






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			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Union[str, Any]				  = fname.split(os.path.sep   )[-1]
						return re.search(R'^(.*)_\d+\.jpg$'    ,			a__   ).groups()[0]




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


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Dict				  = Accelerator(cpu=args.cpu    ,			mixed_precision=args.mixed_precision   )

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

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

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

						# For training we use a simple RandomResizedCrop
						lowerCAmelCase      :Union[str, Any]				  = Compose([RandomResizedCrop(a__    ,			scale=(0.5, 1.0)   ), ToTensor()]   )
						lowerCAmelCase      :Optional[int]				  = PetsDataset(
						    [file_names[i] for i in train_split]    ,			image_transform=a__    ,			label_to_id=a__   )

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

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

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

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

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

						# Instantiate learning rate scheduler
						lowerCAmelCase      :List[Any]				  = 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      :Dict				  = accelerator.prepare(
						    a__    ,			a__    ,			a__    ,			a__    ,			a__   )

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

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

												lowerCAmelCase      :int				  = accurate.item() / num_elems
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}: {1_00 * eval_metric:.2f}"""   )


def 					UpperCAmelCase			(				):





						'''simple docstring'''






						lowerCAmelCase      :Any				  = argparse.ArgumentParser(description='Simple example of training script.'   )
						parser.add_argument('--data_dir'    ,			required=a__    ,			help='The data folder on disk.'   )
						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(
						    '--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('--cpu'    ,			action='store_true'    ,			help='If passed, will train on the CPU.'   )
						lowerCAmelCase      :Optional[int]				  = parser.parse_args()
						lowerCAmelCase      :Union[str, Any]				  = {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 2_24}
						training_function(a__    ,			a__   )


if __name__ == "__main__":
		main()







"""simple docstring"""


from __future__ import annotations


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Union[str, Any]				  = [0] * len(a__   )
						for i in range(1    ,			len(a__   )   ):
												lowerCAmelCase      :Optional[int]				  = duration_times[i - 1] + waiting_times[i - 1]
						return waiting_times


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						return [
						    duration_time + waiting_times[i]
						    for i, duration_time in enumerate(a__   )
						]


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						return sum(a__   ) / len(a__   )


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						return sum(a__   ) / len(a__   )


if __name__ == "__main__":
		# process id's
		__SCREAMING_SNAKE_CASE      =      [1, 2, 3]

		# ensure that we actually have processes
		if len(processes) == 0:
				print('Zero amount of processes')
				raise SystemExit(0)

		# duration time of all processes
		__SCREAMING_SNAKE_CASE      =      [19, 8, 9]

		# ensure we can match each id to a duration time
		if len(duration_times) != len(processes):
				print('Unable to match all id\'s with their duration time')
				raise SystemExit(0)

		# get the waiting times and the turnaround times
		__SCREAMING_SNAKE_CASE      =      calculate_waiting_times(duration_times)
		__SCREAMING_SNAKE_CASE      =      calculate_turnaround_times(duration_times, waiting_times)

		# get the average times
		__SCREAMING_SNAKE_CASE      =      calculate_average_waiting_time(waiting_times)
		__SCREAMING_SNAKE_CASE      =      calculate_average_turnaround_time(turnaround_times)

		# print all the results
		print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
		for i, process in enumerate(processes):
				print(
				    F"""{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"""
				    F"""{turnaround_times[i]}"""
				)
		print(F"""Average waiting time = {average_waiting_time}""")
		print(F"""Average turn around time = {average_turnaround_time}""")