






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      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Optional[Any]      )  -> List[Any]:





    """simple docstring"""

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


class  __lowercase (		lowercase_					):







     '''simple docstring'''




     def __init__(			self    :     Any	,		UpperCamelCase_    :     Optional[Any]	,		UpperCamelCase_    :     int=None	,		UpperCamelCase_    :     Any=None			):


         """simple docstring"""



         __A             =		file_names
         __A             =		image_transform
         __A             =		label_to_id




     def __len__(			self    :     List[str]			):


         """simple docstring"""



         return len(self.file_names			)





     def __getitem__(			self    :     List[str]	,		UpperCamelCase_    :     Any			):


         """simple docstring"""



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



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Any     ,			__lowercase				:     int      )  -> Dict:





    """simple docstring"""

    __A             =		Accelerator(cpu=args.cpu     ,			mixed_precision=args.mixed_precision      )

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __A             =		config["""lr"""]
    __A             =		int(config["""num_epochs"""]      )
    __A             =		int(config["""seed"""]      )
    __A             =		int(config["""batch_size"""]      )
    __A             =		config["""image_size"""]
    if not isinstance(__lowercase     ,			(list, tuple)      ):
        __A             =		(image_size, image_size)

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

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

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

    # Split our filenames between train and validation
    __A             =		np.random.permutation(len(__lowercase      )      )
    __A             =		int(0.8 * len(__lowercase      )      )
    __A             =		random_perm[:cut]
    __A             =		random_perm[cut:]

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

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

    # Instantiate dataloaders.
    __A             =		DataLoader(__lowercase     ,			shuffle=__lowercase     ,			batch_size=__lowercase     ,			num_workers=4      )
    __A             =		DataLoader(__lowercase     ,			shuffle=__lowercase     ,			batch_size=__lowercase     ,			num_workers=4      )

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

    # 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).
    __A             =		model.to(accelerator.device      )

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

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

    # Instantiate optimizer
    __A             =		torch.optim.Adam(params=model.parameters()     ,			lr=lr / 2_5      )

    # Instantiate learning rate scheduler
    __A             =		OneCycleLR(optimizer=__lowercase     ,			max_lr=__lowercase     ,			epochs=__lowercase     ,			steps_per_epoch=len(__lowercase      )      )

    # 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.
    __A      ,		__A      ,		__A      ,		__A      ,		__A             =		accelerator.prepare(
        __lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase      )

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

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

        __A             =		accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"epoch {epoch}: {1_0_0 * eval_metric:.2f}"      )



def      _SCREAMING_SNAKE_CASE  (	)  -> Optional[Any]:





    """simple docstring"""

    __A             =		argparse.ArgumentParser(description="""Simple example of training script."""      )
    parser.add_argument("""--data_dir"""     ,			required=__lowercase     ,			help="""The data folder on disk."""      )
    parser.add_argument(
        """--mixed_precision"""     ,			type=__lowercase     ,			default=__lowercase     ,			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=__lowercase     ,			default=__lowercase     ,			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."""      )
    __A             =		parser.parse_args()
    __A             =		{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 6_4, """image_size""": 2_2_4}
    training_function(__lowercase     ,			__lowercase      )


if __name__ == "__main__":
  main()












from ..utils import DummyObject, requires_backends


__a :    int								=				None



def      _SCREAMING_SNAKE_CASE  (	*__lowercase				:     Any     ,			**__lowercase				:     Optional[int]      )  -> Tuple:





    """simple docstring"""

    requires_backends(__lowercase     ,			["""sentencepiece""", """tokenizers"""]      )




