

"""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   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   fname.split(os.path.sep )[-1]
					return re.search(r"^(.*)_\d+\.jpg$"     ,				lowercase__ ).groups()[0]





class      __UpperCamelCase					(  _A		):


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


		def __len__(self					:						str):
							return len(self.file_names)


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

def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					# Initialize accelerator
					A     =   Accelerator(cpu=args.cpu     ,				mixed_precision=args.mixed_precision )

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

					# Grab all the image filenames
					A     =   [os.path.join(args.data_dir     ,				lowercase__ ) for fname in os.listdir(args.data_dir ) if fname.endswith(".jpg" )]

					# Build the label correspondences
					A     =   [extract_label(lowercase__ ) for fname in file_names]
					A     =   list(set(lowercase__ ) )
					id_to_label.sort()
					A     =   {lbl: i for i, lbl in enumerate(lowercase__ )}

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

					# Split our filenames between train and validation
					A     =   np.random.permutation(len(lowercase__ ) )
					A     =   int(0.8 * len(lowercase__ ) )
					A     =   random_perm[:cut]
					A     =   random_perm[cut:]

					# For training we use a simple RandomResizedCrop
					A     =   Compose([RandomResizedCrop(lowercase__     ,				scale=(0.5, 1.0) ), ToTensor()] )
					A     =   PetsDataset(
					    [file_names[i] for i in train_split]     ,				image_transform=lowercase__     ,				label_to_id=lowercase__ )

					# For evaluation, we use a deterministic Resize
					A     =   Compose([Resize(lowercase__ ), ToTensor()] )
					A     =   PetsDataset([file_names[i] for i in eval_split]     ,				image_transform=lowercase__     ,				label_to_id=lowercase__ )

					# Instantiate dataloaders.
					A     =   DataLoader(lowercase__     ,				shuffle=lowercase__     ,				batch_size=lowercase__     ,				num_workers=4 )
					A     =   DataLoader(lowercase__     ,				shuffle=lowercase__     ,				batch_size=lowercase__     ,				num_workers=4 )

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

					# 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     =   model.to(accelerator.device )

					# Freezing the base model
					for param in model.parameters():
										A     =   False
					for param in model.get_classifier().parameters():
										A     =   True

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

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

					# Instantiate learning rate scheduler
					A     =   OneCycleLR(optimizer=lowercase__     ,				max_lr=lowercase__     ,				epochs=lowercase__     ,				steps_per_epoch=len(lowercase__ ) )

					# 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     ,       A     ,       A     ,       A     ,       A     =   accelerator.prepare(
					    lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ )

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

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

										A     =   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		(		):
					"""simple docstring"""
					A     =   argparse.ArgumentParser(description="Simple example of training script." )
					parser.add_argument("--data_dir"     ,				required=lowercase__     ,				help="The data folder on disk." )
					parser.add_argument(
					    "--mixed_precision"     ,				type=lowercase__     ,				default=lowercase__     ,				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=lowercase__     ,				default=lowercase__     ,				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     =   parser.parse_args()
					A     =   {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
					training_function(lowercase__     ,				lowercase__ )


if __name__ == "__main__":
	main()








"""simple docstring"""




import sys

sys.set_int_max_str_digits(0)  # type: ignore
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   [0] * 11
					A     =   [0] * 11

					# mark last 9 numbers
					for _ in range(9 ):
										A     =   1
										A     =   number // 10
					# flag
					A     =   True

					# check last 9 numbers for pandigitality

					for x in range(9 ):
										if not check_last[x + 1]:
															A     =   False
					if not f:
										return f

					# mark first 9 numbers
					A     =   int(str(lowercase__ )[:9] )

					for _ in range(9 ):
										A     =   1
										A     =   number // 10

					# check first 9 numbers for pandigitality

					for x in range(9 ):
										if not check_front[x + 1]:
															A     =   False
					return f
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   [0] * 11

					# mark last 9 numbers
					for _ in range(9 ):
										A     =   1
										A     =   number // 10
					# flag
					A     =   True

					# check last 9 numbers for pandigitality

					for x in range(9 ):
										if not check_last[x + 1]:
															A     =   False
					return f
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   1
					A     =   1
					A     =   2
					# temporary Fibonacci numbers

					A     =   1
					A     =   1
					A     =   2
					# temporary Fibonacci numbers mod 1e9

					# mod m=1e9, done for fast optimisation
					A     =   [0] * 1_000_000
					A     =   1_000_000_000

					for x in range(1_000_000 ):
										A     =   (aa + ba) % m
										A     =   ba % m
										A     =   ca % m
										if checka(lowercase__ ):
															A     =   1

					for x in range(1_000_000 ):
										A     =   a + b
										A     =   b
										A     =   c
										# perform check only if in tocheck
										if tocheck[x + 3] and check(lowercase__ ):
															return x + 3  # first 2 already done
					return -1


if __name__ == "__main__":
	print(f"""{solution() = }""")





