




'''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_    (UpperCamelCase       :  Dict      ):





							'''simple docstring'''


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

class 			A    (		_a    ):







							def __init__(  self       :  Tuple							,			lowerCAmelCase_       :  List[str]							,			lowerCAmelCase_       :  str=None							,			lowerCAmelCase_       :  str=None					)			->							Dict:


														"""simple docstring"""


														_a	     =				file_names
														_a	     =				image_transform
														_a	     =				label_to_id







							def __len__(  self       :  List[Any]					)			->							Union[str, Any]:


														"""simple docstring"""


														return len(self.file_names					)







							def __getitem__(  self       :  Any							,			lowerCAmelCase_       :  Optional[Any]					)			->							Any:


														"""simple docstring"""


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



def 		snake_case_    (UpperCamelCase       :  List[str] ,						UpperCamelCase       :  Union[str, Any]      ):





							'''simple docstring'''


							_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(UpperCamelCase ,						(list, tuple)      ):
														_a	     =				(image_size, image_size)

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

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

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

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

							# Instantiate dataloaders.
							_a	     =				DataLoader(UpperCamelCase ,						shuffle=UpperCamelCase ,						batch_size=UpperCamelCase ,						num_workers=4      )
							_a	     =				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)
							_a	     =				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).
							_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=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.
							_a     ,							_a     ,							_a     ,							_a     ,							_a	     =				accelerator.prepare(
							    UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )

							# Now we train the model
							for epoch in range(UpperCamelCase      ):
														model.train()
														for step, batch in enumerate(UpperCamelCase      ):
																					# 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(UpperCamelCase      )
																					_a	     =				torch.nn.functional.cross_entropy(UpperCamelCase ,						batch['''label''']      )
																					accelerator.backward(UpperCamelCase      )
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()

														model.eval()
														_a	     =				0
														_a	     =				0
														for _, batch in enumerate(UpperCamelCase      ):
																					# 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(UpperCamelCase      )
																					_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}'      )
def 		snake_case_    ():





							'''simple docstring'''


							_a	     =				argparse.ArgumentParser(description='''Simple example of training script.'''      )
							parser.add_argument('''--data_dir''' ,						required=UpperCamelCase ,						help='''The data folder on disk.'''      )
							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(
							    '''--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('''--cpu''' ,						action='''store_true''' ,						help='''If passed, will train on the CPU.'''      )
							_a	     =				parser.parse_args()
							_a	     =				{'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
							training_function(UpperCamelCase ,						UpperCamelCase      )


if __name__ == "__main__":
				main()







'''simple docstring'''


from __future__ import annotations

from enum import Enum

class 			A    (		_a    ):
							lowercase_							=				24
							lowercase_							=				21
							lowercase_							=				18
							lowercase_							=				15
							lowercase_							=				12
							lowercase_							=				9
							lowercase_							=				6
							lowercase_							=				3
							lowercase_							=				2
							lowercase_							=				1
							lowercase_							=				-1
							lowercase_							=				-2
							lowercase_							=				-3
							lowercase_							=				-6
							lowercase_							=				-9
							lowercase_							=				-12
							lowercase_							=				-15
							lowercase_							=				-18
							lowercase_							=				-21
							lowercase_							=				-24



class 			A    (		_a    ):
							lowercase_							=				8
							lowercase_							=				7
							lowercase_							=				6
							lowercase_							=				5
							lowercase_							=				4
							lowercase_							=				3
							lowercase_							=				2
							lowercase_							=				1



def 		snake_case_    (UpperCamelCase       :  float ,						UpperCamelCase       :  str | SIUnit ,						UpperCamelCase       :  str | SIUnit ,						):





							'''simple docstring'''


							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				SIUnit[known_prefix.lower()]
							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				SIUnit[unknown_prefix.lower()]
							_a	     =				known_amount * (
							    10 ** (known_prefix.value - unknown_prefix.value)
							)
							return unknown_amount
def 		snake_case_    (UpperCamelCase       :  float ,						UpperCamelCase       :  str | BinaryUnit ,						UpperCamelCase       :  str | BinaryUnit ,						):





							'''simple docstring'''


							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				BinaryUnit[known_prefix.lower()]
							if isinstance(UpperCamelCase ,						UpperCamelCase      ):
														_a	     =				BinaryUnit[unknown_prefix.lower()]
							_a	     =				known_amount * (
							    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
							)
							return unknown_amount


if __name__ == "__main__":
				import doctest

				doctest.testmod()

