

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



class 							_UpperCAmelCase			( __lowercase      ):
  '''simple docstring'''







  def __init__(			self						:			Optional[Any]		,    UpperCamelCase__						:			Optional[Any]		,    UpperCamelCase__						:			str=None		,    UpperCamelCase__						:			Dict=None			):
        A 			=					file_names
        A 			=					image_transform
        A 			=					label_to_id







  def __len__(			self						:			List[Any]			):
        return len(self.file_names			)







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


def       __UpperCamelCase     (lowerCAmelCase       :	Dict,     lowerCAmelCase       :	Union[str, Any]  )  ->	Union[str, Any]:
      # Initialize accelerator
      if args.with_tracking:
            A 			=					Accelerator(
                cpu=args.cpu,     mixed_precision=args.mixed_precision,     log_with='all',     project_dir=args.project_dir  )
      else:
            A 			=					Accelerator(cpu=args.cpu,     mixed_precision=args.mixed_precision  )

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

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

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

      # Build the label correspondences
      A 			=					[extract_label(lowercase__  ) for fname in file_names]
      A 			=					list(set(lowercase__  )  )
      id_to_label.sort()
      A 			=					{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
      A 			=					np.random.permutation(len(lowercase__  )  )
      A 			=					int(0.8 * len(lowercase__  )  )
      A 			=					random_perm[:cut]
      A 			=					random_perm[cut:]

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

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

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

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

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

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

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

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

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

      if args.with_tracking:
            accelerator.end_training()


def       __UpperCamelCase     ()  ->	Any:
      A 			=					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',     )
      A 			=					parser.parse_args()
      A 			=					{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
      training_function(lowercase__,     lowercase__  )


if __name__ == "__main__":
  main()






"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						list:
 lowerCAmelCase__   :				List[str] 			=				len(lowercase__   )
 lowerCAmelCase__   :				Dict 			=				[]
 for i in range(len(lowercase__   ) - pat_len + 1   ):
  lowerCAmelCase__   :				Union[str, Any] 			=				True
  for j in range(lowercase__   ):
   if s[i + j] != pattern[j]:
    lowerCAmelCase__   :				int 			=				False
    break
  if match_found:
   position.append(lowercase__   )
 return position


if __name__ == "__main__":
    assert naive_pattern_search("""ABCDEFG""", """DE""") == [3]
    print(naive_pattern_search("""ABAAABCDBBABCDDEBCABC""", """ABC"""))






