
"""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_  (__a					):

					'''simple docstring'''





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



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			def __init__(					self    :       Any							, _snake_case    :       List[str]							, _snake_case    :       List[str]=None							, _snake_case    :       str=None						)   ->			Any:







								"""simple docstring"""
								A_								=    file_names
								A_								=    image_transform
								A_								=    label_to_id




			def __len__(					self    :       int						)   ->			Optional[int]:







								"""simple docstring"""
								return len(self.file_names						)






			def __getitem__(					self    :       Tuple							, _snake_case    :       Optional[int]						)   ->			int:







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





def 				A_  (__a					, __a					):

					'''simple docstring'''





					if args.with_tracking:
										A_								=    Accelerator(
										    cpu=args.cpu					, mixed_precision=args.mixed_precision					, log_with="all"					, project_dir=args.project_dir					)
					else:
										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(__a					, (list, tuple)					):
										A_								=    (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":
															A_								=    args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															A_								=    int(args.checkpointing_steps					)
										else:
															raise ValueError(
															    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'					)
					else:
										A_								=    None

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

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

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

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

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

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

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

					# Instantiate dataloaders.
					A_								=    DataLoader(__a					, shuffle=__a					, batch_size=__a					, num_workers=4					)
					A_								=    DataLoader(__a					, shuffle=__a					, batch_size=__a					, 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=__a					, num_classes=len(__a					)					)

					# 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 / 25					)

					# Instantiate learning rate scheduler
					A_								=    OneCycleLR(optimizer=__a					, max_lr=__a					, epochs=__a					, steps_per_epoch=len(__a					)					)

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

										if "epoch" in training_difference:
															A_								=    int(training_difference.replace("epoch_"					, ""					)					) + 1
															A_								=    None
										else:
															A_								=    int(training_difference.replace("step_"					, ""					)					)
															A_								=    resume_step // len(__a					)
															resume_step -= starting_epoch * len(__a					)

    # Now we train the model
					for epoch in range(__a					, __a					):
										model.train()
										if args.with_tracking:
															A_								=    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
															A_								=    accelerator.skip_first_batches(__a					, __a					)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															A_								=    train_dataloader
										for batch in active_dataloader:
															# 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(__a					)
															A_								=    torch.nn.functional.cross_entropy(__a					, batch["label"]					)
															# We keep track of the loss at each epoch
															if args.with_tracking:
																				total_loss += loss.detach().float()
															accelerator.backward(__a					)
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															overall_step += 1
															if isinstance(__a					, __a					):
																				A_								=    f'step_{overall_step}'
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														A_								=    os.path.join(args.output_dir					, __a					)
																									accelerator.save_state(__a					)
										model.eval()
										A_								=    0
										A_								=    0
										for step, batch in enumerate(__a					):
															# 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(__a					)
															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}: {100 * eval_metric:.2f}'					)
										if args.with_tracking:
															accelerator.log(
															    {
															        "accuracy": 100 * eval_metric,
															        "train_loss": total_loss.item() / len(__a					),
															        "epoch": epoch,
															    }					, step=__a					, )
										if checkpointing_steps == "epoch":
															A_								=    f'epoch_{epoch}'
															if args.output_dir is not None:
																				A_								=    os.path.join(args.output_dir					, __a					)
															accelerator.save_state(__a					)

					if args.with_tracking:
										accelerator.end_training()
def 				A_  ():

					'''simple docstring'''





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


if __name__ == "__main__":
		main()






"""simple docstring"""





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase_       :    Any						      =			{'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    str						      =			[
		    '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''VanForImageClassification''',
		    '''VanModel''',
		    '''VanPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_van import (
				    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    VanForImageClassification,
				    VanModel,
				    VanPreTrainedModel,
				)

else:
		import sys

		UpperCamelCase_       :    Optional[Any]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure)




