"""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(		lowerCAmelCase							):
							A__       :     int								=			fname.split(os.path.sep							)[-1]
							return re.search(r"""^(.*)_\d+\.jpg$"""			,  lowerCAmelCase							).groups()[0]
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=None   ,      snake_case_=None							):







										'''simple docstring'''


										A__       :     Dict								=			file_names
										A__       :     str								=			image_transform
										A__       :     Dict								=			label_to_id





			def __len__(			self							):







										'''simple docstring'''


										return len(self.file_names							)





			def __getitem__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.file_names[idx]
										A__       :     Optional[Any]								=			PIL.Image.open(snake_case_							)
										A__       :     str								=			raw_image.convert("""RGB"""							)
										if self.image_transform is not None:
																	A__       :     Optional[int]								=			self.image_transform(snake_case_							)
										A__       :     Dict								=			extract_label(snake_case_							)
										if self.label_to_id is not None:
																	A__       :     List[Any]								=			self.label_to_id[label]
										return {"image": image, "label": label}





def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							# Initialize accelerator
							if args.with_tracking:
														A__       :     List[Any]								=			Accelerator(
														    cpu=args.cpu			,  mixed_precision=args.mixed_precision			,  log_with="""all"""			,  project_dir=args.project_dir							)
							else:
														A__       :     Optional[Any]								=			Accelerator(cpu=args.cpu			,  mixed_precision=args.mixed_precision							)

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

							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps			,  """isdigit"""							):
														if args.checkpointing_steps == "epoch":
																					A__       :     List[Any]								=			args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					A__       :     int								=			int(args.checkpointing_steps							)
														else:
																					raise ValueError(
																					    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
							else:
														A__       :     Any								=			None

							# We need to initialize the trackers we use, and also store our configuration
							if args.with_tracking:
														A__       :     int								=			os.path.split(lowerCAmelCase							)[-1].split("""."""							)[0]
														accelerator.init_trackers(lowerCAmelCase			,  lowerCAmelCase							)

							# Grab all the image filenames
							A__       :     Union[str, Any]								=			[os.path.join(args.data_dir			,  lowerCAmelCase							) for fname in os.listdir(args.data_dir							) if fname.endswith(""".jpg"""							)]

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

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

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

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

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

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

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

							# 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__       :     Tuple								=			model.to(accelerator.device							)

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

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

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

							# Instantiate learning rate scheduler
							A__       :     Optional[int]								=			OneCycleLR(optimizer=lowerCAmelCase			,  max_lr=lowerCAmelCase			,  epochs=lowerCAmelCase			,  steps_per_epoch=len(lowerCAmelCase							)							)

							# 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__       :     Dict								=			accelerator.prepare(
							    lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
							# We need to keep track of how many total steps we have iterated over
							A__       :     str								=			0
							# We also need to keep track of the starting epoch so files are named properly
							A__       :     Union[str, Any]								=			0

							# Potentially load in the weights and states from a previous save
							if args.resume_from_checkpoint:
														if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																					accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''							)
																					accelerator.load_state(args.resume_from_checkpoint							)
																					A__       :     Dict								=			os.path.basename(args.resume_from_checkpoint							)
														else:
																					# Get the most recent checkpoint
																					A__       :     Tuple								=			[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
																					dirs.sort(key=os.path.getctime							)
																					A__       :     Optional[Any]								=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														A__       :     Optional[Any]								=			os.path.splitext(lowerCAmelCase							)[0]

														if "epoch" in training_difference:
																					A__       :     Optional[Any]								=			int(training_difference.replace("""epoch_"""			,  """"""							)							) + 1
																					A__       :     int								=			None
														else:
																					A__       :     Optional[Any]								=			int(training_difference.replace("""step_"""			,  """"""							)							)
																					A__       :     Union[str, Any]								=			resume_step // len(lowerCAmelCase							)
																					resume_step -= starting_epoch * len(lowerCAmelCase							)

    # Now we train the model
							for epoch in range(lowerCAmelCase			,  lowerCAmelCase							):
														model.train()
														if args.with_tracking:
																					A__       :     List[Any]								=			0
														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																					# We need to skip steps until we reach the resumed step
																					A__       :     int								=			accelerator.skip_first_batches(lowerCAmelCase			,  lowerCAmelCase							)
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					A__       :     Optional[int]								=			train_dataloader
														for batch in active_dataloader:
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					A__       :     Union[str, Any]								=			{k: v.to(accelerator.device							) for k, v in batch.items()}
																					A__       :     Any								=			(batch["""image"""] - mean) / std
																					A__       :     Tuple								=			model(lowerCAmelCase							)
																					A__       :     int								=			torch.nn.functional.cross_entropy(lowerCAmelCase			,  batch["""label"""]							)
																					# We keep track of the loss at each epoch
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					accelerator.backward(lowerCAmelCase							)
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()
																					overall_step += 1
																					if isinstance(lowerCAmelCase			,  lowerCAmelCase							):
																												A__       :     Union[str, Any]								=			F'''step_{overall_step}'''
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										A__       :     List[str]								=			os.path.join(args.output_dir			,  lowerCAmelCase							)
																																			accelerator.save_state(lowerCAmelCase							)
														model.eval()
														A__       :     Any								=			0
														A__       :     Tuple								=			0
														for step, batch in enumerate(lowerCAmelCase							):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					A__       :     List[Any]								=			{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(lowerCAmelCase							)
																					A__       :     Tuple								=			outputs.argmax(dim=-1							)
																					A__				,							A__       :     Tuple								=			accelerator.gather_for_metrics((predictions, batch["""label"""])							)
																					A__       :     int								=			predictions == references
																					num_elems += accurate_preds.shape[0]
																					accurate += accurate_preds.long().sum()

														A__       :     List[Any]								=			accurate.item() / num_elems
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''							)
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        """accuracy""": 100 * eval_metric,
																					        """train_loss""": total_loss.item() / len(lowerCAmelCase							),
																					        """epoch""": epoch,
																					    }			,  step=lowerCAmelCase			,  )
														if checkpointing_steps == "epoch":
																					A__       :     Any								=			F'''epoch_{epoch}'''
																					if args.output_dir is not None:
																												A__       :     Any								=			os.path.join(args.output_dir			,  lowerCAmelCase							)
																					accelerator.save_state(lowerCAmelCase							)

							if args.with_tracking:
														accelerator.end_training()

def  _A(		):
							A__       :     Union[str, Any]								=			argparse.ArgumentParser(description="""Simple example of training script."""							)
							parser.add_argument("""--data_dir"""			,  required=lowerCAmelCase			,  help="""The data folder on disk."""							)
							parser.add_argument("""--fp16"""			,  action="""store_true"""			,  help="""If passed, will use FP16 training."""							)
							parser.add_argument(
							    """--mixed_precision"""			,  type=lowerCAmelCase			,  default=lowerCAmelCase			,  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("""--cpu"""			,  action="""store_true"""			,  help="""If passed, will train on the CPU."""							)
							parser.add_argument(
							    """--checkpointing_steps"""			,  type=lowerCAmelCase			,  default=lowerCAmelCase			,  help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""			,  )
							parser.add_argument(
							    """--output_dir"""			,  type=lowerCAmelCase			,  default="""."""			,  help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,  )
							parser.add_argument(
							    """--resume_from_checkpoint"""			,  type=lowerCAmelCase			,  default=lowerCAmelCase			,  help="""If the training should continue from a checkpoint folder."""			,  )
							parser.add_argument(
							    """--with_tracking"""			,  action="""store_true"""			,  help="""Whether to load in all available experiment trackers from the environment and use them for logging."""			,  )
							parser.add_argument(
							    """--project_dir"""			,  type=lowerCAmelCase			,  default="""logs"""			,  help="""Location on where to store experiment tracking logs` and relevent project information"""			,  )
							A__       :     Optional[Any]								=			parser.parse_args()
							A__       :     Any								=			{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
							training_function(lowerCAmelCase			,  lowerCAmelCase							)


if __name__ == "__main__":
				main()






"""simple docstring"""



import math
from collections.abc import Iterator
from itertools import takewhile

def  _A(		lowerCAmelCase							):

							if 1 < number < 4:
														# 2 and 3 are primes
														return True
							elif number < 2 or number % 2 == 0 or number % 3 == 0:
														# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
														return False

							# All primes number are in format of 6k +/- 1
							for i in range(5			,  int(math.sqrt(lowerCAmelCase							) + 1							)			,  6							):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True

def  _A(		):
							A__       :     List[str]								=			2
							while True:
														if is_prime(lowerCAmelCase							):
																					yield num
														num += 1

def  _A(		lowerCAmelCase = 200_0000							):
							return sum(takewhile(lambda lowerCAmelCase							: x < n			,  prime_generator()							)							)


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





