code: 
  
  
  '''simple docstring'''
  
  
  
  
  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 							a_       (						lowercase    )			->List[str]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      Union[str, Any]											=					fname.split(os.path.sep    )[-1]
  				return re.search(R"^(.*)_\d+\.jpg$"							,						lowercase    ).groups()[0]
  
  
  
  
  
  class _snake_case   ( a__   ):
  							"""simple docstring"""
  
  
  
  
  
  							def __init__(   self			,				__A			,				__A=None			,				__A=None	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[int]											=					file_names
  											__lowerCamelCase		:      List[str]											=					image_transform
  											__lowerCamelCase		:      Any											=					label_to_id
  
  
  
  
  
  
  
  							def __len__(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return len(self.file_names	)
  
  
  
  
  
  
  
  							def __getitem__(   self			,				__A	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Union[str, Any]											=					self.file_names[idx]
  											__lowerCamelCase		:      Any											=					PIL.Image.open(__A	)
  											__lowerCamelCase		:      str											=					raw_image.convert("RGB"	)
  											if self.image_transform is not None:
  															__lowerCamelCase		:      Tuple											=					self.image_transform(__A	)
  											__lowerCamelCase		:      Optional[Any]											=					extract_label(__A	)
  											if self.label_to_id is not None:
  															__lowerCamelCase		:      Optional[int]											=					self.label_to_id[label]
  											return {"image": image, "label": label}
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase    )			->Union[str, Any]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      Tuple											=					Accelerator(cpu=args.cpu							,						mixed_precision=args.mixed_precision    )
  
  				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  				__lowerCamelCase		:      Union[str, Any]											=					config["lr"]
  				__lowerCamelCase		:      Union[str, Any]											=					int(config["num_epochs"]    )
  				__lowerCamelCase		:      Union[str, Any]											=					int(config["seed"]    )
  				__lowerCamelCase		:      List[Any]											=					int(config["batch_size"]    )
  				__lowerCamelCase		:      int											=					config["image_size"]
  				if not isinstance(lowercase							,						(list, tuple)    ):
  								__lowerCamelCase		:      List[Any]											=					(image_size, image_size)
  
  				# Grab all the image filenames
  				__lowerCamelCase		:      int											=					[os.path.join(args.data_dir							,						lowercase    ) for fname in os.listdir(args.data_dir    ) if fname.endswith(".jpg"    )]
  
  				# Build the label correspondences
  				__lowerCamelCase		:      List[str]											=					[extract_label(lowercase    ) for fname in file_names]
  				__lowerCamelCase		:      Optional[Any]											=					list(set(lowercase    )    )
  				id_to_label.sort()
  				__lowerCamelCase		:      Optional[int]											=					{lbl: i for i, lbl in enumerate(lowercase    )}
  
  				# Set the seed before splitting the data.
  				np.random.seed(lowercase    )
  				torch.manual_seed(lowercase    )
  				torch.cuda.manual_seed_all(lowercase    )
  
  				# Split our filenames between train and validation
  				__lowerCamelCase		:      Dict											=					np.random.permutation(len(lowercase    )    )
  				__lowerCamelCase		:      Tuple											=					int(0.8 * len(lowercase    )    )
  				__lowerCamelCase		:      Optional[Any]											=					random_perm[:cut]
  				__lowerCamelCase		:      Union[str, Any]											=					random_perm[cut:]
  
  				# For training we use a simple RandomResizedCrop
  				__lowerCamelCase		:      Union[str, Any]											=					Compose([RandomResizedCrop(lowercase							,						scale=(0.5, 1.0)    ), ToTensor()]    )
  				__lowerCamelCase		:      Union[str, Any]											=					PetsDataset(
  				    [file_names[i] for i in train_split]							,						image_transform=lowercase							,						label_to_id=lowercase    )
  
  				# For evaluation, we use a deterministic Resize
  				__lowerCamelCase		:      Tuple											=					Compose([Resize(lowercase    ), ToTensor()]    )
  				__lowerCamelCase		:      List[Any]											=					PetsDataset([file_names[i] for i in eval_split]							,						image_transform=lowercase							,						label_to_id=lowercase    )
  
