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 							_lowerCamelCase(				__snake_case	)  ->   Tuple:
  		__snake_case		       =     fname.split(os.path.sep	)[-1]
  		return re.search(r"^(.*)_\d+\.jpg$"						,						__a	).groups()[0]
  
  
  
  
  class 					UpperCamelCase	(     UpperCamelCase__      ):
  
  
  
  
  					def __init__( self :					Optional[int]      ,_lowerCAmelCase :					Union[str, Any]      ,_lowerCAmelCase :					Optional[Any]=None      ,_lowerCAmelCase :					Dict=None						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     file_names
  							__snake_case		       =     image_transform
  							__snake_case		       =     label_to_id
  
  
  
  
  					def __len__( self :					str						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return len(self.file_names						)
  
  
  
  
  
  
  
  					def __getitem__( self :					Union[str, Any]      ,_lowerCAmelCase :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.file_names[idx]
  							__snake_case		       =     PIL.Image.open(snake_case_						)
  							__snake_case		       =     raw_image.convert("RGB"						)
  							if self.image_transform is not None:
  									__snake_case		       =     self.image_transform(snake_case_						)
  							__snake_case		       =     extract_label(snake_case_						)
  							if self.label_to_id is not None:
  									__snake_case		       =     self.label_to_id[label]
  							return {"image": image, "label": label}
  
  
  
  
  def 							_lowerCamelCase(				__snake_case						,						__snake_case	)  ->   Union[str, Any]:
  		__snake_case		       =     Accelerator(cpu=args.cpu						,						mixed_precision=args.mixed_precision	)
  
  		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  		__snake_case		       =     config['''lr''']
  		__snake_case		       =     int(config["num_epochs"]	)
  		__snake_case		       =     int(config["seed"]	)
  		__snake_case		       =     int(config["batch_size"]	)
  		__snake_case		       =     config['''image_size''']
  		if not isinstance(__a						,						(list, tuple)	):
  				__snake_case		       =     (image_size, image_size)
  
  		# Grab all the image filenames
  		__snake_case		       =     [os.path.join(args.data_dir						,						__a	) for fname in os.listdir(args.data_dir	) if fname.endswith(".jpg"	)]
  
  		# Build the label correspondences
  		__snake_case		       =     [extract_label(__a	) for fname in file_names]
  		__snake_case		       =     list(set(__a	)	)
  		id_to_label.sort()
  		__snake_case		       =     {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
  		__snake_case		       =     np.random.permutation(len(__a	)	)
  		__snake_case		       =     int(0.8 * len(__a	)	)
  		__snake_case		       =     random_perm[:cut]
  		__snake_case		       =     random_perm[cut:]
  
  		# For training we use a simple RandomResizedCrop
  		__snake_case		       =     Compose([RandomResizedCrop(__a						,						scale=(0.5, 1.0)	), ToTensor()]	)
  		__snake_case		       =     PetsDataset(
  		    [file_names[i] for i in train_split]						,						image_transform=__a						,						label_to_id=__a	)
  
  		# For evaluation, we use a deterministic Resize
  		__snake_case		       =     Compose([Resize(__a	), ToTensor()]	)
  		__snake_case		       =     PetsDataset([file_names[i] for i in eval_split]						,						image_transform=__a						,						label_to_id=__a	)
  
  		# Instantiate dataloaders.
  		__snake_case		       =     DataLoader(__a						,						shuffle=__a						,						batch_size=__a						,						num_workers=4	)
  		__snake_case		       =     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)
  		__snake_case		       =     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).
  		__snake_case		       =     model.to(accelerator.device	)
  
  		# Freezing the base model
  		for param in model.parameters():
  				__snake_case		       =     False
  		for param in model.get_classifier().parameters():
  				__snake_case		       =     True
  
  		# We normalize the batches of images to be a bit faster.
  		__snake_case		       =     torch.tensor(model.default_cfg["mean"]	)[None, :, None, None].to(accelerator.device	)
  		__snake_case		       =     torch.tensor(model.default_cfg["std"]	)[None, :, None, None].to(accelerator.device	)
  
  		# Instantiate optimizer
  		__snake_case		       =     torch.optim.Adam(params=model.parameters()						,						lr=lr / 25	)
  
  		# Instantiate learning rate scheduler
  		__snake_case		       =     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.
  		__snake_case		       =     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`.
  						__snake_case		       =     {k: v.to(accelerator.device	) for k, v in batch.items()}
  						__snake_case		       =     (batch['''image'''] - mean) / std
  						__snake_case		       =     model(__a	)
  						__snake_case		       =     torch.nn.functional.cross_entropy(__a						,						batch["label"]	)
  						accelerator.backward(__a	)
  						optimizer.step()
  						lr_scheduler.step()
  						optimizer.zero_grad()
  
  				model.eval()
  				__snake_case		       =     0
  				__snake_case		       =     0
  				for _, batch in enumerate(__a	):
  						# We could avoid this line since we set the accelerator with `device_placement=True`.
  						__snake_case		       =     {k: v.to(accelerator.device	) for k, v in batch.items()}
  						__snake_case		       =     (batch['''image'''] - mean) / std
  						with torch.no_grad():
  								__snake_case		       =     model(__a	)
  						__snake_case		       =     outputs.argmax(dim=-1	)
  						__snake_case		       =     accelerator.gather_for_metrics((predictions, batch["label"])	)
  						__snake_case		       =     predictions == references
  						num_elems += accurate_preds.shape[0]
  						accurate += accurate_preds.long().sum()
  
