"""
Test training patterns and configurations based on examples.
"""
import pytest
from unittest.mock import patch, MagicMock


class TestTrainingPatterns:
    """Test training patterns from examples."""

    def test_trainer_imports(self):
        """Test trainer imports as shown in quick_start.md."""
        try:
            from omnigenbench import Trainer
            assert True
        except ImportError:
            pytest.skip("omnigenome not available or missing dependencies")

    def test_autobench_imports(self):
        """Test AutoBench imports from examples."""
        try:
            from omnigenbench import AutoBench
            assert True
        except ImportError:
            pytest.skip("omnigenome not available or missing dependencies")

    def test_autocuda_import_pattern(self):
        """Test autocuda import pattern from examples."""
        try:
            import autocuda
            # Pattern from examples
            device = autocuda.auto_cuda()
            # Just verify the function exists and returns something
            assert device is not None
        except ImportError:
            # Skip if autocuda not available
            pytest.skip("autocuda not available")

    @patch('omnigenome.AutoBench')
    def test_autobench_initialization_pattern(self, mock_autobench):
        """Test AutoBench initialization pattern from quick_start.md."""
        mock_instance = MagicMock()
        mock_autobench.return_value = mock_instance
        
        from omnigenbench import AutoBench
        
        # Pattern from quick_start.md
        auto_bench = AutoBench(
            benchmark="RGB",
            model_name_or_path="yangheng/OmniGenome-186M",
            device="cuda:0",
            overwrite=True
        )
        
        mock_autobench.assert_called_once_with(
            benchmark="RGB",
            model_name_or_path="yangheng/OmniGenome-186M",
            device="cuda:0",
            overwrite=True
        )

    def test_benchmark_names(self):
        """Test benchmark names from examples."""
        # Benchmarks from quick_start.md
        benchmarks = ["RGB", "GB", "PGB", "GUE", "BEACON"]
        
        for benchmark in benchmarks:
            assert isinstance(benchmark, str)
            assert len(benchmark) > 0
            assert benchmark.isupper()

    def test_trainer_names(self):
        """Test trainer types from examples."""
        # Trainers from autobench examples
        trainers = ["accelerate", "huggingface"]
        
        for trainer in trainers:
            assert isinstance(trainer, str)
            assert trainer in ["accelerate", "huggingface"]

    @patch('omnigenome.Trainer')
    def test_trainer_initialization_pattern(self, mock_trainer):
        """Test Trainer initialization pattern from quick_start.md."""
        mock_trainer.return_value = MagicMock()
        
        from omnigenbench import Trainer
        
        # Mock training arguments
        mock_args = MagicMock()
        mock_args.output_dir = "./results"
        mock_args.num_train_epochs = 3
        mock_args.per_device_train_batch_size = 8
        mock_args.learning_rate = 2e-5
        
        # Pattern from quick_start.md
        trainer = Trainer(
            model=MagicMock(),
            train_dataset=MagicMock(),
            eval_dataset=MagicMock(),
            args=mock_args
        )
        
        mock_trainer.assert_called_once()

    def test_training_arguments_pattern(self):
        """Test training arguments patterns from examples."""
        # Common training parameters from examples
        training_configs = {
            "output_dir": "./results",
            "num_train_epochs": 3,
            "per_device_train_batch_size": 8,
            "learning_rate": 2e-5,
            "epochs": 3,
            "batch_size": 8,
            "seeds": [42, 43, 44]
        }
        
        # Verify types and ranges
        assert isinstance(training_configs["output_dir"], str)
        assert isinstance(training_configs["num_train_epochs"], int)
        assert training_configs["num_train_epochs"] > 0
        assert isinstance(training_configs["per_device_train_batch_size"], int)
        assert training_configs["per_device_train_batch_size"] > 0
        assert isinstance(training_configs["learning_rate"], float)
        assert training_configs["learning_rate"] > 0
        assert isinstance(training_configs["seeds"], list)
        assert all(isinstance(seed, int) for seed in training_configs["seeds"])

    def test_genetic_algorithm_parameters(self):
        """Test genetic algorithm parameters from RNA design examples."""
        # Parameters from easy_rna_design_emoo.py
        ga_params = {
            "mutation_ratio": 0.1,
            "num_population": 100,
            "num_generation": 50,
            "model": "anonymous8/OmniGenome-186M"
        }
        
        # Verify parameter types and ranges
        assert isinstance(ga_params["mutation_ratio"], float)
        assert 0.0 <= ga_params["mutation_ratio"] <= 1.0
        assert isinstance(ga_params["num_population"], int)
        assert ga_params["num_population"] > 0
        assert isinstance(ga_params["num_generation"], int)
        assert ga_params["num_generation"] > 0
        assert isinstance(ga_params["model"], str)

    def test_web_rna_design_parameters(self):
        """Test web RNA design parameters from web_rna_design.py."""
        # Parameters from web_rna_design.py
        web_params = {
            "mutation_ratio": 0.5,
            "num_population": 500,
            "num_generation": 10,
            "puzzle_id": 0
        }
        
