



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				(					_lowerCamelCase  ):





							'''simple docstring'''




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



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a,			__a=None,			__a=None):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    file_names
														_lowerCAmelCase     :							Optional[int]    =    image_transform
														_lowerCAmelCase     :							Optional[int]    =    label_to_id







							def __len__(   self):

														'''simple docstring'''

														return len(self.file_names)







							def __getitem__(   self,			__a):

														'''simple docstring'''

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






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if args.with_tracking:
														_lowerCAmelCase     :							Dict    =    Accelerator(
														    cpu=args.cpu				, mixed_precision=args.mixed_precision				, log_with="all"				, project_dir=args.project_dir  )
							else:
														_lowerCAmelCase     :							Optional[Any]    =    Accelerator(cpu=args.cpu				, mixed_precision=args.mixed_precision  )

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

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

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

							# Build the label correspondences
							_lowerCAmelCase     :							Optional[int]    =    [extract_label(_lowerCamelCase  ) for fname in file_names]
							_lowerCAmelCase     :							List[str]    =    list(set(_lowerCamelCase  )  )
							id_to_label.sort()
							_lowerCAmelCase     :							Optional[int]    =    {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     :							Optional[int]    =    np.random.permutation(len(_lowerCamelCase  )  )
							_lowerCAmelCase     :							Tuple    =    int(0.8 * len(_lowerCamelCase  )  )
							_lowerCAmelCase     :							Optional[Any]    =    random_perm[:cut]
							_lowerCAmelCase     :							List[Any]    =    random_perm[cut:]

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

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

							# Instantiate dataloaders.
							_lowerCAmelCase     :							Any    =    DataLoader(_lowerCamelCase				, shuffle=_lowerCamelCase				, batch_size=_lowerCamelCase				, num_workers=4  )
							_lowerCAmelCase     :							Any    =    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     :							str    =    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     :							List[Any]    =    model.to(accelerator.device  )

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

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

							# Instantiate optimizer
							_lowerCAmelCase     :							List[str]    =    torch.optim.Adam(params=model.parameters()				, lr=lr / 25  )

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

														if "epoch" in training_difference:
																					_lowerCAmelCase     :							Any    =    int(training_difference.replace("epoch_"				, ""  )  ) + 1
																					_lowerCAmelCase     :							Union[str, Any]    =    None
														else:
																					_lowerCAmelCase     :							Any    =    int(training_difference.replace("step_"				, ""  )  )
																					_lowerCAmelCase     :							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:
																					_lowerCAmelCase     :							Union[str, 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
																					_lowerCAmelCase     :							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
																					_lowerCAmelCase     :							Optional[int]    =    train_dataloader
														for batch in active_dataloader:
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					_lowerCAmelCase     :							Optional[Any]    =    {k: v.to(accelerator.device  ) for k, v in batch.items()}
																					_lowerCAmelCase     :							Dict    =    (batch["image"] - mean) / std
																					_lowerCAmelCase     :							Optional[Any]    =    model(_lowerCamelCase  )
																					_lowerCAmelCase     :							Union[str, 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  ):
																												_lowerCAmelCase     :							Any    =    F"step_{overall_step}"
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										_lowerCAmelCase     :							List[Any]    =    os.path.join(args.output_dir				, _lowerCamelCase  )
																																			accelerator.save_state(_lowerCamelCase  )
														model.eval()
														_lowerCAmelCase     :							List[str]    =    0
														_lowerCAmelCase     :							Union[str, Any]    =    0
														for step, batch in enumerate(_lowerCamelCase  ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					_lowerCAmelCase     :							int    =    {k: v.to(accelerator.device  ) for k, v in batch.items()}
																					_lowerCAmelCase     :							str    =    (batch["image"] - mean) / std
																					with torch.no_grad():
																												_lowerCAmelCase     :							Tuple    =    model(_lowerCamelCase  )
																					_lowerCAmelCase     :							Dict    =    outputs.argmax(dim=-1  )
																					_lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    accelerator.gather_for_metrics((predictions, batch["label"])  )
																					_lowerCAmelCase     :							int    =    predictions == references
																					num_elems += accurate_preds.shape[0]
																					accurate += accurate_preds.long().sum()

														_lowerCAmelCase     :							List[str]    =    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":
																					_lowerCAmelCase     :							int    =    F"epoch_{epoch}"
																					if args.output_dir is not None:
																												_lowerCAmelCase     :							List[Any]    =    os.path.join(args.output_dir				, _lowerCamelCase  )
																					accelerator.save_state(_lowerCamelCase  )

							if args.with_tracking:
														accelerator.end_training()






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[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"				, )
							_lowerCAmelCase     :							Dict    =    parser.parse_args()
							_lowerCAmelCase     :							Any    =    {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
							training_function(_lowerCamelCase				, _lowerCamelCase  )


if __name__ == "__main__":
						main()




import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCAmelCase__      		= logging.get_logger(__name__)







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				None:
            warnings.warn(
                "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                " use BeitImageProcessor instead." ,						__lowerCamelCase ,						)
            super().__init__(*__lowerCamelCase ,						**__lowerCamelCase)
