



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







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""






						def __init__(					self :List[str]   ,  lowercase_ :Dict   ,  lowercase_ :List[str]=None   ,  lowercase_ :Optional[Any]=None					)				->			Optional[int]:
												UpperCAmelCase             = file_names
												UpperCAmelCase             = image_transform
												UpperCAmelCase             = label_to_id






						def __len__(					self :Optional[int]					)				->			Optional[Any]:
												return len(self.file_names					)






						def __getitem__(					self :int   ,  lowercase_ :str					)				->			List[str]:
												UpperCAmelCase             = self.file_names[idx]
												UpperCAmelCase             = PIL.Image.open(lowercase_					)
												UpperCAmelCase             = raw_image.convert('RGB'					)
												if self.image_transform is not None:
																		UpperCAmelCase             = self.image_transform(lowercase_					)
												UpperCAmelCase             = extract_label(lowercase_					)
												if self.label_to_id is not None:
																		UpperCAmelCase             = self.label_to_id[label]
												return {"image": image, "label": label}


def  _lowerCAmelCase      (   lowercase_		,	lowercase_						):
						# Initialize accelerator
						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}: {100 * eval_metric:.2f}"""						)
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        'accuracy': 100 * 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      (   ):
						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': 224}
						training_function(lowercase_		,	lowercase_						)


if __name__ == "__main__":
				main()












from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[Any]							=    'vit_msn'


      def __init__(self       :      Union[str, Any]				,			a__       :      Optional[Any]=768				,			a__       :      Optional[Any]=12				,			a__       :      Optional[int]=12				,			a__       :      Optional[int]=3072				,			a__       :      Union[str, Any]="gelu"				,			a__       :      str=0.0				,			a__       :      int=0.0				,			a__       :      Optional[Any]=0.0_2				,			a__       :      List[Any]=1E-06				,			a__       :      Optional[int]=224				,			a__       :      str=16				,			a__       :      Optional[Any]=3				,			a__       :      int=True				,			**a__       :      List[Any]				,			):





         """simple docstring"""



         super().__init__(**a__      )

         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   initializer_range
         __snake_case							       =   layer_norm_eps
         __snake_case							       =   image_size
         __snake_case							       =   patch_size
         __snake_case							       =   num_channels
         __snake_case							       =   qkv_bias

