code: 
  
  '''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  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Union[str, Any]		       =   fname.split(os.path.sep			)[-1]
  	return re.search(R"^(.*)_\d+\.jpg$"  ,SCREAMING_SNAKE_CASE			).groups()[0]
  
  class __UpperCamelCase    (							UpperCAmelCase__		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__=None							,			lowerCamelCase__=None	):
  						UpperCAmelCase__:  List[str]		       =   file_names
  						UpperCAmelCase__:  Dict		       =   image_transform
  						UpperCAmelCase__:  Optional[Any]		       =   label_to_id
  
  
  
  
  
  					def __len__(							self	):
  						return len(self.file_names	)
  
  
  
  
  
  					def __getitem__(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Optional[int]		       =   self.file_names[idx]
  						UpperCAmelCase__:  Union[str, Any]		       =   PIL.Image.open(lowerCamelCase__	)
  						UpperCAmelCase__:  Optional[int]		       =   raw_image.convert("RGB"	)
  						if self.image_transform is not None:
  							UpperCAmelCase__:  Tuple		       =   self.image_transform(lowerCamelCase__	)
  						UpperCAmelCase__:  List[Any]		       =   extract_label(lowerCamelCase__	)
  						if self.label_to_id is not None:
  							UpperCAmelCase__:  str		       =   self.label_to_id[label]
  						return {"image": image, "label": label}
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	# Initialize accelerator
  	UpperCAmelCase__:  Dict		       =   Accelerator(cpu=args.cpu  ,mixed_precision=args.mixed_precision			)
  
  	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  	UpperCAmelCase__:  str		       =   config["lr"]
  	UpperCAmelCase__:  Union[str, Any]		       =   int(config["num_epochs"]			)
  	UpperCAmelCase__:  Any		       =   int(config["seed"]			)
  	UpperCAmelCase__:  Dict		       =   int(config["batch_size"]			)
  	UpperCAmelCase__:  Any		       =   config["image_size"]
  	if not isinstance(SCREAMING_SNAKE_CASE  ,(list, tuple)			):
  		UpperCAmelCase__:  Tuple		       =   (image_size, image_size)
  
  	# Grab all the image filenames
  	UpperCAmelCase__:  Tuple		       =   [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__:  List[str]		       =   [extract_label(SCREAMING_SNAKE_CASE			) for fname in file_names]
  	UpperCAmelCase__:  List[Any]		       =   list(set(SCREAMING_SNAKE_CASE			)			)
  	id_to_label.sort()
  	UpperCAmelCase__:  Tuple		       =   {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__:  Union[str, Any]		       =   np.random.permutation(len(SCREAMING_SNAKE_CASE			)			)
  	UpperCAmelCase__:  int		       =   int(0.8 * len(SCREAMING_SNAKE_CASE			)			)
  	UpperCAmelCase__:  Tuple		       =   random_perm[:cut]
  	UpperCAmelCase__:  Any		       =   random_perm[cut:]
  
  	# For training we use a simple RandomResizedCrop
  	UpperCAmelCase__:  Union[str, Any]		       =   Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE  ,scale=(0.5, 1.0)			), ToTensor()]			)
  	UpperCAmelCase__:  Optional[int]		       =   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__:  Union[str, Any]		       =   Compose([Resize(SCREAMING_SNAKE_CASE			), ToTensor()]			)
  	UpperCAmelCase__:  Dict		       =   PetsDataset([file_names[i] for i in eval_split]  ,image_transform=SCREAMING_SNAKE_CASE  ,label_to_id=SCREAMING_SNAKE_CASE			)
  
  	# Instantiate dataloaders.
  	UpperCAmelCase__:  List[Any]		       =   DataLoader(SCREAMING_SNAKE_CASE  ,shuffle=SCREAMING_SNAKE_CASE  ,batch_size=SCREAMING_SNAKE_CASE  ,num_workers=4			)
  	UpperCAmelCase__:  Dict		       =   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__:  Optional[int]		       =   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__:  Union[str, Any]		       =   model.to(accelerator.device			)
  
  	# Freezing the base model
  	for param in model.parameters():
  		UpperCAmelCase__:  Optional[Any]		       =   False
  	for param in model.get_classifier().parameters():
  		UpperCAmelCase__:  Optional[Any]		       =   True
  
  	# We normalize the batches of images to be a bit faster.
  	UpperCAmelCase__:  Dict		       =   torch.tensor(model.default_cfg["mean"]			)[None, :, None, None].to(accelerator.device			)
  	UpperCAmelCase__:  List[str]		       =   torch.tensor(model.default_cfg["std"]			)[None, :, None, None].to(accelerator.device			)
  
  	# Instantiate optimizer
  	UpperCAmelCase__:  Any		       =   torch.optim.Adam(params=model.parameters()  ,lr=lr / 2_5			)
  