  				# Instantiate dataloaders.
  				__lowerCamelCase		:      Any											=					DataLoader(lowercase							,						shuffle=lowercase							,						batch_size=lowercase							,						num_workers=4    )
  				__lowerCamelCase		:      int											=					DataLoader(lowercase							,						shuffle=lowercase							,						batch_size=lowercase							,						num_workers=4    )
  
  				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  				__lowerCamelCase		:      Tuple											=					create_model("resnet50d"							,						pretrained=lowercase							,						num_classes=len(lowercase    )    )
  
  				# 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		:      str											=					model.to(accelerator.device    )
  
  				# Freezing the base model
  				for param in model.parameters():
  								__lowerCamelCase		:      List[str]											=					False
  				for param in model.get_classifier().parameters():
  								__lowerCamelCase		:      int											=					True
  
  				# We normalize the batches of images to be a bit faster.
  				__lowerCamelCase		:      Optional[int]											=					torch.tensor(model.default_cfg["mean"]    )[None, :, None, None].to(accelerator.device    )
  				__lowerCamelCase		:      int											=					torch.tensor(model.default_cfg["std"]    )[None, :, None, None].to(accelerator.device    )
  
  				# Instantiate optimizer
  				__lowerCamelCase		:      str											=					torch.optim.Adam(params=model.parameters()							,						lr=lr / 25    )
  
  				# Instantiate learning rate scheduler
  				__lowerCamelCase		:      Optional[Any]											=					OneCycleLR(optimizer=lowercase							,						max_lr=lowercase							,						epochs=lowercase							,						steps_per_epoch=len(lowercase    )    )
  
  				# 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		:      List[str]											=					accelerator.prepare(
  				    lowercase							,						lowercase							,						lowercase							,						lowercase							,						lowercase    )
  
  				# Now we train the model
  				for epoch in range(lowercase    ):
  								model.train()
  								for step, batch in enumerate(lowercase    ):
  												# We could avoid this line since we set the accelerator with `device_placement=True`.
  												__lowerCamelCase		:      Optional[int]											=					{k: v.to(accelerator.device    ) for k, v in batch.items()}
  												__lowerCamelCase		:      Any											=					(batch["image"] - mean) / std
  												__lowerCamelCase		:      Union[str, Any]											=					model(lowercase    )
  												__lowerCamelCase		:      Union[str, Any]											=					torch.nn.functional.cross_entropy(lowercase							,						batch["label"]    )
  												accelerator.backward(lowercase    )
  												optimizer.step()
  												lr_scheduler.step()
  												optimizer.zero_grad()
  
  								model.eval()
  								__lowerCamelCase		:      int											=					0
  								__lowerCamelCase		:      str											=					0
  								for _, batch in enumerate(lowercase    ):
  												# We could avoid this line since we set the accelerator with `device_placement=True`.
  												__lowerCamelCase		:      Optional[Any]											=					{k: v.to(accelerator.device    ) for k, v in batch.items()}
  												__lowerCamelCase		:      str											=					(batch["image"] - mean) / std
  												with torch.no_grad():
  																__lowerCamelCase		:      str											=					model(lowercase    )
  												__lowerCamelCase		:      Union[str, Any]											=					outputs.argmax(dim=-1    )
  												__lowerCamelCase		:      List[str]											=					accelerator.gather_for_metrics((predictions, batch["label"])    )
  												__lowerCamelCase		:      Union[str, Any]											=					predictions == references
  												num_elems += accurate_preds.shape[0]
  												accurate += accurate_preds.long().sum()
  
  								__lowerCamelCase		:      Optional[int]											=					accurate.item() / num_elems
  								# Use accelerator.print to print only on the main process.
  								accelerator.print(F'epoch {epoch}: {100 * eval_metric:.2f}'    )
  
  
  
  
  
  def 							a_       (						)			->Dict:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      Union[str, Any]											=					argparse.ArgumentParser(description="Simple example of training script."    )
  				parser.add_argument("--data_dir"							,						required=lowercase							,						help="The data folder on disk."    )
  				parser.add_argument(
  				    "--mixed_precision"							,						type=lowercase							,						default=lowercase							,						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=lowercase							,						default=lowercase							,						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		:      Tuple											=					parser.parse_args()
  				__lowerCamelCase		:      str											=					{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
  				training_function(lowercase							,						lowercase    )
  
  
  if __name__ == "__main__":
  				main()
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  from __future__ import annotations
  
  from collections import deque
  from enum import Enum
  from math import atana, degrees
  from sys import maxsize
  
