



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     :				Optional[Any]		):

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

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_=None  ,  a_=None		):
											a__				      =		file_names
											a__				      =		image_transform
											a__				      =		label_to_id


							def __len__(							self		):
											return len(self.file_names		)







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




def 			A_      (	__a     :				List[str] ,		__a     :				List[str]		):

				"""simple docstring"""
				# Initialize accelerator
				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()






import os
from datetime import datetime as dt

from github import Github


UpperCAmelCase							 =					[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]

def 			A_      (	):

				"""simple docstring"""
				a__				      =		Github(os.environ["""GITHUB_TOKEN"""]		)
				a__				      =		g.get_repo("""huggingface/diffusers"""		)
				a__				      =		repo.get_issues(state="""open"""		)

				for issue in open_issues:
								a__				      =		sorted(issue.get_comments() ,		key=lambda __a		: i.created_at ,		reverse=__a		)
								a__				      =		comments[0] if len(__a		) > 0 else None
								if (
								    last_comment is not None
								    and last_comment.user.login == "github-actions[bot]"
								    and (dt.utcnow() - issue.updated_at).days > 7
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()		)
								):
												# Closes the issue after 7 days of inactivity since the Stalebot notification.
												issue.edit(state="""closed"""		)
								elif (
								    "stale" in issue.get_labels()
								    and last_comment is not None
								    and last_comment.user.login != "github-actions[bot]"
								):
												# Opens the issue if someone other than Stalebot commented.
												issue.edit(state="""open"""		)
												issue.remove_from_labels("""stale"""		)
								elif (
								    (dt.utcnow() - issue.updated_at).days > 23
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()		)
								):
												# Post a Stalebot notification after 23 days of inactivity.
												issue.create_comment(
												    """This issue has been automatically marked as stale because it has not had """
												    """recent activity. If you think this still needs to be addressed """
												    """please comment on this thread.\n\nPlease note that issues that do not follow the """
												    """[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) """
												    """are likely to be ignored."""		)
												issue.add_to_labels("""stale"""		)


if __name__ == "__main__":
	main()

