code: import numpy as np
  from PIL import Image
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			np.array:
  							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			np.array:
  							return (gray > 1_2_7) & (gray <= 2_5_5)
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			np.array:
  							lowerCAmelCase				=	np.zeros_like(snake_case__							)
  							lowerCAmelCase				=	np.zeros(
  							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)							)
  
  							# Copy image to padded image
  							lowerCAmelCase				=	image
  
  							# Iterate over image & apply kernel
  							for x in range(image.shape[1]							):
  														for y in range(image.shape[0]							):
  																					lowerCAmelCase				=	(
  																					    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
  																					).sum()
  																					lowerCAmelCase				=	int(summation == 5							)
  							return output
  
  
  # kernel to be applied
  lowercase__    :       Any						=			np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
  
  if __name__ == "__main__":
  	# read original image
  	lowercase__    :       int						=			np.array(Image.open(R'''..\image_data\lena.jpg'''))
  	# Apply erosion operation to a binary image
  	lowercase__    :       List[str]						=			erosion(grayabinary(rgbagray(image)), structuring_element)
  	# Save the output image
  	lowercase__    :       Union[str, Any]						=			Image.fromarray(output).convert('''RGB''')
  	pil_img.save('''result_erosion.png''')
  
  
  
  
style_context: 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      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			Optional[Any]:
  							lowerCAmelCase				=	fname.split(os.path.sep							)[-1]
  							return re.search(R'''^(.*)_\d+\.jpg$'''     ,						snake_case__							).groups()[0]
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
  								lowerCAmelCase				=	file_names
  								lowerCAmelCase				=	image_transform
  								lowerCAmelCase				=	label_to_id
  
  
  	def __len__(							self       )		->Optional[int]:
  								return len(self.file_names       )
  
  
  
  
  	def __getitem__(							self	,    __SCREAMING_SNAKE_CASE       )		->int:
  								lowerCAmelCase				=	self.file_names[idx]
  								lowerCAmelCase				=	PIL.Image.open(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	raw_image.convert('''RGB'''       )
  								if self.image_transform is not None:
  															lowerCAmelCase				=	self.image_transform(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	extract_label(__SCREAMING_SNAKE_CASE       )
  								if self.label_to_id is not None:
  															lowerCAmelCase				=	self.label_to_id[label]
  								return {"image": image, "label": label}
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Any:
  							# Initialize accelerator
  							lowerCAmelCase				=	Accelerator(cpu=args.cpu     ,						mixed_precision=args.mixed_precision							)
  
  							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  							lowerCAmelCase				=	config['''lr''']
  							lowerCAmelCase				=	int(config['''num_epochs''']							)
  							lowerCAmelCase				=	int(config['''seed''']							)
  							lowerCAmelCase				=	int(config['''batch_size''']							)
  							lowerCAmelCase				=	config['''image_size''']
  							if not isinstance(snake_case__     ,						(list, tuple)							):
  														lowerCAmelCase				=	(image_size, image_size)
  
  							# Grab all the image filenames
  							lowerCAmelCase				=	[os.path.join(args.data_dir     ,						snake_case__							) for fname in os.listdir(args.data_dir							) if fname.endswith('''.jpg'''							)]
  
  							# Build the label correspondences
  							lowerCAmelCase				=	[extract_label(snake_case__							) for fname in file_names]
  							lowerCAmelCase				=	list(set(snake_case__							)							)
  							id_to_label.sort()
  							lowerCAmelCase				=	{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
  							lowerCAmelCase				=	np.random.permutation(len(snake_case__							)							)
  							lowerCAmelCase				=	int(0.8 * len(snake_case__							)							)
  							lowerCAmelCase				=	random_perm[:cut]
  							lowerCAmelCase				=	random_perm[cut:]
  
  							# For training we use a simple RandomResizedCrop
  							lowerCAmelCase				=	Compose([RandomResizedCrop(snake_case__     ,						scale=(0.5, 1.0)							), ToTensor()]							)
  							lowerCAmelCase				=	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
  							lowerCAmelCase				=	Compose([Resize(snake_case__							), ToTensor()]							)
  							lowerCAmelCase				=	PetsDataset([file_names[i] for i in eval_split]     ,						image_transform=snake_case__     ,						label_to_id=snake_case__							)
  
  							# Instantiate dataloaders.
  							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)
  							lowerCAmelCase				=	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)
  							lowerCAmelCase				=	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).
  							lowerCAmelCase				=	model.to(accelerator.device							)
  
  							# Freezing the base model
  							for param in model.parameters():
  														lowerCAmelCase				=	False
  							for param in model.get_classifier().parameters():
  														lowerCAmelCase				=	True
  
  							# We normalize the batches of images to be a bit faster.
  							lowerCAmelCase				=	torch.tensor(model.default_cfg['''mean''']							)[None, :, None, None].to(accelerator.device							)
  							lowerCAmelCase				=	torch.tensor(model.default_cfg['''std''']							)[None, :, None, None].to(accelerator.device							)
  
  							# Instantiate optimizer
  							lowerCAmelCase				=	torch.optim.Adam(params=model.parameters()     ,						lr=lr / 2_5							)
  
  							# Instantiate learning rate scheduler
  							lowerCAmelCase				=	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.
  							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	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`.
  																					lowerCAmelCase				=	{k: v.to(accelerator.device							) for k, v in batch.items()}
  																					lowerCAmelCase				=	(batch['''image'''] - mean) / std
  																					lowerCAmelCase				=	model(snake_case__							)
  																					lowerCAmelCase				=	torch.nn.functional.cross_entropy(snake_case__     ,						batch['''label''']							)
  																					accelerator.backward(snake_case__							)
  																					optimizer.step()
  																					lr_scheduler.step()
  																					optimizer.zero_grad()
  
  														model.eval()
  														lowerCAmelCase				=	0
  														lowerCAmelCase				=	0
  														for _, batch in enumerate(snake_case__							):
  																					# We could avoid this line since we set the accelerator with `device_placement=True`.
  																					lowerCAmelCase				=	{k: v.to(accelerator.device							) for k, v in batch.items()}
  																					lowerCAmelCase				=	(batch['''image'''] - mean) / std
  																					with torch.no_grad():
  																												lowerCAmelCase				=	model(snake_case__							)
  																					lowerCAmelCase				=	outputs.argmax(dim=-1							)
  																					lowerCAmelCase	,   lowerCAmelCase				=	accelerator.gather_for_metrics((predictions, batch['''label'''])							)
  																					lowerCAmelCase				=	predictions == references
  																					num_elems += accurate_preds.shape[0]
  																					accurate += accurate_preds.long().sum()
  
  														lowerCAmelCase				=	accurate.item() / num_elems
  														# Use accelerator.print to print only on the main process.
  														accelerator.print(f"epoch {epoch}: {1_0_0 * eval_metric:.2f}"							)
  
  def      SCREAMING_SNAKE_CASE_  (		)  ->			List[str]:
  							lowerCAmelCase				=	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.'''							)
  							lowerCAmelCase				=	parser.parse_args()
  							lowerCAmelCase				=	{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
  							training_function(snake_case__     ,						snake_case__							)
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  