
'''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 	A  (   UpperCamelCase_       :  Dict   )       ->							Tuple:







			'''simple docstring'''



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






class  A  (						SCREAMING_SNAKE_CASE__     ):





	def __init__(       self      :							List[Any]   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Any=None   ,				__magic_name__      :							List[Any]=None				):


				"""simple docstring"""

				lowerCAmelCase__										=				file_names
				lowerCAmelCase__										=				image_transform
				lowerCAmelCase__										=				label_to_id





	def __len__(       self      :							Any				):


				"""simple docstring"""

				return len(self.file_names				)







	def __getitem__(       self      :							List[Any]   ,				__magic_name__      :							List[Any]				):


				"""simple docstring"""

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







def 	A  (   UpperCamelCase_       :  Tuple    ,						UpperCamelCase_       :  List[str]   )       ->							List[str]:







			'''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(UpperCamelCase_    ,						(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(UpperCamelCase_   )[-1].split("."   )[0]
						accelerator.init_trackers(UpperCamelCase_    ,						UpperCamelCase_   )

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

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

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

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

			# Instantiate dataloaders.
			lowerCAmelCase__										=				DataLoader(UpperCamelCase_    ,						shuffle=UpperCamelCase_    ,						batch_size=UpperCamelCase_    ,						num_workers=4   )
			lowerCAmelCase__										=				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__										=				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__										=				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=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__										=				accelerator.prepare(
			    UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_   )
			# 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(UpperCamelCase_   )[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(UpperCamelCase_   )
									resume_step -= starting_epoch * len(UpperCamelCase_   )

    # Now we train the model
			for epoch in range(UpperCamelCase_    ,						UpperCamelCase_   ):
						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(UpperCamelCase_    ,						UpperCamelCase_   )
									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(UpperCamelCase_   )
									lowerCAmelCase__										=				torch.nn.functional.cross_entropy(UpperCamelCase_    ,						batch["label"]   )
									# We keep track of the loss at each epoch
									if args.with_tracking:
												total_loss += loss.detach().float()
									accelerator.backward(UpperCamelCase_   )
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()
									overall_step += 1
									if isinstance(UpperCamelCase_    ,						UpperCamelCase_   ):
												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    ,						UpperCamelCase_   )
															accelerator.save_state(UpperCamelCase_   )
						model.eval()
						lowerCAmelCase__										=				0
						lowerCAmelCase__										=				0
						for step, batch in enumerate(UpperCamelCase_   ):
									# 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(UpperCamelCase_   )
									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(UpperCamelCase_   ),
									        "epoch": epoch,
									    }    ,						step=UpperCamelCase_    ,						)
						if checkpointing_steps == "epoch":
									lowerCAmelCase__										=				F"""epoch_{epoch}"""
									if args.output_dir is not None:
												lowerCAmelCase__										=				os.path.join(args.output_dir    ,						UpperCamelCase_   )
									accelerator.save_state(UpperCamelCase_   )

			if args.with_tracking:
						accelerator.end_training()







def 	A  (   )       ->							Optional[int]:







			'''simple docstring'''



			lowerCAmelCase__										=				argparse.ArgumentParser(description="Simple example of training script."   )
			parser.add_argument("--data_dir"    ,						required=UpperCamelCase_    ,						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=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("--cpu"    ,						action="store_true"    ,						help="If passed, will train on the CPU."   )
			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(
			    "--output_dir"    ,						type=UpperCamelCase_    ,						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=UpperCamelCase_    ,						default=UpperCamelCase_    ,						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=UpperCamelCase_    ,						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(UpperCamelCase_    ,						UpperCamelCase_   )


if __name__ == "__main__":
		main()






"""simple docstring"""




from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		return getitem, k







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return setitem, k, v







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		return delitem, k







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	*lowerCamelCase__  ):





		"""simple docstring"""




		try:
				return fun(lowerCamelCase__      ,	*lowerCamelCase__  ), None
		except Exception as e:
				return None, e


__lowerCAmelCase     :				Optional[Any]    				=    (
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
)

__lowerCAmelCase     :				Optional[int]    				=    [
    _set("key_a", "val_a"),
    _set("key_a", "val_b"),
]

__lowerCAmelCase     :				List[Any]    				=    [
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
    _del("key_a"),
    _del("key_b"),
    _set("key_a", "val_a"),
    _del("key_a"),
]

__lowerCAmelCase     :				int    				=    [
    _get("key_a"),
    _del("key_a"),
    _set("key_a", "val_a"),
    _del("key_a"),
    _del("key_a"),
    _get("key_a"),
]

__lowerCAmelCase     :				Optional[Any]    				=    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

__lowerCAmelCase     :				Any    				=    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("key_a", "val_b"),
]







@pytest.mark.parametrize(
    """operations"""      ,	(
        pytest.param(_add_items      ,	id="""add items"""  ),
        pytest.param(_overwrite_items      ,	id="""overwrite items"""  ),
        pytest.param(_delete_items      ,	id="""delete items"""  ),
        pytest.param(_access_absent_items      ,	id="""access absent items"""  ),
        pytest.param(_add_with_resize_up      ,	id="""add with resize up"""  ),
        pytest.param(_add_with_resize_down      ,	id="""add with resize down"""  ),
    )      ,	)
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = HashMap(initial_block_size=4  )
		lowerCAmelCase__   = {}
		for _, (fun, *args) in enumerate(lowerCamelCase__  ):
				lowerCAmelCase__ ,       lowerCAmelCase__   = _run_operation(lowerCamelCase__      ,	lowerCamelCase__      ,	*lowerCamelCase__  )
				lowerCAmelCase__ ,       lowerCAmelCase__   = _run_operation(lowerCamelCase__      ,	lowerCamelCase__      ,	*lowerCamelCase__  )
				assert my_res == py_res
				assert str(lowerCamelCase__  ) == str(lowerCamelCase__  )
				assert set(lowerCamelCase__  ) == set(lowerCamelCase__  )
				assert len(lowerCamelCase__  ) == len(lowerCamelCase__  )
				assert set(my.items()  ) == set(py.items()  )







def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		def is_public(lowerCamelCase__  ) -> bool:
				return not name.startswith("""_"""  )

		lowerCAmelCase__   = {name for name in dir({}  ) if is_public(lowerCamelCase__  )}
		lowerCAmelCase__   = {name for name in dir(HashMap()  ) if is_public(lowerCamelCase__  )}

		assert dict_public_names > hash_public_names




