




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    (							lowercase			: Dict  ):


      '''simple docstring'''



      lowerCamelCase_	  =					fname.split(os.path.sep  )[-1]
      return re.search(r'^(.*)_\d+\.jpg$'			,							lowercase  ).groups()[0]







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def __init__(       self      :						int		,							A_      :						List[Any]		,							A_      :						Union[str, Any]=None		,							A_      :						str=None						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					file_names
       lowerCamelCase_	  =					image_transform
       lowerCamelCase_	  =					label_to_id

 def __len__(       self      :						List[str]						)     ->			Optional[int]:

       """simple docstring"""






       return len(self.file_names						)





 def __getitem__(       self      :						int		,							A_      :						Union[str, Any]						)     ->			List[str]:

       """simple docstring"""






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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, Any]			,							lowercase			: List[Any]  ):


      '''simple docstring'''



      if args.with_tracking:
            lowerCamelCase_	  =					Accelerator(
                cpu=args.cpu			,							mixed_precision=args.mixed_precision			,							log_with='all'			,							project_dir=args.project_dir  )
      else:
            lowerCamelCase_	  =					Accelerator(cpu=args.cpu			,							mixed_precision=args.mixed_precision  )

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

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

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

      # Build the label correspondences
      lowerCamelCase_	  =					[extract_label(lowercase  ) for fname in file_names]
      lowerCamelCase_	  =					list(set(lowercase  )  )
      id_to_label.sort()
      lowerCamelCase_	  =					{lbl: i for i, lbl in enumerate(lowercase  )}

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

      # Split our filenames between train and validation
      lowerCamelCase_	  =					np.random.permutation(len(lowercase  )  )
      lowerCamelCase_	  =					int(0.8 * len(lowercase  )  )
      lowerCamelCase_	  =					random_perm[:cut]
      lowerCamelCase_	  =					random_perm[cut:]

      # For training we use a simple RandomResizedCrop
      lowerCamelCase_	  =					Compose([RandomResizedCrop(lowercase			,							scale=(0.5, 1.0)  ), ToTensor()]  )
      lowerCamelCase_	  =					PetsDataset(
          [file_names[i] for i in train_split]			,							image_transform=lowercase			,							label_to_id=lowercase  )

      # For evaluation, we use a deterministic Resize
      lowerCamelCase_	  =					Compose([Resize(lowercase  ), ToTensor()]  )
      lowerCamelCase_	  =					PetsDataset([file_names[i] for i in eval_split]			,							image_transform=lowercase			,							label_to_id=lowercase  )

      # Instantiate dataloaders.
      lowerCamelCase_	  =					DataLoader(lowercase			,							shuffle=lowercase			,							batch_size=lowercase			,							num_workers=4  )
      lowerCamelCase_	  =					DataLoader(lowercase			,							shuffle=lowercase			,							batch_size=lowercase			,							num_workers=4  )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCamelCase_	  =					create_model('resnet50d'			,							pretrained=lowercase			,							num_classes=len(lowercase  )  )

      # 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).
      lowerCamelCase_	  =					model.to(accelerator.device  )

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

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

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

      # Instantiate learning rate scheduler
      lowerCamelCase_	  =					OneCycleLR(optimizer=lowercase			,							max_lr=lowercase			,							epochs=lowercase			,							steps_per_epoch=len(lowercase  )  )

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

            if "epoch" in training_difference:
                  lowerCamelCase_	  =					int(training_difference.replace('epoch_'			,							''  )  ) + 1
                  lowerCamelCase_	  =					None
            else:
                  lowerCamelCase_	  =					int(training_difference.replace('step_'			,							''  )  )
                  lowerCamelCase_	  =					resume_step // len(lowercase  )
                  resume_step -= starting_epoch * len(lowercase  )

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

            lowerCamelCase_	  =					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(lowercase  ),
                          'epoch': epoch,
                      }			,							step=lowercase			,							)
            if checkpointing_steps == "epoch":
                  lowerCamelCase_	  =					f"""epoch_{epoch}"""
                  if args.output_dir is not None:
                        lowerCamelCase_	  =					os.path.join(args.output_dir			,							lowercase  )
                  accelerator.save_state(lowercase  )

      if args.with_tracking:
            accelerator.end_training()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					argparse.ArgumentParser(description='Simple example of training script.'  )
      parser.add_argument('--data_dir'			,							required=lowercase			,							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=lowercase			,							default=lowercase			,							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=lowercase			,							default=lowercase			,							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=lowercase			,							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=lowercase			,							default=lowercase			,							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=lowercase			,							default='logs'			,							help='Location on where to store experiment tracking logs` and relevent project information'			,							)
      lowerCamelCase_	  =					parser.parse_args()
      lowerCamelCase_	  =					{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 2_24}
      training_function(lowercase			,							lowercase  )


if __name__ == "__main__":
 main()









from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass







@dataclass
class 						A:



 '''simple docstring'''


 UpperCamelCase		     =		42
 UpperCamelCase		     =		None
 UpperCamelCase		     =		None

lowerCamelCase       :	str									=     namedtuple("CoinsDistribResult", "moves excess")

def     _SCREAMING_SNAKE_CASE    (							lowercase			: TreeNode | None  ):


      '''simple docstring'''



      if root is None:
            return 0

      # Validation
      def count_nodes(lowercase			: TreeNode | None  ) -> int:
            if node is None:
                  return 0

            return count_nodes(node.left  ) + count_nodes(node.right  ) + 1

      def count_coins(lowercase			: TreeNode | None  ) -> int:
            if node is None:
                  return 0

            return count_coins(node.left  ) + count_coins(node.right  ) + node.data

      if count_nodes(lowercase  ) != count_coins(lowercase  ):
            raise ValueError('The nodes number should be same as the number of coins'  )

      # Main calculation
      def get_distrib(lowercase			: TreeNode | None  ) -> CoinsDistribResult:

            if node is None:
                  return CoinsDistribResult(0			,							1  )

            lowerCamelCase_  ,       lowerCamelCase_	  =					get_distrib(node.left  )
            lowerCamelCase_  ,       lowerCamelCase_	  =					get_distrib(node.right  )

            lowerCamelCase_	  =					1 - left_distrib_excess
            lowerCamelCase_	  =					1 - right_distrib_excess

            lowerCamelCase_	  =					(
                left_distrib_moves
                + right_distrib_moves
                + abs(lowercase  )
                + abs(lowercase  )
            )
            lowerCamelCase_	  =					node.data - coins_to_left - coins_to_right

            return CoinsDistribResult(lowercase			,							lowercase  )

      return get_distrib(lowercase  )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()



