


'''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       _snake_case     (  A )		->					List[str]:
							lowerCAmelCase__      =			fname.split(os.path.sep )[-1]
							return re.search(R'''^(.*)_\d+\.jpg$'''				,					A ).groups()[0]




class 	a__       (							a__						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=None      ,					lowerCamelCase_=None     ) ->							List[str]:
														lowerCAmelCase__      =			file_names
														lowerCAmelCase__      =			image_transform
														lowerCAmelCase__      =			label_to_id







							def __len__(	self     ) ->							List[str]:
														return len(self.file_names     )







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


def       _snake_case     (  A				,					A )		->					int:
							# Initialize accelerator
							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(A				,					(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(A )[-1].split('''.''' )[0]
														accelerator.init_trackers(A				,					A )

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

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

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

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

							# Instantiate dataloaders.
							lowerCAmelCase__      =			DataLoader(A				,					shuffle=A				,					batch_size=A				,					num_workers=4 )
							lowerCAmelCase__      =			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)
							lowerCAmelCase__      =			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).
							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=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.
							lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__      =			accelerator.prepare(
							    A				,					A				,					A				,					A				,					A )
							# 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(A )[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(A )
																					resume_step -= starting_epoch * len(A )

    # Now we train the model
							for epoch in range(A				,					A ):
														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(A				,					A )
																					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(A )
																					lowerCAmelCase__      =			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 ):
																												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				,					A )
																																			accelerator.save_state(A )
														model.eval()
														lowerCAmelCase__      =			0
														lowerCAmelCase__      =			0
														for step, batch in enumerate(A ):
																					# 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(A )
																					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(A ),
																					        '''epoch''': epoch,
																					    }				,					step=A				,					)
														if checkpointing_steps == "epoch":
																					lowerCAmelCase__      =			F"""epoch_{epoch}"""
																					if args.output_dir is not None:
																												lowerCAmelCase__      =			os.path.join(args.output_dir				,					A )
																					accelerator.save_state(A )

							if args.with_tracking:
														accelerator.end_training()


def       _snake_case     (  )		->					int:
							lowerCAmelCase__      =			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'''				,					)
							lowerCAmelCase__      =			parser.parse_args()
							lowerCAmelCase__      =			{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
							training_function(A				,					A )


if __name__ == "__main__":
		main()



'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase					       =							{
    '''configuration_poolformer''': [
        '''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''PoolFormerConfig''',
        '''PoolFormerOnnxConfig''',
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase					       =							['''PoolFormerFeatureExtractor''']
		__UpperCAmelCase					       =							['''PoolFormerImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase					       =							[
		    '''POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PoolFormerForImageClassification''',
		    '''PoolFormerModel''',
		    '''PoolFormerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_poolformer import (
		    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    PoolFormerConfig,
		    PoolFormerOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_poolformer import PoolFormerFeatureExtractor
				from .image_processing_poolformer import PoolFormerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_poolformer import (
				    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PoolFormerForImageClassification,
				    PoolFormerModel,
				    PoolFormerPreTrainedModel,
				)


else:
		import sys

		__UpperCAmelCase					       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure)