


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


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


    def __init__(  self			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							List[Any]      ,							SCREAMING_SNAKE_CASE			:							str=None      ,							SCREAMING_SNAKE_CASE			:							Dict=None):
           _A     :	Optional[Any]      =					file_names
           _A     :	Union[str, Any]      =					image_transform
           _A     :	str      =					label_to_id


    def __len__(  self			:							int):
           return len(self.file_names)


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






def       lowerCAmelCase__       (				lowerCamelCase     :			int  ,lowerCamelCase     :			Optional[int]   ):
       _A     :	Dict      =					Accelerator(cpu=args.cpu  ,mixed_precision=args.mixed_precision   )

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

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

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

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

       # For evaluation, we use a deterministic Resize
       _A     :	int      =					Compose([Resize(lowercase_   ), ToTensor()]   )
       _A     :	Tuple      =					PetsDataset([file_names[i] for i in eval_split]  ,image_transform=lowercase_  ,label_to_id=lowercase_   )

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

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

       # We normalize the batches of images to be a bit faster.
       _A     :	int      =					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     :	Any      =					torch.optim.Adam(params=model.parameters()  ,lr=lr / 25   )

       # Instantiate learning rate scheduler
       _A     :	List[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.
       _A  , _A  , _A  , _A  , _A     :	Any      =					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`.
                     _A     :	Optional[int]      =					{k: v.to(accelerator.device   ) for k, v in batch.items()}
                     _A     :	Tuple      =					(batch['image'] - mean) / std
                     _A     :	Dict      =					model(lowercase_   )
                     _A     :	Any      =					torch.nn.functional.cross_entropy(lowercase_  ,batch['label']   )
                     accelerator.backward(lowercase_   )
                     optimizer.step()
                     lr_scheduler.step()
                     optimizer.zero_grad()

              model.eval()
              _A     :	Any      =					0
              _A     :	Dict      =					0
              for _, batch in enumerate(lowercase_   ):
                     # 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     :	str      =					(batch['image'] - mean) / std
                     with torch.no_grad():
                            _A     :	int      =					model(lowercase_   )
                     _A     :	str      =					outputs.argmax(dim=-1   )
                     _A  , _A     :	List[str]      =					accelerator.gather_for_metrics((predictions, batch['label'])   )
                     _A     :	Tuple      =					predictions == references
                     num_elems += accurate_preds.shape[0]
                     accurate += accurate_preds.long().sum()

              _A     :	Tuple      =					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__       (				):
       _A     :	List[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.'   )
       _A     :	Union[str, Any]      =					parser.parse_args()
       _A     :	Union[str, Any]      =					{'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
       training_function(lowercase_  ,lowercase_   )


if __name__ == "__main__":
   main()












'''simple docstring'''







from __future__ import annotations

from math import pi, pow






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		if side_length < 0:
				raise ValueError("vol_cube() only accepts non-negative values"  )
		return pow(lowercase_						,							3  )






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		if height < 0 or radius < 0:
				raise ValueError("vol_spherical_cap() only accepts non-negative values"  )
		# Volume is 1/3 pi * height squared * (3 * radius - height)
		return 1 / 3 * pi * pow(lowercase_						,							2  ) * (3 * radius - height)






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		if radius_a < 0 or radius_a < 0 or centers_distance < 0:
				raise ValueError("vol_spheres_intersect() only accepts non-negative values"  )
		if centers_distance == 0:
				return vol_sphere(min(lowercase_						,							lowercase_  )  )

		_snake_case	       =       (
		    (radius_a - radius_a + centers_distance)
		    * (radius_a + radius_a - centers_distance)
		    / (2 * centers_distance)
		)
		_snake_case	       =       (
		    (radius_a - radius_a + centers_distance)
		    * (radius_a + radius_a - centers_distance)
		    / (2 * centers_distance)
		)

		return vol_spherical_cap(lowercase_						,							lowercase_  ) + vol_spherical_cap(lowercase_						,							lowercase_  )






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
				raise ValueError(
				    "vol_spheres_union() only accepts non-negative values, non-zero radius"  )

		if centers_distance == 0:
				return vol_sphere(max(lowercase_						,							lowercase_  )  )

		return (
		    vol_sphere(lowercase_  )
		    + vol_sphere(lowercase_  )
		    - vol_spheres_intersect(lowercase_						,							lowercase_						,							lowercase_  )
		)






