






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  (     UpperCamelCase_					:				int   ) ->   Optional[Any]:
						"""simple docstring"""







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


class      lowercase__     (			_UpperCAmelCase	):

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







							'''simple docstring'''





							lowerCAmelCase__       =						file_names
							lowerCAmelCase__       =						image_transform
							lowerCAmelCase__       =						label_to_id

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







							'''simple docstring'''





							return len(self.file_names       )


	def __getitem__(							self		,    __UpperCAmelCase       )->					Any:







							'''simple docstring'''





							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        _a  (     UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				Union[str, Any]   ) ->   Optional[int]:
						"""simple docstring"""







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

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

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

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

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

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

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

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

						# Instantiate dataloaders.
						lowerCAmelCase__       =						DataLoader(UpperCamelCase_    ,   shuffle=UpperCamelCase_    ,   batch_size=UpperCamelCase_    ,   num_workers=4   )
						lowerCAmelCase__       =						DataLoader(UpperCamelCase_    ,   shuffle=UpperCamelCase_    ,   batch_size=UpperCamelCase_    ,   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=UpperCamelCase_    ,   num_classes=len(UpperCamelCase_   )   )

						# 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=UpperCamelCase_    ,   max_lr=UpperCamelCase_    ,   epochs=UpperCamelCase_    ,   steps_per_epoch=len(UpperCamelCase_   )   )

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

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

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

						if args.with_tracking:
												accelerator.end_training()


def        _a  (     ) ->   Tuple:
						"""simple docstring"""







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


if __name__ == "__main__":
	main()














# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    '''pipelines_utils''',
    '''0.22.0''',
    '''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.''',
    standard_warn=False,
    stacklevel=3,
)