  	# Instantiate learning rate scheduler
  	UpperCAmelCase__:  List[Any]		       =   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__:  Dict		       =   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__:  str		       =   {k: v.to(accelerator.device			) for k, v in batch.items()}
  			UpperCAmelCase__:  int		       =   (batch["image"] - mean) / std
  			UpperCAmelCase__:  str		       =   model(SCREAMING_SNAKE_CASE			)
  			UpperCAmelCase__:  int		       =   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__:  Tuple		       =   0
  		UpperCAmelCase__:  Optional[int]		       =   0
  		for _, batch in enumerate(SCREAMING_SNAKE_CASE			):
  			# We could avoid this line since we set the accelerator with `device_placement=True`.
  			UpperCAmelCase__:  List[str]		       =   {k: v.to(accelerator.device			) for k, v in batch.items()}
  			UpperCAmelCase__:  Any		       =   (batch["image"] - mean) / std
  			with torch.no_grad():
  				UpperCAmelCase__:  Optional[int]		       =   model(SCREAMING_SNAKE_CASE			)
  			UpperCAmelCase__:  List[str]		       =   outputs.argmax(dim=-1			)
  			UpperCAmelCase__:  str		       =   accelerator.gather_for_metrics((predictions, batch["label"])			)
  			UpperCAmelCase__:  List[str]		       =   predictions == references
  			num_elems += accurate_preds.shape[0]
  			accurate += accurate_preds.long().sum()
  
  		UpperCAmelCase__:  int		       =   accurate.item() / num_elems
  		# Use accelerator.print to print only on the main process.
  		accelerator.print(f"epoch {epoch}: {1_0_0 * eval_metric:.2f}"			)
  
  
  
  def  _A  (			):
  	UpperCAmelCase__:  Any		       =   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__:  Optional[int]		       =   parser.parse_args()
  	UpperCAmelCase__:  Union[str, Any]		       =   {"lr": 3e-2, "num_epochs": 3, "seed": 4_2, "batch_size": 6_4, "image_size": 2_2_4}
  	training_function(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
  
  
  if __name__ == "__main__":
  					main()
style_context: 
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,):
  	UpperCAmelCase__:  Tuple		       =   list(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  int		       =   list(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Dict		       =   len(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  int		       =   len(SCREAMING_SNAKE_CASE			)
  
  	UpperCAmelCase__:  Any		       =   [
  	    [0 for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
  	]
  	UpperCAmelCase__:  List[Any]		       =   [
  	    ["0" for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
  	]
  
  	for i in range(1  ,len_source_seq + 1			):
  		UpperCAmelCase__:  List[Any]		       =   i * delete_cost
  		UpperCAmelCase__:  str		       =   f"D{source_seq[i - 1]:c}"
  
  	for i in range(1  ,len_destination_seq + 1			):
  		UpperCAmelCase__:  Union[str, Any]		       =   i * insert_cost
  		UpperCAmelCase__:  Optional[Any]		       =   f"I{destination_seq[i - 1]:c}"
  
  	for i in range(1  ,len_source_seq + 1			):
  		for j in range(1  ,len_destination_seq + 1			):
  			if source_seq[i - 1] == destination_seq[j - 1]:
  				UpperCAmelCase__:  Any		       =   costs[i - 1][j - 1] + copy_cost
  				UpperCAmelCase__:  Tuple		       =   f"C{source_seq[i - 1]:c}"
  			else:
  				UpperCAmelCase__:  Union[str, Any]		       =   costs[i - 1][j - 1] + replace_cost
  				UpperCAmelCase__:  Optional[int]		       =   f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]			)
  
  			if costs[i - 1][j] + delete_cost < costs[i][j]:
  				UpperCAmelCase__:  Optional[int]		       =   costs[i - 1][j] + delete_cost
  				UpperCAmelCase__:  int		       =   f"D{source_seq[i - 1]:c}"
  
  			if costs[i][j - 1] + insert_cost < costs[i][j]:
  				UpperCAmelCase__:  Any		       =   costs[i][j - 1] + insert_cost
  				UpperCAmelCase__:  Tuple		       =   f"I{destination_seq[j - 1]:c}"
  
  	return costs, ops
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	if i == 0 and j == 0:
  		return []
  	else:
  		if ops[i][j][0] == "C" or ops[i][j][0] == "R":
  			UpperCAmelCase__:  Union[str, Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,j - 1			)
  			seq.append(ops[i][j]			)
  			return seq
  		elif ops[i][j][0] == "D":
  			UpperCAmelCase__:  Optional[Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,SCREAMING_SNAKE_CASE			)
  			seq.append(ops[i][j]			)
  			return seq
  		else:
  			UpperCAmelCase__:  Dict		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,j - 1			)
  			seq.append(ops[i][j]			)
  			return seq
  
  
  if __name__ == "__main__":
  					_lowerCAmelCase   ,  _lowerCAmelCase     :			Dict    =compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)
  
  					_lowerCAmelCase     :			List[Any]    =len(operations)
  					_lowerCAmelCase     :			Optional[int]    =len(operations[0])
  					_lowerCAmelCase     :			Dict    =assemble_transformation(operations, m - 1, n - 1)
  
  					_lowerCAmelCase     :			int    =list("""Python""")
  					_lowerCAmelCase     :			Optional[int]    =0
  					_lowerCAmelCase     :			List[Any]    =0
  
  					with open("""min_cost.txt""", """w""") as file:
  										for op in sequence:
  															print("""""".join(string))
  
  															if op[0] == "C":
  																				file.write("""%-16s""" % """Copy %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost -= 1
  															elif op[0] == "R":
  																				_lowerCAmelCase     :			Tuple    =op[2]
  
  																				file.write("""%-16s""" % ("""Replace %c""" % op[1] + """ with """ + str(op[2])))
  																				file.write("""\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 1
  															elif op[0] == "D":
  																				string.pop(i)
  
  																				file.write("""%-16s""" % """Delete %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 2
  															else:
  																				string.insert(i, op[1])
  
  																				file.write("""%-16s""" % """Insert %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 2
  
  															i += 1
  
  										print("""""".join(string))
  										print("""Cost: """, cost)
  
  										file.write("""\r\nMinimum cost: """ + str(cost))