




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

class   UpperCAmelCase_    (			snake_case		):
		def __init__(						self				,						UpperCamelCase_				,						UpperCamelCase_=None				,						UpperCamelCase_=None       )						->       int:
					__lowercase			:  Union[str, Any]								=       file_names
					__lowercase			:  int								=       image_transform
					__lowercase			:  int								=       label_to_id
		def __len__(						self       )						->       Dict:
					return len(self.file_names       )
		def __getitem__(						self				,						UpperCamelCase_       )						->       List[Any]:
					__lowercase			:  Optional[Any]								=       self.file_names[idx]
					__lowercase			:  Tuple								=       PIL.Image.open(UpperCamelCase_       )
					__lowercase			:  Any								=       raw_image.convert('''RGB'''       )
					if self.image_transform is not None:
								__lowercase			:  List[Any]								=       self.image_transform(UpperCamelCase_       )
					__lowercase			:  int								=       extract_label(UpperCamelCase_       )
					if self.label_to_id is not None:
								__lowercase			:  List[Any]								=       self.label_to_id[label]
					return {"image": image, "label": label}







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			# Initialize accelerator
			if args.with_tracking:
						__lowercase			:  int								=       Accelerator(
						    cpu=args.cpu	,				mixed_precision=args.mixed_precision	,				log_with='''all'''	,				project_dir=args.project_dir	)
			else:
						__lowercase			:  Dict								=       Accelerator(cpu=args.cpu	,				mixed_precision=args.mixed_precision	)

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

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

			# Grab all the image filenames
			__lowercase			:  Optional[int]								=       [os.path.join(args.data_dir	,				__UpperCamelCase	) for fname in os.listdir(args.data_dir	) if fname.endswith('''.jpg'''	)]

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

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

			# For evaluation, we use a deterministic Resize
			__lowercase			:  Tuple								=       Compose([Resize(__UpperCamelCase	), ToTensor()]	)
			__lowercase			:  Union[str, Any]								=       PetsDataset([file_names[i] for i in eval_split]	,				image_transform=__UpperCamelCase	,				label_to_id=__UpperCamelCase	)

			# Instantiate dataloaders.
			__lowercase			:  Any								=       DataLoader(__UpperCamelCase	,				shuffle=__UpperCamelCase	,				batch_size=__UpperCamelCase	,				num_workers=4	)
			__lowercase			:  Optional[int]								=       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)
			__lowercase			:  Optional[Any]								=       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).
			__lowercase			:  List[str]								=       model.to(accelerator.device	)

			# Freezing the base model
			for param in model.parameters():
						__lowercase			:  Optional[Any]								=       False
			for param in model.get_classifier().parameters():
						__lowercase			:  Dict								=       True

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

			# Instantiate optimizer
			__lowercase			:  Union[str, Any]								=       torch.optim.Adam(params=model.parameters()	,				lr=lr / 25	)

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

						if "epoch" in training_difference:
									__lowercase			:  Any								=       int(training_difference.replace('''epoch_'''	,				''''''	)	) + 1
									__lowercase			:  Optional[int]								=       None
						else:
									__lowercase			:  Union[str, Any]								=       int(training_difference.replace('''step_'''	,				''''''	)	)
									__lowercase			:  Dict								=       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:
									__lowercase			:  Optional[int]								=       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
									__lowercase			:  str								=       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
									__lowercase			:  Optional[Any]								=       train_dataloader
						for batch in active_dataloader:
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									__lowercase			:  int								=       {k: v.to(accelerator.device	) for k, v in batch.items()}
									__lowercase			:  Dict								=       (batch['''image'''] - mean) / std
									__lowercase			:  List[str]								=       model(__UpperCamelCase	)
									__lowercase			:  str								=       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	):
												__lowercase			:  Union[str, Any]								=       f"""step_{overall_step}"""
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		__lowercase			:  Optional[int]								=       os.path.join(args.output_dir	,				__UpperCamelCase	)
															accelerator.save_state(__UpperCamelCase	)
						model.eval()
						__lowercase			:  List[str]								=       0
						__lowercase			:  Tuple								=       0
						for step, batch in enumerate(__UpperCamelCase	):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									__lowercase			:  int								=       {k: v.to(accelerator.device	) for k, v in batch.items()}
									__lowercase			:  int								=       (batch['''image'''] - mean) / std
									with torch.no_grad():
												__lowercase			:  Dict								=       model(__UpperCamelCase	)
									__lowercase			:  Optional[Any]								=       outputs.argmax(dim=-1	)
									__lowercase			,__lowercase			:  List[str]								=       accelerator.gather_for_metrics((predictions, batch['''label'''])	)
									__lowercase			:  Optional[int]								=       predictions == references
									num_elems += accurate_preds.shape[0]
									accurate += accurate_preds.long().sum()

						__lowercase			:  Tuple								=       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(__UpperCamelCase	),
									        '''epoch''': epoch,
									    }	,				step=__UpperCamelCase	,				)
						if checkpointing_steps == "epoch":
									__lowercase			:  Optional[Any]								=       f"""epoch_{epoch}"""
									if args.output_dir is not None:
												__lowercase			:  Optional[int]								=       os.path.join(args.output_dir	,				__UpperCamelCase	)
									accelerator.save_state(__UpperCamelCase	)

			if args.with_tracking:
						accelerator.end_training()







def      __UpperCAmelCase     ( ):
			__lowercase			:  Any								=       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'''	,				)
			__lowercase			:  Optional[Any]								=       parser.parse_args()
			__lowercase			:  str								=       {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
			training_function(__UpperCamelCase	,				__UpperCamelCase	)


if __name__ == "__main__":
			main()







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


lowerCamelCase		   =			logging.get_logger(__name__)

lowerCamelCase		   =			{
    '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 A    (  UpperCamelCase_	):
      UpperCamelCase__				:    Union[str, Any] ='vit_msn'






      def __init__(   self		:	Union[str, Any]     ,  lowercase_		:	List[str]=768     ,  lowercase_		:	Optional[int]=12     ,  lowercase_		:	List[str]=12     ,  lowercase_		:	List[Any]=3072     ,  lowercase_		:	int="gelu"     ,  lowercase_		:	List[Any]=0.0     ,  lowercase_		:	Optional[int]=0.0     ,  lowercase_		:	Dict=0.02     ,  lowercase_		:	Any=1E-06     ,  lowercase_		:	Union[str, Any]=224     ,  lowercase_		:	Optional[int]=16     ,  lowercase_		:	List[Any]=3     ,  lowercase_		:	Any=True     ,  **lowercase_		:	Tuple     ,  )   ->		Dict:
             """simple docstring"""




             super().__init__(**lowercase_					)

             _lowerCamelCase			:						Dict            =hidden_size
             _lowerCamelCase			:						Any            =num_hidden_layers
             _lowerCamelCase			:						Union[str, Any]            =num_attention_heads
             _lowerCamelCase			:						List[Any]            =intermediate_size
             _lowerCamelCase			:						Tuple            =hidden_act
             _lowerCamelCase			:						Any            =hidden_dropout_prob
             _lowerCamelCase			:						Dict            =attention_probs_dropout_prob
             _lowerCamelCase			:						Any            =initializer_range
             _lowerCamelCase			:						List[Any]            =layer_norm_eps
             _lowerCamelCase			:						Optional[int]            =image_size
             _lowerCamelCase			:						Optional[int]            =patch_size
             _lowerCamelCase			:						List[Any]            =num_channels
             _lowerCamelCase			:						Optional[Any]            =qkv_bias