        # Verify parameter types
        assert isinstance(web_params["mutation_ratio"], float)
        assert 0.0 <= web_params["mutation_ratio"] <= 1.0
        assert isinstance(web_params["num_population"], int)
        assert web_params["num_population"] > 0
        assert isinstance(web_params["num_generation"], int)
        assert web_params["num_generation"] > 0
        assert isinstance(web_params["puzzle_id"], int)
        assert web_params["puzzle_id"] >= 0

    def test_model_optimization_patterns(self):
        """Test model optimization patterns from examples."""
        # Patterns from examples for model optimization
        optimization_configs = {
            "torch_dtype": "float16",
            "device_map": "auto",
            "trust_remote_code": True,
            "gradient_checkpointing": True,
            "fp16": True
        }
        
        for key, value in optimization_configs.items():
            assert isinstance(key, str)
            # Value types vary, just ensure they exist
            assert value is not None

    @patch('torch.cuda.empty_cache')
    def test_memory_management_pattern(self, mock_empty_cache):
        """Test memory management patterns from web_rna_design.py."""
        try:
            import torch
        except ImportError:
            pytest.skip("torch not available")
            
        # Pattern from web_rna_design.py
        def cleanup_model_pattern():
            """Memory cleanup pattern from examples."""
            # del model, tokenizer  # Would normally delete objects
            torch.cuda.empty_cache()
        
        cleanup_model_pattern()
        mock_empty_cache.assert_called_once()

    def test_random_seed_patterns(self):
        """Test random seed patterns from examples."""
        import random
        
        # Pattern from examples
        def set_random_seed_pattern():
            """Random seed pattern from easy_rna_design_emoo.py."""
            return random.randint(0, 99999999)
        
        # Test seed generation
        seed1 = set_random_seed_pattern()
        seed2 = set_random_seed_pattern()
        
        assert isinstance(seed1, int)
        assert isinstance(seed2, int)
        assert 0 <= seed1 <= 99999999
        assert 0 <= seed2 <= 99999999

    def test_evaluation_metrics_patterns(self):
        """Test evaluation metrics patterns from examples."""
        # Common metrics mentioned in examples
        metrics = [
            "accuracy",
            "f1_score", 
            "precision",
            "recall",
            "mse",
            "mae",
            "r2_score"
        ]
        
        for metric in metrics:
            assert isinstance(metric, str)
            assert len(metric) > 0

    def test_device_selection_patterns(self):
        """Test device selection patterns from examples."""
        # Patterns from examples
        device_patterns = [
            "cuda:0",
            "cuda",
            "cpu",
            "auto"
        ]
        
        for device in device_patterns:
            assert isinstance(device, str)
            assert len(device) > 0

    def test_batch_size_patterns(self):
        """Test batch size patterns from examples."""
        # Common batch sizes from examples
        batch_sizes = [4, 8, 16, 32, 64]
        
        for batch_size in batch_sizes:
            assert isinstance(batch_size, int)
            assert batch_size > 0
            assert batch_size <= 1024  # Reasonable upper limit

    def test_learning_rate_patterns(self):
        """Test learning rate patterns from examples."""
        # Common learning rates from examples
        learning_rates = [1e-5, 2e-5, 5e-5, 1e-4, 2e-4]
        
        for lr in learning_rates:
            assert isinstance(lr, float)
            assert lr > 0
            assert lr < 1.0  # Should be small

    def test_epoch_patterns(self):
        """Test epoch patterns from examples."""
        # Common epoch counts from examples
        epoch_counts = [1, 3, 5, 10, 20]
        
        for epochs in epoch_counts:
            assert isinstance(epochs, int)
            assert epochs > 0
            assert epochs <= 100  # Reasonable upper limit

    def test_output_directory_patterns(self):
        """Test output directory patterns from examples."""
        # Common output directory patterns
        output_dirs = [
            "./results",
            "./output", 
            "./checkpoints",
            "./models"
        ]
        
        for output_dir in output_dirs:
            assert isinstance(output_dir, str)
            assert output_dir.startswith("./") or output_dir.startswith("/")

    def test_model_saving_patterns(self):
        """Test model saving patterns from examples."""
        # File extensions for saved models
        model_extensions = [".pt", ".pth", ".bin", ".safetensors"]
        
        for ext in model_extensions:
            assert isinstance(ext, str)
            assert ext.startswith(".")
            assert len(ext) > 1 