


'''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_       (						lowercase    )			->List[str]:






				'''simple docstring'''

				__lowerCamelCase		:      Union[str, Any]											=					fname.split(os.path.sep    )[-1]
				return re.search(R"^(.*)_\d+\.jpg$"							,						lowercase    ).groups()[0]





class _snake_case   ( a__   ):
							"""simple docstring"""





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



											"""simple docstring"""







											__lowerCamelCase		:      Optional[int]											=					file_names
											__lowerCamelCase		:      List[str]											=					image_transform
											__lowerCamelCase		:      Any											=					label_to_id







							def __len__(   self	)     ->Dict:



											"""simple docstring"""







											return len(self.file_names	)







							def __getitem__(   self			,				__A	)     ->List[str]:



											"""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		:      Tuple											=					self.image_transform(__A	)
											__lowerCamelCase		:      Optional[Any]											=					extract_label(__A	)
											if self.label_to_id is not None:
															__lowerCamelCase		:      Optional[int]											=					self.label_to_id[label]
											return {"image": image, "label": label}





def 							a_       (						lowercase							,						lowercase    )			->Union[str, Any]:






				'''simple docstring'''

				__lowerCamelCase		:      Tuple											=					Accelerator(cpu=args.cpu							,						mixed_precision=args.mixed_precision    )

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

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

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

				# For training we use a simple RandomResizedCrop
				__lowerCamelCase		:      Union[str, Any]											=					Compose([RandomResizedCrop(lowercase							,						scale=(0.5, 1.0)    ), ToTensor()]    )
				__lowerCamelCase		:      Union[str, Any]											=					PetsDataset(
				    [file_names[i] for i in train_split]							,						image_transform=lowercase							,						label_to_id=lowercase    )

				# For evaluation, we use a deterministic Resize
				__lowerCamelCase		:      Tuple											=					Compose([Resize(lowercase    ), ToTensor()]    )
				__lowerCamelCase		:      List[Any]											=					PetsDataset([file_names[i] for i in eval_split]							,						image_transform=lowercase							,						label_to_id=lowercase    )

				# Instantiate dataloaders.
				__lowerCamelCase		:      Any											=					DataLoader(lowercase							,						shuffle=lowercase							,						batch_size=lowercase							,						num_workers=4    )
				__lowerCamelCase		:      int											=					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)
				__lowerCamelCase		:      Tuple											=					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).
				__lowerCamelCase		:      str											=					model.to(accelerator.device    )

				# Freezing the base model
				for param in model.parameters():
								__lowerCamelCase		:      List[str]											=					False
				for param in model.get_classifier().parameters():
								__lowerCamelCase		:      int											=					True

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

				# Instantiate optimizer
				__lowerCamelCase		:      str											=					torch.optim.Adam(params=model.parameters()							,						lr=lr / 25    )

				# Instantiate learning rate scheduler
				__lowerCamelCase		:      Optional[Any]											=					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.
				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      List[str]											=					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`.
												__lowerCamelCase		:      Optional[int]											=					{k: v.to(accelerator.device    ) for k, v in batch.items()}
												__lowerCamelCase		:      Any											=					(batch["image"] - mean) / std
												__lowerCamelCase		:      Union[str, Any]											=					model(lowercase    )
												__lowerCamelCase		:      Union[str, Any]											=					torch.nn.functional.cross_entropy(lowercase							,						batch["label"]    )
												accelerator.backward(lowercase    )
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

								model.eval()
								__lowerCamelCase		:      int											=					0
								__lowerCamelCase		:      str											=					0
								for _, batch in enumerate(lowercase    ):
												# 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		:      str											=					(batch["image"] - mean) / std
												with torch.no_grad():
																__lowerCamelCase		:      str											=					model(lowercase    )
												__lowerCamelCase		:      Union[str, Any]											=					outputs.argmax(dim=-1    )
												__lowerCamelCase						,					__lowerCamelCase		:      List[str]											=					accelerator.gather_for_metrics((predictions, batch["label"])    )
												__lowerCamelCase		:      Union[str, Any]											=					predictions == references
												num_elems += accurate_preds.shape[0]
												accurate += accurate_preds.long().sum()

								__lowerCamelCase		:      Optional[int]											=					accurate.item() / num_elems
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'epoch {epoch}: {100 * eval_metric:.2f}'    )





def 							a_       (						)			->Dict:






				'''simple docstring'''

				__lowerCamelCase		:      Union[str, Any]											=					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."    )
				__lowerCamelCase		:      Tuple											=					parser.parse_args()
				__lowerCamelCase		:      str											=					{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
				training_function(lowercase							,						lowercase    )


if __name__ == "__main__":
				main()









'''simple docstring'''




# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowercase  :      Tuple      		=				abspath(join(dirname(dirname(dirname(__file__))), """src"""))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)





def 							a_       (						lowercase    )			->Any:






				'''simple docstring'''

				from diffusers.utils.testing_utils import pytest_addoption_shared

				pytest_addoption_shared(lowercase    )





def 							a_       (						lowercase    )			->str:






				'''simple docstring'''

				from diffusers.utils.testing_utils import pytest_terminal_summary_main

				__lowerCamelCase		:      Union[str, Any]											=					terminalreporter.config.getoption("--make-reports"    )
				if make_reports:
								pytest_terminal_summary_main(lowercase							,						id=lowercase    )





