code: 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 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	fname.split(os.path.sep					)[-1]
  							return re.search(r"""^(.*)_\d+\.jpg$""" ,_A					).groups()[0]
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase=None     ,   UpperCAmelCase=None  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	file_names
  														_lowercase				=	image_transform
  														_lowercase				=	label_to_id
  
  
  
  							def __len__(    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return len(self.file_names  )
  
  
  
  							def __getitem__(    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.file_names[idx]
  														_lowercase				=	PIL.Image.open(UpperCAmelCase  )
  														_lowercase				=	raw_image.convert("""RGB"""  )
  														if self.image_transform is not None:
  																					_lowercase				=	self.image_transform(UpperCAmelCase  )
  														_lowercase				=	extract_label(UpperCAmelCase  )
  														if self.label_to_id is not None:
  																					_lowercase				=	self.label_to_id[label]
  														return {"image": image, "label": label}
  
  
  
  
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision					)
  
  							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  							_lowercase				=	config["""lr"""]
  							_lowercase				=	int(config["""num_epochs"""]					)
  							_lowercase				=	int(config["""seed"""]					)
  							_lowercase				=	int(config["""batch_size"""]					)
  							_lowercase				=	config["""image_size"""]
  							if not isinstance(_A ,(list, tuple)					):
  														_lowercase				=	(image_size, image_size)
  
  							# Grab all the image filenames
  							_lowercase				=	[os.path.join(args.data_dir ,_A					) for fname in os.listdir(args.data_dir					) if fname.endswith(""".jpg"""					)]
  
  							# Build the label correspondences
  							_lowercase				=	[extract_label(_A					) for fname in file_names]
  							_lowercase				=	list(set(_A					)					)
  							id_to_label.sort()
  							_lowercase				=	{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
  							_lowercase				=	np.random.permutation(len(_A					)					)
  							_lowercase				=	int(0.8 * len(_A					)					)
  							_lowercase				=	random_perm[:cut]
  							_lowercase				=	random_perm[cut:]
  
  							# For training we use a simple RandomResizedCrop
  							_lowercase				=	Compose([RandomResizedCrop(_A ,scale=(0.5, 1.0)					), ToTensor()]					)
  							_lowercase				=	PetsDataset(
  							    [file_names[i] for i in train_split] ,image_transform=_A ,label_to_id=_A					)
  
  							# For evaluation, we use a deterministic Resize
  							_lowercase				=	Compose([Resize(_A					), ToTensor()]					)
  							_lowercase				=	PetsDataset([file_names[i] for i in eval_split] ,image_transform=_A ,label_to_id=_A					)
  
  							# Instantiate dataloaders.
  							_lowercase				=	DataLoader(_A ,shuffle=_A ,batch_size=_A ,num_workers=4					)
  							_lowercase				=	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)
  							_lowercase				=	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).
  							_lowercase				=	model.to(accelerator.device					)
  
  							# Freezing the base model
  							for param in model.parameters():
  														_lowercase				=	False
  							for param in model.get_classifier().parameters():
  														_lowercase				=	True
  
  							# We normalize the batches of images to be a bit faster.
  							_lowercase				=	torch.tensor(model.default_cfg["""mean"""]					)[None, :, None, None].to(accelerator.device					)
  							_lowercase				=	torch.tensor(model.default_cfg["""std"""]					)[None, :, None, None].to(accelerator.device					)
  
  							# Instantiate optimizer
  							_lowercase				=	torch.optim.Adam(params=model.parameters() ,lr=lr / 25					)
  
  							# Instantiate learning rate scheduler
  							_lowercase				=	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.
  							_lowercase				,       _lowercase				,       _lowercase				,       _lowercase				,       _lowercase				=	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`.
  																					_lowercase				=	{k: v.to(accelerator.device					) for k, v in batch.items()}
  																					_lowercase				=	(batch["""image"""] - mean) / std
  																					_lowercase				=	model(_A					)
  																					_lowercase				=	torch.nn.functional.cross_entropy(_A ,batch["""label"""]					)
  																					accelerator.backward(_A					)
  																					optimizer.step()
  																					lr_scheduler.step()
  																					optimizer.zero_grad()
  
  														model.eval()
  														_lowercase				=	0
  														_lowercase				=	0
  														for _, batch in enumerate(_A					):
  																					# We could avoid this line since we set the accelerator with `device_placement=True`.
  																					_lowercase				=	{k: v.to(accelerator.device					) for k, v in batch.items()}
  																					_lowercase				=	(batch["""image"""] - mean) / std
  																					with torch.no_grad():
  																												_lowercase				=	model(_A					)
  																					_lowercase				=	outputs.argmax(dim=-1					)
  																					_lowercase				,       _lowercase				=	accelerator.gather_for_metrics((predictions, batch["""label"""])					)
  																					_lowercase				=	predictions == references
  																					num_elems += accurate_preds.shape[0]
  																					accurate += accurate_preds.long().sum()
  
