

'''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__		(						SCREAMING_SNAKE_CASE      :   Tuple						):
  UpperCAmelCase													=	fname.split(os.path.sep						)[-1]
  return re.search(R'^(.*)_\d+\.jpg$'       ,   SCREAMING_SNAKE_CASE						).groups()[0]



class        lowercase_	(		a ):

  '''simple docstring'''





  def __init__(     self   , a_   , a_=None   , a_=None     )							->				int:


    """simple docstring"""
    UpperCAmelCase													=	file_names
    UpperCAmelCase													=	image_transform
    UpperCAmelCase													=	label_to_id
  def __len__(     self     )							->				List[Any]:


    """simple docstring"""
    return len(self.file_names     )





  def __getitem__(     self   , a_     )							->				Tuple:


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




def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Any       ,   SCREAMING_SNAKE_CASE      :   int						):
  # Initialize accelerator
  UpperCAmelCase													=	Accelerator(cpu=args.cpu       ,   mixed_precision=args.mixed_precision						)

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

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

  # Build the label correspondences
  UpperCAmelCase													=	[extract_label(SCREAMING_SNAKE_CASE						) for fname in file_names]
  UpperCAmelCase													=	list(set(SCREAMING_SNAKE_CASE						)						)
  id_to_label.sort()
  UpperCAmelCase													=	{lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE						)}

  # Set the seed before splitting the data.
  np.random.seed(SCREAMING_SNAKE_CASE						)
  torch.manual_seed(SCREAMING_SNAKE_CASE						)
  torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE						)

  # Split our filenames between train and validation
  UpperCAmelCase													=	np.random.permutation(len(SCREAMING_SNAKE_CASE						)						)
  UpperCAmelCase													=	int(0.8 * len(SCREAMING_SNAKE_CASE						)						)
  UpperCAmelCase													=	random_perm[:cut]
  UpperCAmelCase													=	random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  UpperCAmelCase													=	Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE       ,   scale=(0.5, 1.0)						), ToTensor()]						)
  UpperCAmelCase													=	PetsDataset(
      [file_names[i] for i in train_split]       ,   image_transform=SCREAMING_SNAKE_CASE       ,   label_to_id=SCREAMING_SNAKE_CASE						)

  # For evaluation, we use a deterministic Resize
  UpperCAmelCase													=	Compose([Resize(SCREAMING_SNAKE_CASE						), ToTensor()]						)
  UpperCAmelCase													=	PetsDataset([file_names[i] for i in eval_split]       ,   image_transform=SCREAMING_SNAKE_CASE       ,   label_to_id=SCREAMING_SNAKE_CASE						)

  # Instantiate dataloaders.
  UpperCAmelCase													=	DataLoader(SCREAMING_SNAKE_CASE       ,   shuffle=SCREAMING_SNAKE_CASE       ,   batch_size=SCREAMING_SNAKE_CASE       ,   num_workers=4						)
  UpperCAmelCase													=	DataLoader(SCREAMING_SNAKE_CASE       ,   shuffle=SCREAMING_SNAKE_CASE       ,   batch_size=SCREAMING_SNAKE_CASE       ,   num_workers=4						)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  UpperCAmelCase													=	create_model('resnet50d'       ,   pretrained=SCREAMING_SNAKE_CASE       ,   num_classes=len(SCREAMING_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).
  UpperCAmelCase													=	model.to(accelerator.device						)

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

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

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

  # Instantiate learning rate scheduler
  UpperCAmelCase													=	OneCycleLR(optimizer=SCREAMING_SNAKE_CASE       ,   max_lr=SCREAMING_SNAKE_CASE       ,   epochs=SCREAMING_SNAKE_CASE       ,   steps_per_epoch=len(SCREAMING_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.
  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase													=	accelerator.prepare(
      SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

