

'''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_					(	snake_case	):
  SCREAMING_SNAKE_CASE:int   =   fname.split(os.path.sep	)[-1]
  return re.search(r"^(.*)_\d+\.jpg$" ,    snake_case	).groups()[0]





class 							_snake_case	(    _a  ):
   def __init__(	self     : Union[str, Any]       ,SCREAMING_SNAKE_CASE__     : str       ,SCREAMING_SNAKE_CASE__     : Any=None       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]=None							):
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   file_names
     SCREAMING_SNAKE_CASE:Tuple   =   image_transform
     SCREAMING_SNAKE_CASE:Tuple   =   label_to_id
   def __len__(	self     : Optional[int]							):
     return len(self.file_names							)







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





def 				A_					(	snake_case ,    snake_case	):
  # Initialize accelerator
  SCREAMING_SNAKE_CASE:List[str]   =   Accelerator(cpu=args.cpu ,    mixed_precision=args.mixed_precision	)

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

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

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

  # For training we use a simple RandomResizedCrop
  SCREAMING_SNAKE_CASE:Dict   =   Compose([RandomResizedCrop(snake_case ,    scale=(0.5, 1.0)	), ToTensor()]	)
  SCREAMING_SNAKE_CASE:Optional[Any]   =   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
  SCREAMING_SNAKE_CASE:List[Any]   =   Compose([Resize(snake_case	), ToTensor()]	)
  SCREAMING_SNAKE_CASE:str   =   PetsDataset([file_names[i] for i in eval_split] ,    image_transform=snake_case ,    label_to_id=snake_case	)

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

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

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

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

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

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

    SCREAMING_SNAKE_CASE: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_					(	):
  SCREAMING_SNAKE_CASE:Tuple   =   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."	)
  SCREAMING_SNAKE_CASE:Any   =   parser.parse_args()
  SCREAMING_SNAKE_CASE:Tuple   =   {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
  training_function(snake_case ,    snake_case	)


if __name__ == "__main__":
    main()









'''simple docstring'''

from collections import Counter





def 				A_					(	snake_case	):
  return sum(socks_by_color // 2 for socks_by_color in Counter(snake_case	).values()	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    A_      =							[int(x) for x in input("Enter socks by color :").rstrip().split()]
    print(f'''sock_merchant({colors}) = {sock_merchant(colors)}''')






