import subprocess
import sys
subprocess.check_call((sys.executable, "-m", "pip", "set up", "-q", "hydra-core"))


import hydra
from hydra import compose, initialize_config_dir
from omegaconf import OmegaConf, DictConfig
from dataclasses import dataclass, subject
from typing import Record, Non-obligatory
import os
from pathlib import Path

@dataclass
class OptimizerConfig:
   _target_: str = "torch.optim.SGD"
   lr: float = 0.01
  
@dataclass
class AdamConfig(OptimizerConfig):
   _target_: str = "torch.optim.Adam"
   lr: float = 0.001
   betas: tuple = (0.9, 0.999)
   weight_decay: float = 0.0


@dataclass
class SGDConfig(OptimizerConfig):
   _target_: str = "torch.optim.SGD"
   lr: float = 0.01
   momentum: float = 0.9
   nesterov: bool = True


@dataclass
class ModelConfig:
   identify: str = "resnet"
   num_layers: int = 50
   hidden_dim: int = 512
   dropout: float = 0.1


@dataclass
class DataConfig:
   dataset: str = "cifar10"
   batch_size: int = 32
   num_workers: int = 4
   augmentation: bool = True


@dataclass
class TrainingConfig:
   mannequin: ModelConfig = subject(default_factory=ModelConfig)
   information: DataConfig = subject(default_factory=DataConfig)
   optimizer: OptimizerConfig = subject(default_factory=AdamConfig)
   epochs: int = 100
   seed: int = 42
   machine: str = "cuda"
   experiment_name: str = "exp_001"

def setup_config_dir():
   config_dir = Path("./hydra_configs")
   config_dir.mkdir(exist_ok=True)
  
   main_config = """
defaults:
 - mannequin: resnet
 - information: cifar10
 - optimizer: adam
 - _self_


epochs: 100
seed: 42
machine: cuda
experiment_name: exp_001
"""
   (config_dir / "config.yaml").write_text(main_config)
  
   model_dir = config_dir / "mannequin"
   model_dir.mkdir(exist_ok=True)
  
   (model_dir / "resnet.yaml").write_text("""
identify: resnet
num_layers: 50
hidden_dim: 512
dropout: 0.1
""")
  
   (model_dir / "vit.yaml").write_text("""
identify: vision_transformer
num_layers: 12
hidden_dim: 768
dropout: 0.1
patch_size: 16
""")
  
   data_dir = config_dir / "information"
   data_dir.mkdir(exist_ok=True)
  
   (data_dir / "cifar10.yaml").write_text("""
dataset: cifar10
batch_size: 32
num_workers: 4
augmentation: true
""")
  
   (data_dir / "imagenet.yaml").write_text("""
dataset: imagenet
batch_size: 128
num_workers: 8
augmentation: true
""")
  
   opt_dir = config_dir / "optimizer"
   opt_dir.mkdir(exist_ok=True)
  
   (opt_dir / "adam.yaml").write_text("""
_target_: torch.optim.Adam
lr: 0.001
betas: (0.9, 0.999)
weight_decay: 0.0
""")
  
   (opt_dir / "sgd.yaml").write_text("""
_target_: torch.optim.SGD
lr: 0.01
momentum: 0.9
nesterov: true
""")
  
   return str(config_dir.absolute())

@hydra.important(version_base=None, config_path="hydra_configs", config_name="config")
def prepare(cfg: DictConfig) -> float:
   print("=" * 80)
   print("CONFIGURATION")
   print("=" * 80)
   print(OmegaConf.to_yaml(cfg))
  
   print("n" + "=" * 80)
   print("ACCESSING CONFIGURATION VALUES")
   print("=" * 80)
   print(f"Mannequin: {cfg.mannequin.identify}")
   print(f"Dataset: {cfg.information.dataset}")
   print(f"Batch Measurement: {cfg.information.batch_size}")
   print(f"Optimizer LR: {cfg.optimizer.lr}")
   print(f"Epochs: {cfg.epochs}")
  
   best_acc = 0.0
   for epoch in vary(min(cfg.epochs, 3)):
       acc = 0.5 + (epoch * 0.1) + (cfg.optimizer.lr * 10)
       best_acc = max(best_acc, acc)
       print(f"Epoch {epoch+1}/{cfg.epochs}: Accuracy = {acc:.4f}")
  
   return best_acc

def demo_basic_usage():
   print("n" + " DEMO 1: Fundamental Configurationn")
   config_dir = setup_config_dir()
   with initialize_config_dir(version_base=None, config_dir=config_dir):
       cfg = compose(config_name="config")
       print(OmegaConf.to_yaml(cfg))


def demo_config_override():
   print("n" + " DEMO 2: Configuration Overridesn")
   config_dir = setup_config_dir()
   with initialize_config_dir(version_base=None, config_dir=config_dir):
       cfg = compose(
           config_name="config",
           overrides=(
               "mannequin=vit",
               "information=imagenet",
               "optimizer=sgd",
               "optimizer.lr=0.1",
               "epochs=50"
           )
       )
       print(OmegaConf.to_yaml(cfg))


def demo_structured_config():
   print("n" + " DEMO 3: Structured Config Validationn")
   from hydra.core.config_store import ConfigStore
   cs = ConfigStore.occasion()
   cs.retailer(identify="training_config", node=TrainingConfig)
   with initialize_config_dir(version_base=None, config_dir=setup_config_dir()):
       cfg = compose(config_name="config")
       print(f"Config sort: {sort(cfg)}")
       print(f"Epochs (validated as int): {cfg.epochs}")


def demo_multirun_simulation():
   print("n" + " DEMO 4: Multirun Simulationn")
   config_dir = setup_config_dir()
   experiments = (
       ("mannequin=resnet", "optimizer=adam", "optimizer.lr=0.001"),
       ("mannequin=resnet", "optimizer=sgd", "optimizer.lr=0.01"),
       ("mannequin=vit", "optimizer=adam", "optimizer.lr=0.0001"),
   )
   outcomes = {}
   for i, overrides in enumerate(experiments):
       print(f"n--- Experiment {i+1} ---")
       with initialize_config_dir(version_base=None, config_dir=config_dir):
           cfg = compose(config_name="config", overrides=overrides)
           print(f"Mannequin: {cfg.mannequin.identify}, Optimizer: {cfg.optimizer._target_}")
           print(f"Studying Charge: {cfg.optimizer.lr}")
           outcomes(f"exp_{i+1}") = cfg
   return outcomes


def demo_interpolation():
   print("n" + " DEMO 5: Variable Interpolationn")
   cfg = OmegaConf.create({
       "mannequin": {"identify": "resnet", "layers": 50},
       "experiment": "${mannequin.identify}_${mannequin.layers}",
       "output_dir": "/outputs/${experiment}",
       "checkpoint": "${output_dir}/greatest.ckpt"
   })
   print(OmegaConf.to_yaml(cfg))
   print(f"nResolved experiment identify: {cfg.experiment}")
   print(f"Resolved checkpoint path: {cfg.checkpoint}")

if __name__ == "__main__":
   demo_basic_usage()
   demo_config_override()
   demo_structured_config()
   demo_multirun_simulation()
   demo_interpolation()
   print("n" + "=" * 80)
   print("Tutorial full! Key takeaways:")
   print("✓ Config composition with defaults")
   print("✓ Runtime overrides by way of command line")
   print("✓ Structured configs with sort security")
   print("✓ Multirun for hyperparameter sweeps")
   print("✓ Variable interpolation")
   print("=" * 80)