  # Now we train the model
  for epoch in range(SCREAMING_SNAKE_CASE						):
    model.train()
    for step, batch in enumerate(SCREAMING_SNAKE_CASE						):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      UpperCAmelCase													=	{k: v.to(accelerator.device						) for k, v in batch.items()}
      UpperCAmelCase													=	(batch['image'] - mean) / std
      UpperCAmelCase													=	model(SCREAMING_SNAKE_CASE						)
      UpperCAmelCase													=	torch.nn.functional.cross_entropy(SCREAMING_SNAKE_CASE       ,   batch['label']						)
      accelerator.backward(SCREAMING_SNAKE_CASE						)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()

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

    UpperCAmelCase													=	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__		(						):
  UpperCAmelCase													=	argparse.ArgumentParser(description='Simple example of training script.'						)
  parser.add_argument('--data_dir'       ,   required=SCREAMING_SNAKE_CASE       ,   help='The data folder on disk.'						)
  parser.add_argument(
      '--mixed_precision'       ,   type=SCREAMING_SNAKE_CASE       ,   default=SCREAMING_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=SCREAMING_SNAKE_CASE       ,   default=SCREAMING_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.'						)
  UpperCAmelCase													=	parser.parse_args()
  UpperCAmelCase													=	{'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
  training_function(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
  main()





'''simple docstring'''




def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   list[list[int]]						):
  if len(SCREAMING_SNAKE_CASE						) == 0 or not all(len(SCREAMING_SNAKE_CASE						) == 2 for pair in coordinates						):
    raise ValueError('The program cannot work out a fitting polynomial.'						)

  if len({tuple(SCREAMING_SNAKE_CASE						) for pair in coordinates}						) != len(SCREAMING_SNAKE_CASE						):
    raise ValueError('The program cannot work out a fitting polynomial.'						)

  UpperCAmelCase													=	{x for x, _ in coordinates}
  if len(SCREAMING_SNAKE_CASE						) == 1:
    return f'''x={coordinates[0][0]}'''

  if len(SCREAMING_SNAKE_CASE						) != len(SCREAMING_SNAKE_CASE						):
    raise ValueError('The program cannot work out a fitting polynomial.'						)

  UpperCAmelCase													=	len(SCREAMING_SNAKE_CASE						)

  UpperCAmelCase													=	0
  UpperCAmelCase													=	[]
  # put the x and x to the power values in a matrix
  while count_of_line < x:
    UpperCAmelCase													=	0
    UpperCAmelCase													=	coordinates[count_of_line][0]
    UpperCAmelCase													=	[]
    while count_in_line < x:
      count_line.append(a ** (x - (count_in_line + 1))						)
      count_in_line += 1
    matrix.append(SCREAMING_SNAKE_CASE						)
    count_of_line += 1

  UpperCAmelCase													=	0
  # put the y values into a vector
  UpperCAmelCase													=	[]
  while count_of_line < x:
    vector.append(coordinates[count_of_line][1]						)
    count_of_line += 1

  UpperCAmelCase													=	0

  while count < x:
    UpperCAmelCase													=	0
    while zahlen < x:
      if count == zahlen:
        zahlen += 1
      if zahlen == x:
        break
      UpperCAmelCase													=	matrix[zahlen][count] / matrix[count][count]
      for counting_columns, item in enumerate(matrix[count]						):
        # manipulating all the values in the matrix
        matrix[zahlen][counting_columns] -= item * bruch
      # manipulating the values in the vector
      vector[zahlen] -= vector[count] * bruch
      zahlen += 1
    count += 1

  UpperCAmelCase													=	0
  # make solutions
  UpperCAmelCase													=	[]
  while count < x:
    solution.append(str(vector[count] / matrix[count][count]						)						)
    count += 1

  UpperCAmelCase													=	0
  UpperCAmelCase													=	'f(x)='

  while count < x:
    UpperCAmelCase													=	solution[count].split('E'						)
    if len(SCREAMING_SNAKE_CASE						) > 1:
      UpperCAmelCase													=	f'''{remove_e[0]}*10^{remove_e[1]}'''
    solved += f'''x^{x - (count + 1)}*{solution[count]}'''
    if count + 1 != x:
      solved += "+"
    count += 1

  return solved


if __name__ == "__main__":
  print(points_to_polynomial([]))
  print(points_to_polynomial([[]]))
  print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
  print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
  print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
  print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
  print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
  print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
  print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
  print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))