  				__snake_case		       =     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(				)  ->   Optional[Any]:
  		__snake_case		       =     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."	)
  		__snake_case		       =     parser.parse_args()
  		__snake_case		       =     {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
  		training_function(__a						,						__a	)
  
  
  if __name__ == "__main__":
  		main()
  
  
  
  
  
  
style_context: from __future__ import annotations
  
  from collections.abc import Iterable
  from dataclasses import dataclass
  from typing import Any
  
  
  
  
  class 					UpperCamelCase	:
  					__UpperCamelCase						=   ("""_front""", """_back""", """_len""")
  
  
  
  
  					@dataclass
  					class 					UpperCamelCase	:
  										__UpperCamelCase						=   None
  										__UpperCamelCase						=   None
  										__UpperCamelCase						=   None
  
  
  
  
  					class 					UpperCamelCase	:
  										__UpperCamelCase						=   ("""_cur""",)
  
  
  
  
  										def __init__( self :					Dict      ,_lowerCAmelCase :					Deque._Node | None						):
  
  
  												"""simple docstring"""
  
  
  
  
  												__snake_case		       =     cur
  
  
  
  
  										def __iter__( self :					Dict						):
  
  
  												"""simple docstring"""
  
  
  
  
  												return self
  
  
  
  
  
  
  
  										def 							UpperCamelCase_						( self :					Optional[Any]						):
  
  
  												"""simple docstring"""
  
  
  
  
  												if self._cur is None:
  														# finished iterating
  														raise StopIteration
  												__snake_case		       =     self._cur.val
  												__snake_case		       =     self._cur.next_node
  
  												return val
  
  
  
  
  					def __init__( self :					Optional[int]      ,_lowerCAmelCase :					Iterable[Any] | None = None						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     None
  							__snake_case		       =     None
  							__snake_case		       =     0
  
  							if iterable is not None:
  									# append every value to the deque
  									for val in iterable:
  											self.append(_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self._Node(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
  							if self.is_empty():
  									# front = back
  									__snake_case		       =     __snake_case		       =     node
  									__snake_case		       =     1
  							else:
  									# connect nodes
  									__snake_case		       =     node
  									__snake_case		       =     self._back
  									__snake_case		       =     node  # assign new back to the new node
  
  									self._len += 1
  
  									# make sure there were no errors
  									assert not self.is_empty(), "Error on appending value."
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self._Node(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
  							if self.is_empty():
  									# front = back
  									__snake_case		       =     __snake_case		       =     node
  									__snake_case		       =     1
  							else:
  									# connect nodes
  									__snake_case		       =     self._front
  									__snake_case		       =     node
  									__snake_case		       =     node  # assign new front to the new node
  
  									self._len += 1
  
  									# make sure there were no errors
  									assert not self.is_empty(), "Error on appending value."
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Iterable[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for val in iterable:
  									self.append(_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]      ,_lowerCAmelCase :					Iterable[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for val in iterable:
  									self.appendleft(_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							assert not self.is_empty(), "Deque is empty."
  
  							__snake_case		       =     self._back
  							__snake_case		       =     self._back.prev_node  # set new back
  							# drop the last node - python will deallocate memory automatically
  							__snake_case		       =     None
  
  							self._len -= 1
  
  							return topop.val
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							assert not self.is_empty(), "Deque is empty."
  
  							__snake_case		       =     self._front
  							__snake_case		       =     self._front.next_node  # set new front and drop the first node
  							__snake_case		       =     None
  
  							self._len -= 1
  
  							return topop.val
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self._front is None
  
  
  
  
  					def __len__( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self._len
  
  
  
  
  					def __eq__( self :					Union[str, Any]      ,_lowerCAmelCase :					object						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if not isinstance(_lowerCAmelCase      ,_lowerCAmelCase						):
  									return NotImplemented
  
  							__snake_case		       =     self._front
  							__snake_case		       =     other._front
  
  							# if the length of the dequeues are not the same, they are not equal
  							if len(self						) != len(_lowerCAmelCase						):
  									return False
  
  							while me is not None and oth is not None:
  									# compare every value
  									if me.val != oth.val:
  											return False
  									__snake_case		       =     me.next_node
  									__snake_case		       =     oth.next_node
  
  							return True
  
  
  
  
  					def __iter__( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return Deque._Iterator(self._front						)
  
  
  
  
  
  
  
  					def __repr__( self :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     []
  							__snake_case		       =     self._front
  							while aux is not None:
  									# append the values in a list to display
  									values_list.append(aux.val						)
  									__snake_case		       =     aux.next_node
  
  							return F"""[{", ".join(repr(_lowerCAmelCase						) for val in values_list						)}]"""
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  
  
  
  