




def lowerCamelCase__   (			_lowercase      ):

     '''simple docstring'''


     for insert_index, insert_value in enumerate(collection[1:]      ):
          UpperCAmelCase_			:							Union[str, Any]   =			insert_index
          while insert_index >= 0 and insert_value < collection[insert_index]:
               UpperCAmelCase_			:							Optional[Any]   =			collection[insert_index]
               insert_index -= 1
          if insert_index != temp_index:
               UpperCAmelCase_			:							int   =			insert_value
     return collection


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     __a							= input('Enter numbers separated by a comma:\n').strip()
     __a							= [int(item) for item in user_input.split(',')]
     print(F"""{insertion_sort(unsorted) = }""")





'''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 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







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





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase=None,  __UpperCamelCase=None ):
							_snake_case	       =       file_names
							_snake_case	       =       image_transform
							_snake_case	       =       label_to_id






					def __len__(    self ):
							return len(self.file_names )






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






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       Accelerator(cpu=args.cpu						,							mixed_precision=args.mixed_precision  )

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		_snake_case	       =       config["lr"]
		_snake_case	       =       int(config["num_epochs"]  )
		_snake_case	       =       int(config["seed"]  )
		_snake_case	       =       int(config["batch_size"]  )
		_snake_case	       =       config["image_size"]
		if not isinstance(lowercase_						,							(list, tuple)  ):
				_snake_case	       =       (image_size, image_size)

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

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

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

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

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

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

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

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

		# Instantiate learning rate scheduler
		_snake_case	       =       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.
		_snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case	       =       accelerator.prepare(
		    lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  )

		# Now we train the model
		for epoch in range(lowercase_  ):
				model.train()
				for step, batch in enumerate(lowercase_  ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						_snake_case	       =       {k: v.to(accelerator.device  ) for k, v in batch.items()}
						_snake_case	       =       (batch["image"] - mean) / std
						_snake_case	       =       model(lowercase_  )
						_snake_case	       =       torch.nn.functional.cross_entropy(lowercase_						,							batch["label"]  )
						accelerator.backward(lowercase_  )
						optimizer.step()
						lr_scheduler.step()
						optimizer.zero_grad()

				model.eval()
				_snake_case	       =       0
				_snake_case	       =       0
				for _, batch in enumerate(lowercase_  ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						_snake_case	       =       {k: v.to(accelerator.device  ) for k, v in batch.items()}
						_snake_case	       =       (batch["image"] - mean) / std
						with torch.no_grad():
								_snake_case	       =       model(lowercase_  )
						_snake_case	       =       outputs.argmax(dim=-1  )
						_snake_case       ,    _snake_case	       =       accelerator.gather_for_metrics((predictions, batch["label"])  )
						_snake_case	       =       predictions == references
						num_elems += accurate_preds.shape[0]
						accurate += accurate_preds.long().sum()

				_snake_case	       =       accurate.item() / num_elems
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''  )






def 		_lowercase		(	):



		'''simple docstring'''







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


if __name__ == "__main__":
						main()





