


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






class 		lowerCAmelCase__	(	lowerCamelCase__		):




			def __init__(						self     :   Union[str, Any]		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   Optional[Any]=None		,     lowerCamelCase__     :   Dict=None       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					file_names
					_UpperCAmelCase						:				List[Any]	    =					image_transform
					_UpperCAmelCase						:				Dict	    =					label_to_id




			def __len__(						self     :   Union[str, Any]       )      ->Dict:



					'''simple docstring'''







					return len(self.file_names       )






			def __getitem__(						self     :   Optional[int]		,     lowerCamelCase__     :   List[str]       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					self.file_names[idx]
					_UpperCAmelCase						:				Tuple	    =					PIL.Image.open(lowerCAmelCase__       )
					_UpperCAmelCase						:				Optional[int]	    =					raw_image.convert("RGB"       )
					if self.image_transform is not None:
							_UpperCAmelCase						:				List[str]	    =					self.image_transform(lowerCAmelCase__       )
					_UpperCAmelCase						:				str	    =					extract_label(lowerCAmelCase__       )
					if self.label_to_id is not None:
							_UpperCAmelCase						:				Optional[int]	    =					self.label_to_id[label]
					return {"image": image, "label": label}






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		# Initialize accelerator
		if args.with_tracking:
				_UpperCAmelCase						:				List[str]	    =					Accelerator(
				    cpu=args.cpu					,       mixed_precision=args.mixed_precision					,       log_with="all"					,       project_dir=args.project_dir      )
		else:
				_UpperCAmelCase						:				List[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						:				Optional[Any]	    =					config["""lr"""]
		_UpperCAmelCase						:				int	    =					int(config["num_epochs"]      )
		_UpperCAmelCase						:				List[Any]	    =					int(config["seed"]      )
		_UpperCAmelCase						:				Optional[Any]	    =					int(config["batch_size"]      )
		_UpperCAmelCase						:				Optional[Any]	    =					config["""image_size"""]
		if not isinstance(_lowerCamelCase					,       (list, tuple)      ):
				_UpperCAmelCase						:				Tuple	    =					(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						:				List[str]	    =					int(args.checkpointing_steps      )
				else:
						raise ValueError(
						    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""      )
		else:
				_UpperCAmelCase						:				str	    =					None

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

		# Grab all the image filenames
		_UpperCAmelCase						:				Any	    =					[os.path.join(args.data_dir					,       _lowerCamelCase      ) for fname in os.listdir(args.data_dir      ) if fname.endswith(".jpg"      )]

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

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

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

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

		# For evaluation, we use a deterministic Resize
		_UpperCAmelCase						:				List[str]	    =					Compose([Resize(_lowerCamelCase      ), ToTensor()]      )
		_UpperCAmelCase						:				int	    =					PetsDataset([file_names[i] for i in eval_split]					,       image_transform=_lowerCamelCase					,       label_to_id=_lowerCamelCase      )

		# Instantiate dataloaders.
		_UpperCAmelCase						:				Optional[Any]	    =					DataLoader(_lowerCamelCase					,       shuffle=_lowerCamelCase					,       batch_size=_lowerCamelCase					,       num_workers=4      )
		_UpperCAmelCase						:				Optional[int]	    =					DataLoader(_lowerCamelCase					,       shuffle=_lowerCamelCase					,       batch_size=_lowerCamelCase					,       num_workers=4      )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_UpperCAmelCase						:				Any	    =					create_model("resnet50d"					,       pretrained=_lowerCamelCase					,       num_classes=len(_lowerCamelCase      )      )

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

		# Freezing the base model
		for param in model.parameters():
				_UpperCAmelCase						:				int	    =					False
		for param in model.get_classifier().parameters():
				_UpperCAmelCase						:				Union[str, Any]	    =					True

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

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

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

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

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

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

				_UpperCAmelCase						:				Optional[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(_lowerCamelCase      ),
						        "epoch": epoch,
						    }					,       step=_lowerCamelCase					,       )
				if checkpointing_steps == "epoch":
						_UpperCAmelCase						:				Any	    =					F"""epoch_{epoch}"""
						if args.output_dir is not None:
								_UpperCAmelCase						:				Tuple	    =					os.path.join(args.output_dir					,       _lowerCamelCase      )
						accelerator.save_state(_lowerCamelCase      )

		if args.with_tracking:
				accelerator.end_training()






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


if __name__ == "__main__":
						main()








'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]       ,      _lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		str           =		int(_lowerCamelCase					)

		# Initialize Result
		__SCREAMING_SNAKE_CASE       :		Tuple           =		[]

		# Traverse through all denomination
		for denomination in reversed(_lowerCamelCase					):
				# Find denominations
				while int(_lowerCamelCase					) >= int(_lowerCamelCase					):
						total_value -= int(_lowerCamelCase					)
						answer.append(_lowerCamelCase					)  # Append the "answers" array

		return answer


# Driver Code
if __name__ == "__main__":
						UpperCamelCase__       :	int    = []
						UpperCamelCase__       :	List[Any]    = '''0'''

						if (
						    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
						    == "y"
						):
												UpperCamelCase__       :	Tuple    = int(input('''Enter the number of denominations you want to add: ''').strip())

												for i in range(0, n):
																		denominations.append(int(input(f"Denomination {i}: ").strip()))
												UpperCamelCase__       :	str    = input('''Enter the change you want to make in Indian Currency: ''').strip()
						else:
												# All denominations of Indian Currency if user does not enter
												UpperCamelCase__       :	List[Any]    = [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
												UpperCamelCase__       :	str    = input('''Enter the change you want to make: ''').strip()

						if int(value) == 0 or int(value) < 0:
												print('''The total value cannot be zero or negative.''')

						else:
												print(f"Following is minimal change for {value}: ")
												UpperCamelCase__       :	int    = find_minimum_change(denominations, value)
												# Print result
												for i in range(len(answer)):
																		print(answer[i], end=''' ''')