




"""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			:		Union[str, Any]						)    ->       Optional[int]:
 _UpperCamelCase      :	List[Any]					      =  fname.split(os.path.sep						)[-1]
 return re.search(R"^(.*)_\d+\.jpg$"  ,	a__						).groups()[0]







class  _SCREAMING_SNAKE_CASE       (     A__				):



 '''simple docstring'''





 def __init__(self					,     lowerCAmelCase__					,     lowerCAmelCase__=None					,     lowerCAmelCase__=None   ):

  '''simple docstring'''







  _UpperCamelCase      :	List[Any]					      =  file_names
  _UpperCamelCase      :	str					      =  image_transform
  _UpperCamelCase      :	int					      =  label_to_id






 def __len__(self   ):

  '''simple docstring'''







  return len(self.file_names   )






 def __getitem__(self					,     lowerCAmelCase__   ):

  '''simple docstring'''







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






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		int  ,	__lowerCAmelCase			:		Union[str, Any]						)    ->       Optional[Any]:
 _UpperCamelCase      :	List[Any]					      =  Accelerator(cpu=args.cpu  ,	mixed_precision=args.mixed_precision						)

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

 # Grab all the image filenames
 _UpperCamelCase      :	Optional[int]					      =  [os.path.join(args.data_dir  ,	a__						) for fname in os.listdir(args.data_dir						) if fname.endswith(".jpg"						)]

 # Build the label correspondences
 _UpperCamelCase      :	int					      =  [extract_label(a__						) for fname in file_names]
 _UpperCamelCase      :	Optional[int]					      =  list(set(a__						)						)
 id_to_label.sort()
 _UpperCamelCase      :	List[str]					      =  {lbl: i for i, lbl in enumerate(a__						)}

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

 # Split our filenames between train and validation
 _UpperCamelCase      :	str					      =  np.random.permutation(len(a__						)						)
 _UpperCamelCase      :	Optional[int]					      =  int(0.8 * len(a__						)						)
 _UpperCamelCase      :	List[str]					      =  random_perm[:cut]
 _UpperCamelCase      :	Any					      =  random_perm[cut:]

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

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

 # Instantiate dataloaders.
 _UpperCamelCase      :	List[str]					      =  DataLoader(a__  ,	shuffle=a__  ,	batch_size=a__  ,	num_workers=4						)
 _UpperCamelCase      :	Optional[int]					      =  DataLoader(a__  ,	shuffle=a__  ,	batch_size=a__  ,	num_workers=4						)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _UpperCamelCase      :	List[Any]					      =  create_model("resnet50d"  ,	pretrained=a__  ,	num_classes=len(a__						)						)

 # 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      :	Dict					      =  model.to(accelerator.device						)

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

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

 # Instantiate optimizer
 _UpperCamelCase      :	Optional[int]					      =  torch.optim.Adam(params=model.parameters()  ,	lr=lr / 25						)

 # Instantiate learning rate scheduler
 _UpperCamelCase      :	List[Any]					      =  OneCycleLR(optimizer=a__  ,	max_lr=a__  ,	epochs=a__  ,	steps_per_epoch=len(a__						)						)

 # 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      :	List[Any]					      =  accelerator.prepare(
     a__  ,	a__  ,	a__  ,	a__  ,	a__						)

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

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

  _UpperCamelCase      :	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 				__lowerCAmelCase					(					)    ->       int:
 _UpperCamelCase      :	Optional[int]					      =  argparse.ArgumentParser(description="Simple example of training script."						)
 parser.add_argument("--data_dir"  ,	required=a__  ,	help="The data folder on disk."						)
 parser.add_argument(
     "--mixed_precision"  ,	type=a__  ,	default=a__  ,	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=a__  ,	default=a__  ,	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      :	List[str]					      =  parser.parse_args()
 _UpperCamelCase      :	Tuple					      =  {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
 training_function(a__  ,	a__						)


if __name__ == "__main__":
    main()





'''simple docstring'''







from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
    import torch

lowerCAmelCase_      =   logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
    from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):







    def   lowerCAmelCase(  cls    :  Dict      ):







           '''simple docstring'''






           return cls







def   lowerCAmelCase(  a__    :  Union[Tuple, List]      , a__    :  Optional[Union[List["torch.Generator"], "torch.Generator"]] = None      , a__    :  Optional["torch.device"] = None      , a__    :  Optional["torch.dtype"] = None      , a__    :  Optional["torch.layout"] = None      , ):







       '''simple docstring'''






       lowerCamelCase__				   =      device
       lowerCamelCase__				   =      shape[0]

       lowerCamelCase__				   =      layout or torch.strided
       lowerCamelCase__				   =      device or torch.device("cpu"      )

       if generator is not None:
              lowerCamelCase__				   =      generator.device.type if not isinstance(a__      , a__      ) else generator[0].device.type
              if gen_device_type != device.type and gen_device_type == "cpu":
                     lowerCamelCase__				   =      "cpu"
                     if device != "mps":
                            logger.info(
                                f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
                                f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
                                f""" slighly speed up this function by passing a generator that was created on the {device} device."""      )
              elif gen_device_type != device.type and gen_device_type == "cuda":
                     raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""      )

       if isinstance(a__      , a__      ):
              lowerCamelCase__				   =      (1,) + shape[1:]
              lowerCamelCase__				   =      [
                  torch.randn(a__      , generator=generator[i]      , device=a__      , dtype=a__      , layout=a__      )
                  for i in range(a__      )
              ]
              lowerCamelCase__				   =      torch.cat(a__      , dim=0      ).to(a__      )
       else:
              lowerCamelCase__				   =      torch.randn(a__      , generator=a__      , device=a__      , dtype=a__      , layout=a__      ).to(a__      )

       return latents







def   lowerCAmelCase(  a__    :  Union[str, Any]      ):







       '''simple docstring'''






       if is_torch_version("<"      , "2.0.0"      ) or not hasattr(a__      , "_dynamo"      ):
              return False
       return isinstance(a__      , torch._dynamo.eval_frame.OptimizedModule      )

