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      (	snake_case__							)  ->      int:
							__UpperCAmelCase  :	Any   			=      fname.split(os.path.sep							)[-1]
							return re.search(r"^(.*)_\d+\.jpg$",			snake_case__							).groups()[0]




class     _snake_case  (  lowerCamelCase_ ):
					def __init__(     self: Optional[Any]		,     __lowerCamelCase: List[Any]		,     __lowerCamelCase: List[str]=None		,     __lowerCamelCase: List[Any]=None   )			-> Any:
												__UpperCAmelCase  :	Any   			=      file_names
												__UpperCAmelCase  :	str   			=      image_transform
												__UpperCAmelCase  :	Dict   			=      label_to_id
					def __len__(     self: Any   )			-> List[Any]:
												return len(self.file_names   )





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




def      _UpperCamelCase      (	snake_case__,			snake_case__							)  ->      str:
							# Initialize accelerator
							if args.with_tracking:
														__UpperCAmelCase  :	Optional[Any]   			=      Accelerator(
														    cpu=args.cpu,			mixed_precision=args.mixed_precision,			log_with="all",			project_dir=args.project_dir							)
							else:
														__UpperCAmelCase  :	Any   			=      Accelerator(cpu=args.cpu,			mixed_precision=args.mixed_precision							)

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

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

							# Grab all the image filenames
							__UpperCAmelCase  :	List[str]   			=      [os.path.join(args.data_dir,			snake_case__							) for fname in os.listdir(args.data_dir							) if fname.endswith(".jpg"							)]

							# Build the label correspondences
							__UpperCAmelCase  :	List[Any]   			=      [extract_label(snake_case__							) for fname in file_names]
							__UpperCAmelCase  :	Union[str, Any]   			=      list(set(snake_case__							)							)
							id_to_label.sort()
							__UpperCAmelCase  :	Tuple   			=      {lbl: i for i, lbl in enumerate(snake_case__							)}

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

							# Split our filenames between train and validation
							__UpperCAmelCase  :	Optional[Any]   			=      np.random.permutation(len(snake_case__							)							)
							__UpperCAmelCase  :	str   			=      int(0.8 * len(snake_case__							)							)
							__UpperCAmelCase  :	Optional[int]   			=      random_perm[:cut]
							__UpperCAmelCase  :	Union[str, Any]   			=      random_perm[cut:]

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

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

							# Instantiate dataloaders.
							__UpperCAmelCase  :	int   			=      DataLoader(snake_case__,			shuffle=snake_case__,			batch_size=snake_case__,			num_workers=4							)
							__UpperCAmelCase  :	int   			=      DataLoader(snake_case__,			shuffle=snake_case__,			batch_size=snake_case__,			num_workers=4							)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__UpperCAmelCase  :	str   			=      create_model("resnet50d",			pretrained=snake_case__,			num_classes=len(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).
							__UpperCAmelCase  :	Optional[int]   			=      model.to(accelerator.device							)

							# Freezing the base model
							for param in model.parameters():
														__UpperCAmelCase  :	List[str]   			=      False
							for param in model.get_classifier().parameters():
														__UpperCAmelCase  :	Tuple   			=      True

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

							# Instantiate optimizer
							__UpperCAmelCase  :	Optional[int]   			=      torch.optim.Adam(params=model.parameters(),			lr=lr / 25							)

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

														if "epoch" in training_difference:
																					__UpperCAmelCase  :	Any   			=      int(training_difference.replace("epoch_",			""							)							) + 1
																					__UpperCAmelCase  :	Tuple   			=      None
														else:
																					__UpperCAmelCase  :	List[Any]   			=      int(training_difference.replace("step_",			""							)							)
																					__UpperCAmelCase  :	Dict   			=      resume_step // len(snake_case__							)
																					resume_step -= starting_epoch * len(snake_case__							)

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

														__UpperCAmelCase  :	Any   			=      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(snake_case__							),
																					        "epoch": epoch,
																					    },			step=snake_case__,			)
														if checkpointing_steps == "epoch":
																					__UpperCAmelCase  :	Tuple   			=      f'''epoch_{epoch}'''
																					if args.output_dir is not None:
																												__UpperCAmelCase  :	List[str]   			=      os.path.join(args.output_dir,			snake_case__							)
																					accelerator.save_state(snake_case__							)

							if args.with_tracking:
														accelerator.end_training()




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


if __name__ == "__main__":
	main()











'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      int						)	->	bool:
				if num < 0:
								return False

				_a								= num
				_a								= 0
				while num > 0:
								_a								= rev_num * 10 + (num % 10)
								num //= 10

				return num_copy == rev_num


if __name__ == "__main__":
					import doctest

					doctest.testmod()