  
  
  
  
  class _snake_case   ( a__   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     1
  							__a												=     2
  							__a												=     3
  
  
  
  
  
  
  
  							def __repr__(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return f'{self.__class__.__name__}.{self.name}'
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->float:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase						,					__lowerCamelCase		:      str											=					point
  				return degrees(atana(y - miny							,						x - minx    )    )
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Direction:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase						,					__lowerCamelCase		:      List[str]											=					starting
  				__lowerCamelCase						,					__lowerCamelCase		:      Any											=					via
  				__lowerCamelCase						,					__lowerCamelCase		:      str											=					target
  				__lowerCamelCase		:      str											=					degrees(atana(ya - ya							,						xa - xa    )    )
  				via_angle %= 360
  				__lowerCamelCase		:      List[Any]											=					degrees(atana(ya - ya							,						xa - xa    )    )
  				target_angle %= 360
  				# t-
  				#  \ \
  				#   \ v
  				#    \|
  				#     s
  				# via_angle is always lower than target_angle, if direction is left.
  				# If they are same, it means they are on a same line of convex hull.
  				if target_angle > via_angle:
  								return Direction.left
  				elif target_angle == via_angle:
  								return Direction.straight
  				else:
  								return Direction.right
  
  
  
  
  
  def 							a_       (						lowercase    )			->list[tuple[int, int]]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				if len(lowercase    ) <= 2:
  								# There is no convex hull
  								raise ValueError("graham_scan: argument must contain more than 3 points."    )
  				if len(lowercase    ) == 3:
  								return points
  				# find the lowest and the most left point
  				__lowerCamelCase		:      Optional[Any]											=					0
  				__lowerCamelCase						,					__lowerCamelCase		:      List[Any]											=					maxsize, maxsize
  				for i, point in enumerate(lowercase    ):
  								__lowerCamelCase		:      Dict											=					point[0]
  								__lowerCamelCase		:      Optional[int]											=					point[1]
  								if y < miny:
  												__lowerCamelCase		:      Dict											=					y
  												__lowerCamelCase		:      int											=					x
  												__lowerCamelCase		:      List[Any]											=					i
  								if y == miny and x < minx:
  												__lowerCamelCase		:      Optional[int]											=					x
  												__lowerCamelCase		:      Any											=					i
  
      # remove the lowest and the most left point from points for preparing for sort
  				points.pop(lowercase    )
  
  				__lowerCamelCase		:      Optional[Any]											=					sorted(lowercase							,						key=lambda lowercase    : angle_comparer(lowercase							,						lowercase							,						lowercase    )    )
  				# This insert actually costs complexity,
  				# and you should instead add (minx, miny) into stack later.
  				# I'm using insert just for easy understanding.
  				sorted_points.insert(0							,						(minx, miny)    )
  
  				__lowerCamelCase		:      deque[tuple[int, int]]											=					deque()
  				stack.append(sorted_points[0]    )
  				stack.append(sorted_points[1]    )
  				stack.append(sorted_points[2]    )
  				# In any ways, the first 3 points line are towards left.
  				# Because we sort them the angle from minx, miny.
  				__lowerCamelCase		:      str											=					Direction.left
  
  				for i in range(3							,						len(lowercase    )    ):
  								while True:
  												__lowerCamelCase		:      List[Any]											=					stack[-2]
  												__lowerCamelCase		:      str											=					stack[-1]
  												__lowerCamelCase		:      int											=					sorted_points[i]
  												__lowerCamelCase		:      List[Any]											=					check_direction(lowercase							,						lowercase							,						lowercase    )
  
  												if next_direction == Direction.left:
  																__lowerCamelCase		:      Optional[int]											=					Direction.left
  																break
  												if next_direction == Direction.straight:
  																if current_direction == Direction.left:
  																				# We keep current_direction as left.
  																				# Because if the straight line keeps as straight,
  																				# we want to know if this straight line is towards left.
  																				break
  																elif current_direction == Direction.right:
  																				# If the straight line is towards right,
  																				# every previous points on those straigh line is not convex hull.
  																				stack.pop()
  												if next_direction == Direction.right:
  																stack.pop()
  								stack.append(sorted_points[i]    )
  				return list(lowercase    )
  
  
  
  
  
  