

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 _a					(			SCREAMING_SNAKE_CASE						:     Dict						)       ->       Tuple:

 """simple docstring"""






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






class 				A_					(						__lowerCamelCase   ):


     def __init__(				self     :						List[str]     ,    UpperCAmelCase     :						int     ,    UpperCAmelCase     :						Dict=None     ,    UpperCAmelCase     :						Optional[int]=None   )				->      int:
      __lowerCAmelCase:  Dict        = file_names
      __lowerCAmelCase:  Dict        = image_transform
      __lowerCAmelCase:  List[str]        = label_to_id


     def __len__(				self     :						int   )				->      List[str]:
      return len(self.file_names   )


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







def _a					(			SCREAMING_SNAKE_CASE						:     int       ,   SCREAMING_SNAKE_CASE						:     Union[str, Any]						)       ->       List[str]:

 """simple docstring"""






 __lowerCAmelCase:  str        = Accelerator(cpu=args.cpu       ,   mixed_precision=args.mixed_precision						)

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __lowerCAmelCase:  int        = config['lr']
 __lowerCAmelCase:  Optional[int]        = int(config['num_epochs']						)
 __lowerCAmelCase:  List[str]        = int(config['seed']						)
 __lowerCAmelCase:  Any        = int(config['batch_size']						)
 __lowerCAmelCase:  Tuple        = config['image_size']
 if not isinstance(UpperCAmelCase__       ,   (list, tuple)						):
  __lowerCAmelCase:  Any        = (image_size, image_size)

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

 # Build the label correspondences
 __lowerCAmelCase:  int        = [extract_label(UpperCAmelCase__						) for fname in file_names]
 __lowerCAmelCase:  Dict        = list(set(UpperCAmelCase__						)						)
 id_to_label.sort()
 __lowerCAmelCase:  Tuple        = {lbl: i for i, lbl in enumerate(UpperCAmelCase__						)}

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

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

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

 # For evaluation, we use a deterministic Resize
 __lowerCAmelCase:  List[Any]        = Compose([Resize(UpperCAmelCase__						), ToTensor()]						)
 __lowerCAmelCase:  List[Any]        = PetsDataset([file_names[i] for i in eval_split]       ,   image_transform=UpperCAmelCase__       ,   label_to_id=UpperCAmelCase__						)

 # Instantiate dataloaders.
 __lowerCAmelCase:  str        = DataLoader(UpperCAmelCase__       ,   shuffle=UpperCAmelCase__       ,   batch_size=UpperCAmelCase__       ,   num_workers=4						)
 __lowerCAmelCase:  Any        = DataLoader(UpperCAmelCase__       ,   shuffle=UpperCAmelCase__       ,   batch_size=UpperCAmelCase__       ,   num_workers=4						)

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

 # 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:  Tuple        = model.to(accelerator.device						)

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

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

 # Instantiate optimizer
 __lowerCAmelCase:  List[str]        = torch.optim.Adam(params=model.parameters()       ,   lr=lr / 25						)

 # Instantiate learning rate scheduler
 __lowerCAmelCase:  str        = OneCycleLR(optimizer=UpperCAmelCase__       ,   max_lr=UpperCAmelCase__       ,   epochs=UpperCAmelCase__       ,   steps_per_epoch=len(UpperCAmelCase__						)						)

 # 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:  Tuple        = accelerator.prepare(
     UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__						)

 # Now we train the model
 for epoch in range(UpperCAmelCase__						):
  model.train()
  for step, batch in enumerate(UpperCAmelCase__						):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   __lowerCAmelCase:  Any        = {k: v.to(accelerator.device						) for k, v in batch.items()}
   __lowerCAmelCase:  Tuple        = (batch['image'] - mean) / std
   __lowerCAmelCase:  Union[str, Any]        = model(UpperCAmelCase__						)
   __lowerCAmelCase:  List[str]        = torch.nn.functional.cross_entropy(UpperCAmelCase__       ,   batch['label']						)
   accelerator.backward(UpperCAmelCase__						)
   optimizer.step()
   lr_scheduler.step()
   optimizer.zero_grad()

  model.eval()
  __lowerCAmelCase:  int        = 0
  __lowerCAmelCase:  Dict        = 0
  for _, batch in enumerate(UpperCAmelCase__						):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   __lowerCAmelCase:  Optional[Any]        = {k: v.to(accelerator.device						) for k, v in batch.items()}
   __lowerCAmelCase:  Union[str, Any]        = (batch['image'] - mean) / std
   with torch.no_grad():
    __lowerCAmelCase:  str        = model(UpperCAmelCase__						)
   __lowerCAmelCase:  Optional[Any]        = outputs.argmax(dim=-1						)
   __lowerCAmelCase      ,				__lowerCAmelCase:  Union[str, Any]        = accelerator.gather_for_metrics((predictions, batch['label'])						)
   __lowerCAmelCase:  Optional[int]        = predictions == references
   num_elems += accurate_preds.shape[0]
   accurate += accurate_preds.long().sum()

