code: 
  
  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        __SCREAMING_SNAKE_CASE    (      a__						:      int					)				->							Optional[Any]:
  	__A   : Union[str, Any]								= fname.split(os.path.sep					)[-1]
  	return re.search(r"""^(.*)_\d+\.jpg$""" ,a__					).groups()[0]
  
  
  
  
  
  
  class     lowerCamelCase_			(  UpperCAmelCase__  ):
  
  
  
  
  	def __init__(    self	:    str				,			__A	:    Optional[Any]				,			__A	:    Union[str, Any]=None				,			__A	:    Optional[Any]=None       ):
  		__A   : List[str]								= file_names
  		__A   : Dict								= image_transform
  		__A   : Optional[Any]								= label_to_id
  
  
  
  
  	def __len__(    self	:    Tuple       ):
  		return len(self.file_names       )
  
  
  
  
  	def __getitem__(    self	:    Optional[int]				,			__A	:    Optional[int]       ):
  		__A   : Optional[int]								= self.file_names[idx]
  		__A   : Union[str, Any]								= PIL.Image.open(lowerCamelCase__       )
  		__A   : Optional[int]								= raw_image.convert("""RGB"""       )
  		if self.image_transform is not None:
  			__A   : Tuple								= self.image_transform(lowerCamelCase__       )
  		__A   : List[Any]								= extract_label(lowerCamelCase__       )
  		if self.label_to_id is not None:
  			__A   : str								= self.label_to_id[label]
  		return {"image": image, "label": label}
  
  
  
  
  
  
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Dict ,a__						:      Union[str, Any]					)				->							List[Any]:
  	# Initialize accelerator
  	__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   : str								= config["lr"]
  	__A   : Union[str, Any]								= int(config["""num_epochs"""]					)
  	__A   : Any								= int(config["""seed"""]					)
  	__A   : Dict								= int(config["""batch_size"""]					)
  	__A   : Any								= config["image_size"]
  	if not isinstance(a__ ,(list, tuple)					):
  		__A   : Tuple								= (image_size, image_size)
  
  	# Grab all the image filenames
  	__A   : Tuple								= [os.path.join(args.data_dir ,a__					) for fname in os.listdir(args.data_dir					) if fname.endswith(""".jpg"""					)]
  
  	# Build the label correspondences
  	__A   : List[str]								= [extract_label(a__					) for fname in file_names]
  	__A   : List[Any]								= list(set(a__					)					)
  	id_to_label.sort()
  	__A   : Tuple								= {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
  	__A   : Union[str, Any]								= np.random.permutation(len(a__					)					)
  	__A   : int								= int(0.8 * len(a__					)					)
  	__A   : Tuple								= random_perm[:cut]
  	__A   : Any								= random_perm[cut:]
  
  	# For training we use a simple RandomResizedCrop
  	__A   : Union[str, Any]								= Compose([RandomResizedCrop(a__ ,scale=(0.5, 1.0)					), ToTensor()]					)
  	__A   : Optional[int]								= PetsDataset(
  	    [file_names[i] for i in train_split] ,image_transform=a__ ,label_to_id=a__					)
  
  	# For evaluation, we use a deterministic Resize
  	__A   : Union[str, Any]								= Compose([Resize(a__					), ToTensor()]					)
  	__A   : Dict								= PetsDataset([file_names[i] for i in eval_split] ,image_transform=a__ ,label_to_id=a__					)
  
  	# Instantiate dataloaders.
  	__A   : List[Any]								= DataLoader(a__ ,shuffle=a__ ,batch_size=a__ ,num_workers=4					)
  	__A   : Dict								= 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)
  	__A   : Optional[int]								= 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).
  	__A   : Union[str, Any]								= 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   : Optional[Any]								= True
  
  	# We normalize the batches of images to be a bit faster.
  	__A   : Dict								= 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=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.
  	__A   : Dict								= 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`.
  			__A   : str								= {k: v.to(accelerator.device					) for k, v in batch.items()}
  			__A   : int								= (batch["image"] - mean) / std
  			__A   : str								= model(a__					)
  			__A   : int								= torch.nn.functional.cross_entropy(a__ ,batch["""label"""]					)
  			accelerator.backward(a__					)
  			optimizer.step()
  			lr_scheduler.step()
  			optimizer.zero_grad()
  
  		model.eval()
  		__A   : Tuple								= 0
  		__A   : Optional[int]								= 0
  		for _, batch in enumerate(a__					):
  			# We could avoid this line since we set the accelerator with `device_placement=True`.
  			__A   : List[str]								= {k: v.to(accelerator.device					) for k, v in batch.items()}
  			__A   : Any								= (batch["image"] - mean) / std
  			with torch.no_grad():
  				__A   : Optional[int]								= model(a__					)
  			__A   : List[str]								= outputs.argmax(dim=-1					)
  			__A   : str								= accelerator.gather_for_metrics((predictions, batch["""label"""])					)
  			__A   : List[str]								= predictions == references
  			num_elems += accurate_preds.shape[0]
  			accurate += accurate_preds.long().sum()
  
  		__A   : 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        __SCREAMING_SNAKE_CASE    (      )				->							List[Any]:
  	__A   : Any								= 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."""					)
  	__A   : Optional[int]								= parser.parse_args()
  	__A   : Union[str, Any]								= {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
  	training_function(a__ ,a__					)
  
  
  if __name__ == "__main__":
  			main()
  
  
  
  
  
  
style_context: 
  
  import unittest
  from pathlib import Path
  
  from transformers import is_vision_available, load_tool
  from transformers.testing_utils import get_tests_dir
  
  from .test_tools_common import ToolTesterMixin
  
  
  if is_vision_available():
  			from PIL import Image
  
  
  
  
  
  
  class     lowerCamelCase_			(  unittest.TestCase	,			_lowercase  ):
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Dict       ):
  		__A   : str								= load_tool("""image-captioning"""       )
  		self.tool.setup()
  		__A   : Dict								= load_tool("""image-captioning"""				,			remote=__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		__A   : List[Any]								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
  		__A   : Optional[Any]								= self.tool(__A       )
  		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Dict       ):
  		__A   : Any								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
  		__A   : List[str]								= self.remote_tool(__A       )
  		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
  		__A   : Optional[Any]								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
  		__A   : str								= self.tool(image=__A       )
  		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    str       ):
  		__A   : Dict								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
  		__A   : List[Any]								= self.remote_tool(image=__A       )
  		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )
  
  
  
  
  
  