  														_lowercase				=	accurate.item() / num_elems
  														# Use accelerator.print to print only on the main process.
  														accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''					)
  
  
  def 					__lowerCAmelCase			(  ):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	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."""					)
  							_lowercase				=	parser.parse_args()
  							_lowercase				=	{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
  							training_function(_A ,_A					)
  
  
  if __name__ == "__main__":
  				main()
  
  
  
style_context: import random
  
  
  class _lowercase					:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase = 0.01     ,   UpperCAmelCase = 1000     ,   UpperCAmelCase = -1     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	sample
  														if len(self.sample  ) == 0:
  																					raise ValueError("""Sample data can not be empty"""  )
  														_lowercase				=	target
  														if len(self.target  ) == 0:
  																					raise ValueError("""Target data can not be empty"""  )
  														if len(self.sample  ) != len(self.target  ):
  																					raise ValueError("""Sample data and Target data do not have matching lengths"""  )
  														_lowercase				=	learning_rate
  														_lowercase				=	epoch_number
  														_lowercase				=	bias
  														_lowercase				=	len(UpperCAmelCase  )
  														_lowercase				=	len(sample[0]  )  # number of columns in dataset
  														_lowercase				=	[]
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														for sample in self.sample:
  																					sample.insert(0     ,   self.bias  )
  
  														for _ in range(self.col_sample  ):
  																					self.weight.append(random.random()  )
  
  														self.weight.insert(0     ,   self.bias  )
  
  														_lowercase				=	0
  
  														while True:
  																					_lowercase				=	False
  																					for i in range(self.number_sample  ):
  																												_lowercase				=	0
  																												for j in range(self.col_sample + 1  ):
  																																			_lowercase				=	u + self.weight[j] * self.sample[i][j]
  																												_lowercase				=	self.sign(UpperCAmelCase  )
  																												if y != self.target[i]:
  																																			for j in range(self.col_sample + 1  ):
  																																										_lowercase				=	(
  																																										    self.weight[j]
  																																										    + self.learning_rate
  																																										    * (self.target[i] - y)
  																																										    * self.sample[i][j]
  																																										)
  																																			_lowercase				=	True
              # print('Epoch: \n',epoch_count)
  																					_lowercase				=	epoch_count + 1
  																					# if you want control the epoch or just by error
  																					if not has_misclassified:
  																												print(("""\nEpoch:\n""", epoch_count)  )
  																												print("""------------------------\n"""  )
  																												# if epoch_count > self.epoch_number or not error:
  																												break
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if len(self.sample  ) == 0:
  																					raise ValueError("""Sample data can not be empty"""  )
  														sample.insert(0     ,   self.bias  )
  														_lowercase				=	0
  														for i in range(self.col_sample + 1  ):
  																					_lowercase				=	u + self.weight[i] * sample[i]
  
  														_lowercase				=	self.sign(UpperCAmelCase  )
  
  														if y == -1:
  																					print(("""Sample: """, sample)  )
  																					print("""classification: P1"""  )
  														else:
  																					print(("""Sample: """, sample)  )
  																					print("""classification: P2"""  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return 1 if u >= 0 else -1
  
  
  
  
  
  
  A_:      Optional[int]        =			[
      [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
      [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
      [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
      [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
      [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
      [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
      [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
      [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
      [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
      [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
      [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
      [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
      [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
      [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
      [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
      [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
      [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
      [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
      [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
      [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
      [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
      [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
      [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
      [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
      [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
      [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
      [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
      [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
      [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
      [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
  ]
  
  A_:      Optional[int]        =			[
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  				A_:      str        =			Perceptron(
  				    sample=samples, target=target, learning_rate=0.0_1, epoch_number=1000, bias=-1
  				)
  				network.training()
  				print('Finished training perceptron')
  				print('Enter values to predict or q to exit')
  				while True:
  								A_:      list        =			[]
  								for i in range(len(samples[0])):
  												A_:      Dict        =			input('value: ').strip()
  												if user_input == "q":
  																break
  												A_:      Union[str, Any]        =			float(user_input)
  												sample.insert(i, observation)
  								network.sort(sample)
  
  
  