAMING_SNAKE_CASE	=				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	=				Compose([Resize(__A  ), ToTensor()]  )
   _SCREAMING_SNAKE_CASE	=				PetsDataset([file_names[i] for i in eval_split]	,					image_transform=__A	,					label_to_id=__A  )

   # Instantiate dataloaders.
   _SCREAMING_SNAKE_CASE	=				DataLoader(__A	,					shuffle=__A	,					batch_size=__A	,					num_workers=4  )
   _SCREAMING_SNAKE_CASE	=				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	=				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	=				model.to(accelerator.device  )

   # Freezing the base model
   for param in model.parameters():
      _SCREAMING_SNAKE_CASE	=				False
   for param in model.get_classifier().parameters():
      _SCREAMING_SNAKE_CASE	=				True

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

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

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

      if "epoch" in training_difference:
         _SCREAMING_SNAKE_CASE	=				int(training_difference.replace("epoch_"	,					""  )  ) + 1
         _SCREAMING_SNAKE_CASE	=				None
      else:
         _SCREAMING_SNAKE_CASE	=				int(training_difference.replace("step_"	,					""  )  )
         _SCREAMING_SNAKE_CASE	=				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	=				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	=				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	=				train_dataloader
      for batch in active_dataloader:
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         _SCREAMING_SNAKE_CASE	=				{k: v.to(accelerator.device  ) for k, v in batch.items()}
         _SCREAMING_SNAKE_CASE	=				(batch["image"] - mean) / std
         _SCREAMING_SNAKE_CASE	=				model(__A  )
         _SCREAMING_SNAKE_CASE	=				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	=				f"""step_{overall_step}"""
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  _SCREAMING_SNAKE_CASE	=				os.path.join(args.output_dir	,					__A  )
               accelerator.save_state(__A  )
      model.eval()
      _SCREAMING_SNAKE_CASE	=				0
      _SCREAMING_SNAKE_CASE	=				0
      for step, batch in enumerate(__A  ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         _SCREAMING_SNAKE_CASE	=				{k: v.to(accelerator.device  ) for k, v in batch.items()}
         _SCREAMING_SNAKE_CASE	=				(batch["image"] - mean) / std
         with torch.no_grad():
            _SCREAMING_SNAKE_CASE	=				model(__A  )
         _SCREAMING_SNAKE_CASE	=				outputs.argmax(dim=-1  )
         _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				accelerator.gather_for_metrics((predictions, batch["label"])  )
         _SCREAMING_SNAKE_CASE	=				predictions == references
         num_elems += accurate_preds.shape[0]
         accurate += accurate_preds.long().sum()

      _SCREAMING_SNAKE_CASE	=				accurate.item() / num_elems
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"""epoch {epoch}: {1_00 * eval_metric:.2f}"""  )
      if args.with_tracking:
         accelerator.log(
             {
                 "accuracy": 1_00 * eval_metric,
                 "train_loss": total_loss.item() / len(__A  ),
                 "epoch": epoch,
             }	,					step=__A	,					)
      if checkpointing_steps == "epoch":
         _SCREAMING_SNAKE_CASE	=				f"""epoch_{epoch}"""
         if args.output_dir is not None:
            _SCREAMING_SNAKE_CASE	=				os.path.join(args.output_dir	,					__A  )
         accelerator.save_state(__A  )

   if args.with_tracking:
      accelerator.end_training()
def 			SCREAMING_SNAKE_CASE_			(							)			->			Dict:
   _SCREAMING_SNAKE_CASE	=				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	=				parser.parse_args()
   _SCREAMING_SNAKE_CASE	=				{"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 2_24}
   training_function(__A	,					__A  )


if __name__ == "__main__":
 main()







'''simple docstring'''



from collections.abc import Sequence
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Sequence[int] | None = None  )			->			int:
   if nums is None or not nums:
      raise ValueError("Input sequence should not be empty"  )

   _SCREAMING_SNAKE_CASE	=				nums[0]
   for i in range(1	,					len(__A  )  ):
      _SCREAMING_SNAKE_CASE	=				nums[i]
      _SCREAMING_SNAKE_CASE	=				max(__A	,					ans + num	,					__A  )

   return ans


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Try on a sample input from the user
 lowerCamelCase_			=						int(input('Enter number of elements : ').strip())
 lowerCamelCase_			=						list(map(int, input('\nEnter the numbers : ').strip().split()))[:n]
 print(max_subsequence_sum(array))

