

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      :			Union[str, Any]	)			->     Union[str, Any]:

					"""simple docstring"""

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

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







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


							def __len__(			self       )    ->    Dict:
												return len(self.file_names       )




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



def    __UpperCAmelCase (	__a      :			Dict		,__a      :			Any	)			->     List[str]:

					"""simple docstring"""

					if args.with_tracking:
										_a	:    List[str]         =		Accelerator(
										    cpu=args.cpu		,mixed_precision=args.mixed_precision		,log_with='''all'''		,project_dir=args.project_dir	)
					else:
										_a	:    Tuple         =		Accelerator(cpu=args.cpu		,mixed_precision=args.mixed_precision	)

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_a	:    int         =		config['''lr''']
					_a	:    Union[str, Any]         =		int(config['''num_epochs''']	)
					_a	:    List[Any]         =		int(config['''seed''']	)
					_a	:    Dict         =		int(config['''batch_size''']	)
					_a	:    Union[str, Any]         =		config['''image_size''']
					if not isinstance(__a		,(list, tuple)	):
										_a	:    Any         =		(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":
															_a	:    Tuple         =		args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															_a	:    Any         =		int(args.checkpointing_steps	)
										else:
															raise ValueError(
															    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""	)
					else:
										_a	:    Union[str, Any]         =		None

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										_a	:    List[Any]         =		os.path.split(__a	)[-1].split('''.'''	)[0]
										accelerator.init_trackers(__a		,__a	)

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

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

					# For training we use a simple RandomResizedCrop
					_a	:    Optional[int]         =		Compose([RandomResizedCrop(__a		,scale=(0.5, 1.0)	), ToTensor()]	)
					_a	:    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
					_a	:    Any         =		Compose([Resize(__a	), ToTensor()]	)
					_a	:    Dict         =		PetsDataset([file_names[i] for i in eval_split]		,image_transform=__a		,label_to_id=__a	)

					# Instantiate dataloaders.
					_a	:    int         =		DataLoader(__a		,shuffle=__a		,batch_size=__a		,num_workers=4	)
					_a	:    Optional[Any]         =		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)
					_a	:    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).
					_a	:    Optional[Any]         =		model.to(accelerator.device	)

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

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

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

					# Instantiate learning rate scheduler
					_a	:    List[str]         =		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.
					_a      ,					_a      ,					_a      ,					_a      ,					_a	:    List[Any]         =		accelerator.prepare(
					    __a		,__a		,__a		,__a		,__a	)
					# We need to keep track of how many total steps we have iterated over
					_a	:    int         =		0
					# We also need to keep track of the starting epoch so files are named properly
					_a	:    List[str]         =		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	)
															_a	:    Tuple         =		os.path.basename(args.resume_from_checkpoint	)
										else:
															# Get the most recent checkpoint
															_a	:    List[Any]         =		[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
															dirs.sort(key=os.path.getctime	)
															_a	:    Tuple         =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										_a	:    Optional[Any]         =		os.path.splitext(__a	)[0]

										if "epoch" in training_difference:
															_a	:    List[Any]         =		int(training_difference.replace('''epoch_'''		,''''''	)	) + 1
															_a	:    Dict         =		None
										else:
															_a	:    Tuple         =		int(training_difference.replace('''step_'''		,''''''	)	)
															_a	:    List[str]         =		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:
															_a	:    str         =		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
															_a	:    int         =		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
															_a	:    int         =		train_dataloader
										for batch in active_dataloader:
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															_a	:    str         =		{k: v.to(accelerator.device	) for k, v in batch.items()}
															_a	:    Optional[Any]         =		(batch['''image'''] - mean) / std
															_a	:    str         =		model(__a	)
															_a	:    Optional[int]         =		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	):
																				_a	:    Optional[Any]         =		F"""step_{overall_step}"""
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														_a	:    List[Any]         =		os.path.join(args.output_dir		,__a	)
																									accelerator.save_state(__a	)
										model.eval()
										_a	:    Dict         =		0
										_a	:    Optional[Any]         =		0
										for step, batch in enumerate(__a	):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															_a	:    Tuple         =		{k: v.to(accelerator.device	) for k, v in batch.items()}
															_a	:    List[str]         =		(batch['''image'''] - mean) / std
															with torch.no_grad():
																				_a	:    int         =		model(__a	)
															_a	:    Union[str, Any]         =		outputs.argmax(dim=-1	)
															_a      ,					_a	:    Tuple         =		accelerator.gather_for_metrics((predictions, batch['''label'''])	)
															_a	:    int         =		predictions == references
															num_elems += accurate_preds.shape[0]
															accurate += accurate_preds.long().sum()

										_a	:    Dict         =		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":
															_a	:    List[Any]         =		F"""epoch_{epoch}"""
															if args.output_dir is not None:
																				_a	:    Tuple         =		os.path.join(args.output_dir		,__a	)
															accelerator.save_state(__a	)

					if args.with_tracking:
										accelerator.end_training()



def    __UpperCAmelCase (	)			->     List[str]:

					"""simple docstring"""

					_a	:    int         =		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'''		,)
					_a	:    Any         =		parser.parse_args()
					_a	:    int         =		{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
					training_function(__a		,__a	)


if __name__ == "__main__":
			main()






def    __UpperCAmelCase (	__a      :			float	)			->     float:

					"""simple docstring"""

					return 10 - x * x



def    __UpperCAmelCase (	__a      :			float		,__a      :			float	)			->     float:

					"""simple docstring"""

					if equation(__a	) * equation(__a	) >= 0:
										raise ValueError('''Wrong space!'''	)

					_a	:    Dict         =		a
					while (b - a) >= 0.01:
										# Find middle point
										_a	:    Any         =		(a + b) / 2
										# Check if middle point is root
										if equation(__a	) == 0.0:
															break
										# Decide the side to repeat the steps
										if equation(__a	) * equation(__a	) < 0:
															_a	:    str         =		c
										else:
															_a	:    Union[str, Any]         =		c
					return c


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			print(bisection(-2, 5))
			print(bisection(0, 6))