  __lowerCAmelCase:  Optional[Any]        = accurate.item() / num_elems
  # Use accelerator.print to print only on the main process.
  accelerator.print(f'''epoch {epoch}: {1_00 * eval_metric:.2f}'''						)







def _a					(			)       ->       Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = argparse.ArgumentParser(description='Simple example of training script.'						)
 parser.add_argument('--data_dir'       ,   required=UpperCAmelCase__       ,   help='The data folder on disk.'						)
 parser.add_argument(
     '--mixed_precision'       ,   type=UpperCAmelCase__       ,   default=UpperCAmelCase__       ,   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(
     '--checkpointing_steps'       ,   type=UpperCAmelCase__       ,   default=UpperCAmelCase__       ,   help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'       ,   )
 parser.add_argument('--cpu'       ,   action='store_true'       ,   help='If passed, will train on the CPU.'						)
 __lowerCAmelCase:  int        = parser.parse_args()
 __lowerCAmelCase:  Optional[Any]        = {'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 2_24}
 training_function(UpperCAmelCase__       ,   UpperCAmelCase__						)


if __name__ == "__main__":
  main()






"""simple docstring"""







import numpy as np


def 	a__   (		UpperCAmelCase__							:      List[str]		,      UpperCAmelCase__							:      int		,      UpperCAmelCase__							:      str      )->						Optional[int]:
						UpperCAmelCase__       						=		n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						UpperCAmelCase__       						=		np.floor(UpperCAmelCase__      )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						UpperCAmelCase__       						=		int(n_draws - floored.sum()      )
						if need_to_add > 0:
												UpperCAmelCase__       						=		continuous - floored
												UpperCAmelCase__       						=		np.sort(np.unique(UpperCAmelCase__      )      )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((UpperCAmelCase__)						,   )       						=		np.where(remainder == value      )
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		UpperCAmelCase__       						=		min(len(UpperCAmelCase__      )		,      UpperCAmelCase__      )
																		UpperCAmelCase__       						=		rng.choice(UpperCAmelCase__		,      size=UpperCAmelCase__		,      replace=UpperCAmelCase__      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )







def 	a__   (		UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      Dict		,      UpperCAmelCase__							:      Any		,      UpperCAmelCase__							:      Union[str, Any]=1_0      )->						Tuple:
						UpperCAmelCase__						,   UpperCAmelCase__       						=		np.unique(UpperCAmelCase__		,      return_inverse=UpperCAmelCase__      )
						UpperCAmelCase__       						=		classes.shape[0]
						UpperCAmelCase__       						=		np.bincount(UpperCAmelCase__      )
						if np.min(UpperCAmelCase__      ) < 2:
												raise ValueError("""Minimum class count error"""      )
						if n_train < n_classes:
												raise ValueError(
												    """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)      )
						if n_test < n_classes:
												raise ValueError(
												    """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)      )
						UpperCAmelCase__       						=		np.split(np.argsort(UpperCAmelCase__		,      kind="""mergesort"""      )		,      np.cumsum(UpperCAmelCase__      )[:-1]      )
						for _ in range(UpperCAmelCase__      ):
												UpperCAmelCase__       						=		approximate_mode(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )
												UpperCAmelCase__       						=		class_counts - n_i
												UpperCAmelCase__       						=		approximate_mode(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )

												UpperCAmelCase__       						=		[]
												UpperCAmelCase__       						=		[]

												for i in range(UpperCAmelCase__      ):
																		UpperCAmelCase__       						=		rng.permutation(class_counts[i]      )
																		UpperCAmelCase__       						=		class_indices[i].take(UpperCAmelCase__		,      mode="""clip"""      )
																		train.extend(perm_indices_class_i[: n_i[i]]      )
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
												UpperCAmelCase__       						=		rng.permutation(UpperCAmelCase__      )
												UpperCAmelCase__       						=		rng.permutation(UpperCAmelCase__      )

												yield train, test

