
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  :  Optional[Any]    ):




			"""simple docstring"""


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





class      _lowerCAmelCase (     __a	):




				def __init__( self    ,    _UpperCamelCase    ,    _UpperCamelCase=None    ,    _UpperCamelCase=None					)    ->				Optional[int]:
							lowerCAmelCase_      =     file_names
							lowerCAmelCase_      =     image_transform
							lowerCAmelCase_      =     label_to_id




				def __len__( self					)    ->				Optional[Any]:
							return len(self.file_names					)




				def __getitem__( self    ,    _UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     self.file_names[idx]
							lowerCAmelCase_      =     PIL.Image.open(_UpperCamelCase					)
							lowerCAmelCase_      =     raw_image.convert("RGB"					)
							if self.image_transform is not None:
										lowerCAmelCase_      =     self.image_transform(_UpperCamelCase					)
							lowerCAmelCase_      =     extract_label(_UpperCamelCase					)
							if self.label_to_id is not None:
										lowerCAmelCase_      =     self.label_to_id[label]
							return {"image": image, "label": label}






def        lowerCamelCase__    (    __lowerCAmelCase  :  Tuple   ,							__lowerCAmelCase  :  Optional[int]    ):




			"""simple docstring"""


			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(__lowerCAmelCase   ,							(list, tuple)    ):
						lowerCAmelCase_      =     (image_size, image_size)

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

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

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

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

			# Instantiate dataloaders.
			lowerCAmelCase_      =     DataLoader(__lowerCAmelCase   ,							shuffle=__lowerCAmelCase   ,							batch_size=__lowerCAmelCase   ,							num_workers=4    )
			lowerCAmelCase_      =     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)
			lowerCAmelCase_      =     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).
			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=__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.
			lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_      =     accelerator.prepare(
			    __lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase    )

			# Now we train the model
			for epoch in range(__lowerCAmelCase    ):
						model.train()
						for step, batch in enumerate(__lowerCAmelCase    ):
									# 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(__lowerCAmelCase    )
									lowerCAmelCase_      =     torch.nn.functional.cross_entropy(__lowerCAmelCase   ,							batch["label"]    )
									accelerator.backward(__lowerCAmelCase    )
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()

						model.eval()
						lowerCAmelCase_      =     0
						lowerCAmelCase_      =     0
						for _, batch in enumerate(__lowerCAmelCase    ):
									# 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(__lowerCAmelCase    )
									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}: {100 * eval_metric:.2f}"""    )






def        lowerCamelCase__    (    ):




			"""simple docstring"""


			lowerCAmelCase_      =     argparse.ArgumentParser(description="Simple example of training script."    )
			parser.add_argument("--data_dir"   ,							required=__lowerCAmelCase   ,							help="The data folder on disk."    )
			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(
			    "--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("--cpu"   ,							action="store_true"   ,							help="If passed, will train on the CPU."    )
			lowerCAmelCase_      =     parser.parse_args()
			lowerCAmelCase_      =     {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
			training_function(__lowerCAmelCase   ,							__lowerCAmelCase    )


if __name__ == "__main__":
			main()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A       =					{"configuration_nat": ["NAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "NatConfig"]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "NAT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "NatForImageClassification",
			    "NatModel",
			    "NatPreTrainedModel",
			    "NatBackbone",
			]

if TYPE_CHECKING:
			from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_nat import (
						    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    NatBackbone,
						    NatForImageClassification,
						    NatModel,
						    NatPreTrainedModel,
						)

else:
			import sys

			_A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



