






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__   (  __snake_case					:							Dict						):


					'''simple docstring'''


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



class 						lowerCamelCase   (_snake_case    ):







		'''simple docstring'''



		def __init__(							self       ,	_UpperCamelCase       ,	_UpperCamelCase=None       ,	_UpperCamelCase=None     )			->							int:
							UpperCAmelCase_					:   Any   =		file_names
							UpperCAmelCase_					:   Optional[Any]   =		image_transform
							UpperCAmelCase_					:   int   =		label_to_id



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



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





def   lowercase__   (  __snake_case					:							int  , __snake_case					:							List[Any]						):


					'''simple docstring'''


					UpperCAmelCase_					:   Tuple   =		Accelerator(cpu=args.cpu  , mixed_precision=args.mixed_precision						)

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCAmelCase_					:   Dict   =		config['lr']
					UpperCAmelCase_					:   Optional[int]   =		int(config['num_epochs']						)
					UpperCAmelCase_					:   List[str]   =		int(config['seed']						)
					UpperCAmelCase_					:   List[Any]   =		int(config['batch_size']						)
					UpperCAmelCase_					:   Tuple   =		config['image_size']
					if not isinstance(__snake_case  , (list, tuple)						):
										UpperCAmelCase_					:   Optional[Any]   =		(image_size, image_size)

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

					# Build the label correspondences
					UpperCAmelCase_					:   int   =		[extract_label(__snake_case						) for fname in file_names]
					UpperCAmelCase_					:   List[str]   =		list(set(__snake_case						)						)
					id_to_label.sort()
					UpperCAmelCase_					:   str   =		{lbl: i for i, lbl in enumerate(__snake_case						)}

					# Set the seed before splitting the data.
					np.random.seed(__snake_case						)
					torch.manual_seed(__snake_case						)
					torch.cuda.manual_seed_all(__snake_case						)

					# Split our filenames between train and validation
					UpperCAmelCase_					:   Optional[Any]   =		np.random.permutation(len(__snake_case						)						)
					UpperCAmelCase_					:   List[str]   =		int(0.8 * len(__snake_case						)						)
					UpperCAmelCase_					:   List[Any]   =		random_perm[:cut]
					UpperCAmelCase_					:   List[Any]   =		random_perm[cut:]

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

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

					# Instantiate dataloaders.
					UpperCAmelCase_					:   Any   =		DataLoader(__snake_case  , shuffle=__snake_case  , batch_size=__snake_case  , num_workers=4						)
					UpperCAmelCase_					:   Union[str, Any]   =		DataLoader(__snake_case  , shuffle=__snake_case  , batch_size=__snake_case  , num_workers=4						)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCAmelCase_					:   Optional[int]   =		create_model('resnet50d'  , pretrained=__snake_case  , num_classes=len(__snake_case						)						)

					# 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_					:   str   =		model.to(accelerator.device						)

					# Freezing the base model
					for param in model.parameters():
										UpperCAmelCase_					:   List[Any]   =		False
					for param in model.get_classifier().parameters():
										UpperCAmelCase_					:   str   =		True

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

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

					# Instantiate learning rate scheduler
					UpperCAmelCase_					:   Union[str, Any]   =		OneCycleLR(optimizer=__snake_case  , max_lr=__snake_case  , epochs=__snake_case  , steps_per_epoch=len(__snake_case						)						)

					# 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_					:   List[str]   =		accelerator.prepare(
					    __snake_case  , __snake_case  , __snake_case  , __snake_case  , __snake_case						)

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

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

										UpperCAmelCase_					:   str   =		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'''


