


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_	(					__snake_case      :    List[str]  )->   List[str]:




						'''simple docstring'''
						__magic_name__					=      fname.split(os.path.sep  )[-1]
						return re.search(R'^(.*)_\d+\.jpg$'						,      __snake_case  ).groups()[0]

class      __snake_case				(   _UpperCAmelCase):
					def __init__(						self       :						Any	,      _lowerCAmelCase       :						List[str]	,      _lowerCAmelCase       :						str=None	,      _lowerCAmelCase       :						Dict=None  )							->					int:



											'''simple docstring'''





											__magic_name__					=      file_names
											__magic_name__					=      image_transform
											__magic_name__					=      label_to_id
					def __len__(						self       :						Any  )							->					int:



											'''simple docstring'''





											return len(self.file_names  )







					def __getitem__(						self       :						Tuple	,      _lowerCAmelCase       :						List[str]  )							->					int:



											'''simple docstring'''





											__magic_name__					=      self.file_names[idx]
											__magic_name__					=      PIL.Image.open(_lowerCAmelCase  )
											__magic_name__					=      raw_image.convert('RGB'  )
											if self.image_transform is not None:
																	__magic_name__					=      self.image_transform(_lowerCAmelCase  )
											__magic_name__					=      extract_label(_lowerCAmelCase  )
											if self.label_to_id is not None:
																	__magic_name__					=      self.label_to_id[label]
											return {"image": image, "label": label}







def 				UpperCamelCase_	(					__snake_case      :    List[Any]						,      __snake_case      :    str  )->   Any:




						'''simple docstring'''
						# Initialize accelerator
						__magic_name__					=      Accelerator(cpu=args.cpu						,      mixed_precision=args.mixed_precision  )

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__magic_name__					=      config['lr']
						__magic_name__					=      int(config['num_epochs']  )
						__magic_name__					=      int(config['seed']  )
						__magic_name__					=      int(config['batch_size']  )
						__magic_name__					=      config['image_size']
						if not isinstance(__snake_case						,      (list, tuple)  ):
												__magic_name__					=      (image_size, image_size)

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

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

						# For training we use a simple RandomResizedCrop
						__magic_name__					=      Compose([RandomResizedCrop(__snake_case						,      scale=(0.5, 1.0)  ), ToTensor()]  )
						__magic_name__					=      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
						__magic_name__					=      Compose([Resize(__snake_case  ), ToTensor()]  )
						__magic_name__					=      PetsDataset([file_names[i] for i in eval_split]						,      image_transform=__snake_case						,      label_to_id=__snake_case  )

						# Instantiate dataloaders.
						__magic_name__					=      DataLoader(__snake_case						,      shuffle=__snake_case						,      batch_size=__snake_case						,      num_workers=4  )
						__magic_name__					=      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)
						__magic_name__					=      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).
						__magic_name__					=      model.to(accelerator.device  )

						# Freezing the base model
						for param in model.parameters():
												__magic_name__					=      False
						for param in model.get_classifier().parameters():
												__magic_name__					=      True

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

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

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

												model.eval()
												__magic_name__					=      0
												__magic_name__					=      0
												for _, batch in enumerate(__snake_case  ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		__magic_name__					=      {k: v.to(accelerator.device  ) for k, v in batch.items()}
																		__magic_name__					=      (batch['image'] - mean) / std
																		with torch.no_grad():
																								__magic_name__					=      model(__snake_case  )
																		__magic_name__					=      outputs.argmax(dim=-1  )
																		__magic_name__		,      __magic_name__					=      accelerator.gather_for_metrics((predictions, batch['label'])  )
																		__magic_name__					=      predictions == references
																		num_elems += accurate_preds.shape[0]
																		accurate += accurate_preds.long().sum()

												__magic_name__					=      accurate.item() / num_elems
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}: {1_00 * eval_metric:.2f}'''  )
def 				UpperCamelCase_	(					)->   int:




						'''simple docstring'''
						__magic_name__					=      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.'  )
						__magic_name__					=      parser.parse_args()
						__magic_name__					=      {'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 2_24}
						training_function(__snake_case						,      __snake_case  )


if __name__ == "__main__":
					main()





from itertools import count
from math import asin, pi, sqrt
def 				UpperCamelCase_	(					__snake_case      :    float  )->   float:




						'''simple docstring'''
						return (
						    (1 - 2 * point) * sqrt(point - point**2  ) + 2 * point + asin(sqrt(1 - point  )  )
						) / 4
def 				UpperCamelCase_	(					__snake_case      :    int  )->   float:




						'''simple docstring'''
						__magic_name__					=      (circles_number + 1 - sqrt(2 * circles_number  )) / (
						    2 * (circles_number**2 + 1)
						)
						__magic_name__					=      circles_number * intersection_y

						__magic_name__					=      intersection_x * intersection_y / 2
						__magic_name__					=      circle_bottom_arc_integral(
						    1 / 2  ) - circle_bottom_arc_integral(__snake_case  )

						return triangle_area + concave_region_area
def 				UpperCamelCase_	(					__snake_case      :    float = 1 / 10_00  )->   int:




						'''simple docstring'''
						__magic_name__					=      (1 - pi / 4) / 4

						for n in count(1  ):
												if concave_triangle_area(__snake_case  ) / l_section_area < fraction:
																		return n

						return -1


if __name__ == "__main__":
					print(f'''{solution() = }''')