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		if width < 0 or height < 0 or length < 0:
				raise ValueError("vol_cuboid() only accepts non-negative values"  )
		return float(width * height * length  )






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		if height < 0 or area_of_base < 0:
				raise ValueError("vol_cone() only accepts non-negative values"  )
		return area_of_base * height / 3.0






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		if height < 0 or radius < 0:
				raise ValueError("vol_right_circ_cone() only accepts non-negative values"  )
		return pi * pow(lowercase_						,							2  ) * height / 3.0






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		if height < 0 or area_of_base < 0:
				raise ValueError("vol_prism() only accepts non-negative values"  )
		return float(area_of_base * height  )






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		if height < 0 or area_of_base < 0:
				raise ValueError("vol_pyramid() only accepts non-negative values"  )
		return area_of_base * height / 3.0






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		if radius < 0:
				raise ValueError("vol_sphere() only accepts non-negative values"  )
		# Volume is 4/3 * pi * radius cubed
		return 4 / 3 * pi * pow(lowercase_						,							3  )






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		if radius < 0:
				raise ValueError("vol_hemisphere() only accepts non-negative values"  )
		# Volume is radius cubed * pi * 2/3
		return pow(lowercase_						,							3  ) * pi * 2 / 3






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		if height < 0 or radius < 0:
				raise ValueError("vol_circular_cylinder() only accepts non-negative values"  )
		# Volume is radius squared * height * pi
		return pow(lowercase_						,							2  ) * height * pi






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		if inner_radius < 0 or outer_radius < 0 or height < 0:
				raise ValueError(
				    "vol_hollow_circular_cylinder() only accepts non-negative values"  )
		if outer_radius <= inner_radius:
				raise ValueError("outer_radius must be greater than inner_radius"  )
		return pi * (pow(lowercase_						,							2  ) - pow(lowercase_						,							2  )) * height






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		if radius_a < 0 or radius_a < 0 or height < 0:
				raise ValueError("vol_conical_frustum() only accepts non-negative values"  )
		return (
		    1
		    / 3
		    * pi
		    * height
		    * (pow(lowercase_						,							2  ) + pow(lowercase_						,							2  ) + radius_a * radius_a)
		)






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		if torus_radius < 0 or tube_radius < 0:
				raise ValueError("vol_torus() only accepts non-negative values"  )
		return 2 * pow(lowercase_						,							2  ) * torus_radius * pow(lowercase_						,							2  )






def 		_lowercase		(	):



		'''simple docstring'''







		print("Volumes:"  )
		print(f'''Cube: {vol_cube(2  ) = }'''  )  # = 8
		print(f'''Cuboid: {vol_cuboid(2						,							2						,							2  ) = }'''  )  # = 8
		print(f'''Cone: {vol_cone(2						,							2  ) = }'''  )  # ~= 1.33
		print(f'''Right Circular Cone: {vol_right_circ_cone(2						,							2  ) = }'''  )  # ~= 8.38
		print(f'''Prism: {vol_prism(2						,							2  ) = }'''  )  # = 4
		print(f'''Pyramid: {vol_pyramid(2						,							2  ) = }'''  )  # ~= 1.33
		print(f'''Sphere: {vol_sphere(2  ) = }'''  )  # ~= 33.5
		print(f'''Hemisphere: {vol_hemisphere(2  ) = }'''  )  # ~= 16.75
		print(f'''Circular Cylinder: {vol_circular_cylinder(2						,							2  ) = }'''  )  # ~= 25.1
		print(f'''Torus: {vol_torus(2						,							2  ) = }'''  )  # ~= 157.9
		print(f'''Conical Frustum: {vol_conical_frustum(2						,							2						,							4  ) = }'''  )  # ~= 58.6
		print(f'''Spherical cap: {vol_spherical_cap(1						,							2  ) = }'''  )  # ~= 5.24
		print(f'''Spheres intersetion: {vol_spheres_intersect(2						,							2						,							1  ) = }'''  )  # ~= 21.21
		print(f'''Spheres union: {vol_spheres_union(2						,							2						,							1  ) = }'''  )  # ~= 45.81
		print(
		    f'''Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1						,							2						,							3  ) = }'''  )  # ~= 28.3


if __name__ == "__main__":
						main()





