

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_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Dict							= fname.split(os.path.sep					)[-1]
						return re.search(R"^(.*)_\d+\.jpg$"       ,			_UpperCAmelCase					).groups()[0]



class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			def __init__(				self						:  Any     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  Union[str, Any]=None     ,		lowerCAmelCase__						:  Any=None):
									SCREAMING_SNAKE_CASE_:				List[Any]							= file_names
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= image_transform
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= label_to_id



			def __len__(				self						:  Union[str, Any]):
									return len(self.file_names)




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







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						# Initialize accelerator
						SCREAMING_SNAKE_CASE_:				Dict							= 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_:				List[Any]							= config["lr"]
						SCREAMING_SNAKE_CASE_:				Optional[int]							= int(config["num_epochs"]					)
						SCREAMING_SNAKE_CASE_:				List[Any]							= int(config["seed"]					)
						SCREAMING_SNAKE_CASE_:				str							= int(config["batch_size"]					)
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= config["image_size"]
						if not isinstance(_UpperCAmelCase       ,			(list, tuple)					):
												SCREAMING_SNAKE_CASE_:				Optional[int]							= (image_size, image_size)

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

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

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

						# Split our filenames between train and validation
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= np.random.permutation(len(_UpperCAmelCase					)					)
						SCREAMING_SNAKE_CASE_:				Dict							= int(0.8 * len(_UpperCAmelCase					)					)
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= random_perm[:cut]
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= random_perm[cut:]

						# For training we use a simple RandomResizedCrop
						SCREAMING_SNAKE_CASE_:				str							= Compose([RandomResizedCrop(_UpperCAmelCase       ,			scale=(0.5, 1.0)					), ToTensor()]					)
						SCREAMING_SNAKE_CASE_:				Dict							= PetsDataset(
						    [file_names[i] for i in train_split]       ,			image_transform=_UpperCAmelCase       ,			label_to_id=_UpperCAmelCase					)

						# For evaluation, we use a deterministic Resize
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= Compose([Resize(_UpperCAmelCase					), ToTensor()]					)
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= PetsDataset([file_names[i] for i in eval_split]       ,			image_transform=_UpperCAmelCase       ,			label_to_id=_UpperCAmelCase					)

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE_:				List[Any]							= DataLoader(_UpperCAmelCase       ,			shuffle=_UpperCAmelCase       ,			batch_size=_UpperCAmelCase       ,			num_workers=4					)
						SCREAMING_SNAKE_CASE_:				str							= DataLoader(_UpperCAmelCase       ,			shuffle=_UpperCAmelCase       ,			batch_size=_UpperCAmelCase       ,			num_workers=4					)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE_:				str							= create_model("resnet50d"       ,			pretrained=_UpperCAmelCase       ,			num_classes=len(_UpperCAmelCase					)					)

						# 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_:				int							= model.to(accelerator.device					)

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

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

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

						# Instantiate learning rate scheduler
						SCREAMING_SNAKE_CASE_:				Tuple							= OneCycleLR(optimizer=_UpperCAmelCase       ,			max_lr=_UpperCAmelCase       ,			epochs=_UpperCAmelCase       ,			steps_per_epoch=len(_UpperCAmelCase					)					)

						# 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_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Optional[int]							= accelerator.prepare(
						    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					)

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

												model.eval()
												SCREAMING_SNAKE_CASE_:				List[Any]							= 0
												SCREAMING_SNAKE_CASE_:				List[Any]							= 0
												for _, batch in enumerate(_UpperCAmelCase					):
																		# 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_:				int							= (batch["image"] - mean) / std
																		with torch.no_grad():
																								SCREAMING_SNAKE_CASE_:				Tuple							= model(_UpperCAmelCase					)
																		SCREAMING_SNAKE_CASE_:				List[Any]							= outputs.argmax(dim=-1					)
																		SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 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_:				Dict							= accurate.item() / num_elems
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}: {1_00 * eval_metric:.2f}"					)







def     A_   (					):
						SCREAMING_SNAKE_CASE_:				Tuple							= argparse.ArgumentParser(description="Simple example of training script."					)
						parser.add_argument("--data_dir"       ,			required=_UpperCAmelCase       ,			help="The data folder on disk."					)
						parser.add_argument(
						    "--mixed_precision"       ,			type=_UpperCAmelCase       ,			default=_UpperCAmelCase       ,			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=_UpperCAmelCase       ,			default=_UpperCAmelCase       ,			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_:				Optional[int]							= parser.parse_args()
						SCREAMING_SNAKE_CASE_:				Any							= {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 2_24}
						training_function(_UpperCAmelCase       ,			_UpperCAmelCase					)


if __name__ == "__main__":
						main()









import heapq







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				List[Any]							= [(0, start)]  # cost from start node,end node
						SCREAMING_SNAKE_CASE_:				Any							= set()
						while heap:
												((SCREAMING_SNAKE_CASE_)	,					(SCREAMING_SNAKE_CASE_)):				Tuple							= heapq.heappop(_UpperCAmelCase					)
												if u in visited:
																		continue
												visited.add(_UpperCAmelCase					)
												if u == end:
																		return cost
												for v, c in graph[u]:
																		if v in visited:
																								continue
																		SCREAMING_SNAKE_CASE_:				List[str]							= cost + c
																		heapq.heappush(_UpperCAmelCase       ,			(next_item, v)					)
						return -1


lowerCAmelCase							:						Optional[Any]     			=		{
    """A""": [["""B""", 2], ["""C""", 5]],
    """B""": [["""A""", 2], ["""D""", 3], ["""E""", 1], ["""F""", 1]],
    """C""": [["""A""", 5], ["""F""", 3]],
    """D""": [["""B""", 3]],
    """E""": [["""B""", 4], ["""F""", 3]],
    """F""": [["""C""", 3], ["""E""", 3]],
}
lowerCAmelCase							:						Tuple     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""F""", 3]],
    """F""": [],
}
lowerCAmelCase							:						str     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(G, """E""", """C""")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

lowerCAmelCase							:						Tuple     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 3 --> F == 3

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
						import doctest

						doctest.testmod()