					UpperCAmelCase_					:   List[str]   =		argparse.ArgumentParser(description='Simple example of training script.'						)
					parser.add_argument('--data_dir'  , required=__snake_case  , help='The data folder on disk.'						)
					parser.add_argument(
					    '--mixed_precision'  , type=__snake_case  , default=__snake_case  , 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=__snake_case  , default=__snake_case  , 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.'						)
					UpperCAmelCase_					:   Any   =		parser.parse_args()
					UpperCAmelCase_					:   str   =		{'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
					training_function(__snake_case  , __snake_case						)


if __name__ == "__main__":
		main()














from __future__ import annotations




def   lowercase__   (  __snake_case					:							Any  , __snake_case					:							int						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[int]   =		0
					UpperCAmelCase_					:   Optional[int]   =		len(__snake_case						) - 1
					while left <= right:
										UpperCAmelCase_					:   Optional[Any]   =		(left + right) // 2
										if left == right:
															if sorted_list[middle] < item:
																				UpperCAmelCase_					:   List[Any]   =		middle + 1
															break
										elif sorted_list[middle] < item:
															UpperCAmelCase_					:   Union[str, Any]   =		middle + 1
										else:
															UpperCAmelCase_					:   Tuple   =		middle - 1
					sorted_list.insert(__snake_case  , __snake_case						)
					return sorted_list




def   lowercase__   (  __snake_case					:							Union[str, Any]  , __snake_case					:							int						):


					'''simple docstring'''


					UpperCAmelCase_					:   str   =		[]
					while left and right:
										if left[0][0] < right[0][0]:
															result.append(left.pop(0						)						)
										else:
															result.append(right.pop(0						)						)
					return result + left + right




def   lowercase__   (  __snake_case					:							Dict						):


					'''simple docstring'''


					UpperCAmelCase_					:   str   =		len(__snake_case						)
					if length <= 1:
										return list_ad
					UpperCAmelCase_					:   List[Any]   =		length // 2
					return merge(sortlist_ad(list_ad[:middle]						)  , sortlist_ad(list_ad[middle:]						)						)




def   lowercase__   (  __snake_case					:							list[int]						):


					'''simple docstring'''


					if len(__snake_case						) <= 1:
										return collection
					UpperCAmelCase_					:   Any   =		[]
					UpperCAmelCase_					:   Union[str, Any]   =		False
					for i in range(0  , len(__snake_case						)  , 2						):
										if i == len(__snake_case						) - 1:
															UpperCAmelCase_					:   Tuple   =		True
										else:
															if collection[i] < collection[i + 1]:
																				two_paired_list.append([collection[i], collection[i + 1]]						)
															else:
																				two_paired_list.append([collection[i + 1], collection[i]]						)
					UpperCAmelCase_					:   Union[str, Any]   =		sortlist_ad(__snake_case						)
					UpperCAmelCase_					:   Any   =		[i[0] for i in sorted_list_ad]
					result.append(sorted_list_ad[-1][1]						)
					if has_last_odd_item:
										UpperCAmelCase_					:   Union[str, Any]   =		collection[-1]
										UpperCAmelCase_					:   List[Any]   =		binary_search_insertion(__snake_case  , __snake_case						)
					UpperCAmelCase_					:   Tuple   =		False
					for i in range(len(__snake_case						) - 1						):
										if result[i] == collection[-1] and has_last_odd_item:
															UpperCAmelCase_					:   List[Any]   =		True
										UpperCAmelCase_					:   Optional[int]   =		sorted_list_ad[i][1]
										# If last_odd_item is inserted before the item's index,
										# you should forward index one more.
										if is_last_odd_item_inserted_before_this_index:
															UpperCAmelCase_					:   List[str]   =		result[: i + 2] + binary_search_insertion(result[i + 2 :]  , __snake_case						)
										else:
															UpperCAmelCase_					:   List[Any]   =		result[: i + 1] + binary_search_insertion(result[i + 1 :]  , __snake_case						)

					return result


if __name__ == "__main__":
		__UpperCAmelCase										=		input('Enter numbers separated by a comma:\n').strip()
		__UpperCAmelCase										=		[int(item) for item in user_input.split(',')]
		print(merge_insertion_sort(unsorted))






