


'''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    lowerCamelCase__   ( A     :    List[Any]  ):




						'''simple docstring'''






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






class  UpperCamelCase__(     lowerCAmelCase ):




				def __init__(	self    :				int   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Tuple=None   ,					lowerCAmelCase    :				List[str]=None   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			file_names
										UpperCAmelCase         =			image_transform
										UpperCAmelCase         =			label_to_id




				def __len__(	self    :				Dict   )->  str:

										"""simple docstring"""




										return len(self.file_names   )





				def __getitem__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				Optional[Any]   )->  int:

										"""simple docstring"""




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







def    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    List[Any]  ):




						'''simple docstring'''






						if args.with_tracking:
												UpperCAmelCase         =			Accelerator(
												    cpu=args.cpu   ,     mixed_precision=args.mixed_precision   ,     log_with='''all'''   ,     project_dir=args.project_dir  )
						else:
												UpperCAmelCase         =			Accelerator(cpu=args.cpu   ,     mixed_precision=args.mixed_precision  )

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

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

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

						# Build the label correspondences
						UpperCAmelCase         =			[extract_label(_lowercase  ) for fname in file_names]
						UpperCAmelCase         =			list(set(_lowercase  )  )
						id_to_label.sort()
						UpperCAmelCase         =			{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
						UpperCAmelCase         =			np.random.permutation(len(_lowercase  )  )
						UpperCAmelCase         =			int(0.8 * len(_lowercase  )  )
						UpperCAmelCase         =			random_perm[:cut]
						UpperCAmelCase         =			random_perm[cut:]

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

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

						# Instantiate dataloaders.
						UpperCAmelCase         =			DataLoader(_lowercase   ,     shuffle=_lowercase   ,     batch_size=_lowercase   ,     num_workers=4  )
						UpperCAmelCase         =			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)
						UpperCAmelCase         =			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).
						UpperCAmelCase         =			model.to(accelerator.device  )

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

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

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

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

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

    # Now we train the model
						for epoch in range(_lowercase   ,     _lowercase  ):
												model.train()
												if args.with_tracking:
																		UpperCAmelCase         =			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
																		UpperCAmelCase         =			accelerator.skip_first_batches(_lowercase   ,     _lowercase  )
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		UpperCAmelCase         =			train_dataloader
												for batch in active_dataloader:
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		UpperCAmelCase         =			{k: v.to(accelerator.device  ) for k, v in batch.items()}
																		UpperCAmelCase         =			(batch['''image'''] - mean) / std
																		UpperCAmelCase         =			model(_lowercase  )
																		UpperCAmelCase         =			torch.nn.functional.cross_entropy(_lowercase   ,     batch['''label''']  )
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(_lowercase  )
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()
																		overall_step += 1
																		if isinstance(_lowercase   ,     _lowercase  ):
																								UpperCAmelCase         =			f"""step_{overall_step}"""
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				UpperCAmelCase         =			os.path.join(args.output_dir   ,     _lowercase  )
																														accelerator.save_state(_lowercase  )
												model.eval()
												UpperCAmelCase         =			0
												UpperCAmelCase         =			0
												for step, batch in enumerate(_lowercase  ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		UpperCAmelCase         =			{k: v.to(accelerator.device  ) for k, v in batch.items()}
																		UpperCAmelCase         =			(batch['''image'''] - mean) / std
																		with torch.no_grad():
																								UpperCAmelCase         =			model(_lowercase  )
																		UpperCAmelCase         =			outputs.argmax(dim=-1  )
																		UpperCAmelCase       ,       UpperCAmelCase         =			accelerator.gather_for_metrics((predictions, batch['''label'''])  )
																		UpperCAmelCase         =			predictions == references
																		num_elems += accurate_preds.shape[0]
																		accurate += accurate_preds.long().sum()

												UpperCAmelCase         =			accurate.item() / num_elems
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"""epoch {epoch}: {1_00 * eval_metric:.2f}"""  )
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''accuracy''': 1_00 * eval_metric,
																		        '''train_loss''': total_loss.item() / len(_lowercase  ),
																		        '''epoch''': epoch,
																		    }   ,     step=_lowercase   ,     )
												if checkpointing_steps == "epoch":
																		UpperCAmelCase         =			f"""epoch_{epoch}"""
																		if args.output_dir is not None:
																								UpperCAmelCase         =			os.path.join(args.output_dir   ,     _lowercase  )
																		accelerator.save_state(_lowercase  )

						if args.with_tracking:
												accelerator.end_training()


def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			argparse.ArgumentParser(description='''Simple example of training script.'''  )
						parser.add_argument('''--data_dir'''   ,     required=_lowercase   ,     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=_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('''--cpu'''   ,     action='''store_true'''   ,     help='''If passed, will train on the CPU.'''  )
						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(
						    '''--output_dir'''   ,     type=_lowercase   ,     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=_lowercase   ,     default=_lowercase   ,     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=_lowercase   ,     default='''logs'''   ,     help='''Location on where to store experiment tracking logs` and relevent project information'''   ,     )
						UpperCAmelCase         =			parser.parse_args()
						UpperCAmelCase         =			{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
						training_function(_lowercase   ,     _lowercase  )


if __name__ == "__main__":
			main()









'''simple docstring'''



from ..utils import DummyObject, requires_backends






class  UpperCamelCase__(     metaclass=lowerCAmelCase ):
				__magic_name__       :   List[str]							= ["note_seq"]




				def __init__(	self    :				Any   ,					*lowerCAmelCase    :				List[str]   ,					**lowerCAmelCase    :				int   )->  Optional[int]:

										"""simple docstring"""




										requires_backends(self   ,					['''note_seq''']   )




				@classmethod
				def   a__(	cls    :				Dict   ,					*lowerCAmelCase    :				int   ,					**lowerCAmelCase    :				Optional[int]   )->  Dict:

										"""simple docstring"""




										requires_backends(cls   ,					['''note_seq''']   )





				@classmethod
				def   a__(	cls    :				int   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				Optional[Any]   )->  Union[str, Any]:

										"""simple docstring"""




										requires_backends(cls   ,					['''note_seq''']   )





