"""
Test dataset loading and processing patterns based on examples.
"""
import pytest
import json
import tempfile
import os
from unittest.mock import patch, MagicMock, mock_open


class TestDatasetPatterns:
    """Test dataset patterns from examples."""

    def test_dataset_imports(self):
        """Test dataset class imports as shown in examples."""
        try:
            from omnigenbench import (
                OmniDatasetForSequenceClassification,
                OmniDatasetForSequenceRegression,
                OmniDatasetForTokenClassification,
                OmniDatasetForTokenRegression,
            )
            assert True
        except ImportError:
            pytest.skip("omnigenome not available or missing dependencies")

    def test_json_dataset_format(self):
        """Test JSON dataset format used in examples."""
        # Sample data format from toy_datasets
        sample_data = [
            {"seq": "AUCG", "label": "(...)"},
            {"seq": "AUGC", "label": "(..)"},
            {"seq": "CGAU", "label": "().."},
        ]
        
        # Verify format
        for item in sample_data:
            assert "seq" in item
            assert "label" in item
            assert isinstance(item["seq"], str)
            assert len(item["seq"]) > 0

    @patch("builtins.open", new_callable=mock_open)
    @patch("json.loads")
    def test_dataset_loading_pattern(self, mock_json_loads, mock_file):
        """Test dataset loading pattern from examples."""
        # Mock data similar to examples
        mock_data = [
            {"seq": "AUCG", "label": "(..)"},
            {"seq": "AUGC", "label": "()"},
        ]
        
        mock_json_loads.return_value = mock_data[0]
        mock_file.return_value.__iter__ = lambda self: iter([
            '{"seq": "AUCG", "label": "(..)"}\n',
            '{"seq": "AUGC", "label": "()"}\n'
        ])
        
        # Pattern from examples for loading test data
        def load_test_data(file_path):
            """Pattern from Secondary_Structure_Prediction.py."""
            data = []
            with open(file_path) as f:
                for line in f:
                    data.append(json.loads(line))
            return data
        
        # Test the pattern
        result = load_test_data("test_file.json")
        assert len(result) == 2

    def test_config_file_structure(self):
        """Test config.py structure from toy_datasets."""
        # Common config patterns from examples
        config_patterns = {
            "max_length": [128, 256, 512, 1024],
            "num_labels": [2, 3, 4, 5],
            "task_type": ["classification", "regression", "token_classification"],
        }
        
        for key, valid_values in config_patterns.items():
            assert isinstance(key, str)
            assert isinstance(valid_values, list)
            assert len(valid_values) > 0

    def test_sample_data_extraction_pattern(self):
        """Test sample data extraction pattern from examples."""
        import random
        try:
            import numpy as np
        except ImportError:
            pytest.skip("numpy not available")
        
        def sample_rna_sequence_pattern():
            """Pattern from Secondary_Structure_Prediction.py."""
            try:
                # Mock data similar to toy_datasets/Archive2/test.json
                mock_examples = [
                    {"seq": "AUCG", "label": "(..)"},
                    {"seq": "AUGC", "label": "().."},
                    {"seq": "CGAU", "label": "(())"},
                ]
                ex = mock_examples[np.random.randint(len(mock_examples))]
                return ex['seq'], ex.get('label', '')
            except Exception as e:
                return f"Error loading sample: {e}", ""
        
        # Test the pattern
        seq, label = sample_rna_sequence_pattern()
        assert isinstance(seq, str)
        assert isinstance(label, str)

    def test_data_validation_patterns(self):
        """Test data validation patterns from examples."""
        def validate_sequence_label_pair(seq, label):
            """Validate sequence-label pair format."""
            if not isinstance(seq, str) or not isinstance(label, str):
                return False
            if len(seq) == 0:
                return False
            # RNA sequence validation
            if not all(base in "AUCG" for base in seq):
                return False
            # Structure validation (if applicable)
            if label and not all(c in "()." for c in label):
                return False
            return True
        
        # Test valid pairs
        valid_pairs = [
            ("AUCG", "(..)"),
            ("AUG", "..."),
            ("AU", "()"),
            ("A", "."),
        ]
        
        for seq, label in valid_pairs:
            assert validate_sequence_label_pair(seq, label)
        
        # Test invalid pairs
        invalid_pairs = [
            ("", ""),           # Empty sequence
            ("AUXG", "(..)"),   # Invalid base X
            ("AUCG", "(.)X"),   # Invalid structure char
            (123, "(..)"),      # Non-string sequence
            ("AUCG", 123),      # Non-string label
        ]
        
        for seq, label in invalid_pairs:
            assert not validate_sequence_label_pair(seq, label)

    def test_train_test_split_patterns(self):
        """Test train/test split patterns from examples."""
        # Mock dataset similar to toy_datasets structure
        mock_data = [
            {"seq": "AUCG", "label": "(..)"},
            {"seq": "AUGC", "label": "().."},
            {"seq": "CGAU", "label": "(())"},
            {"seq": "GAUC", "label": "...."},
        ]
        
        def split_data_pattern(data, train_ratio=0.8):
            """Simple train/test split pattern."""
            import random
            random.shuffle(data)
            split_idx = int(len(data) * train_ratio)
            return data[:split_idx], data[split_idx:]
        
        train_data, test_data = split_data_pattern(mock_data.copy())
        
        # Verify split
        assert len(train_data) + len(test_data) == len(mock_data)
        assert len(train_data) >= len(test_data)  # With 80/20 split

    def test_dataset_file_patterns(self):
        """Test dataset file naming patterns from examples."""
        expected_files = ["train.json", "test.json", "valid.json", "config.py"]
        
        for filename in expected_files:
            # Verify naming patterns
            if filename.endswith(".json"):
                assert filename in ["train.json", "test.json", "valid.json"]
            elif filename.endswith(".py"):
                assert filename == "config.py"

    def test_dataset_initialization_pattern(self):
        """Test dataset initialization pattern from examples."""
        try:
            from omnigenbench import OmniDatasetForSequenceClassification
        except ImportError:
            pytest.skip("omnigenome not available")
            
        with patch("omnigenome.OmniDatasetForSequenceClassification") as mock_dataset:
            mock_dataset.return_value = MagicMock()
            
            # Create a single mock tokenizer instance to use in both call and assertion
            mock_tokenizer_instance = MagicMock()
            
            # Pattern from examples
            dataset = OmniDatasetForSequenceClassification(
                train_file="path/to/train.json",
                test_file="path/to/test.json",
                tokenizer=mock_tokenizer_instance,
                max_length=512
            )
            
            # Verify the call was made with the expected arguments
            mock_dataset.assert_called_once()
            call_args = mock_dataset.call_args
            assert call_args[1]["train_file"] == "path/to/train.json"
            assert call_args[1]["test_file"] == "path/to/test.json"
            assert call_args[1]["max_length"] == 512

    def test_benchmark_dataset_structure(self):
        """Test benchmark dataset structure from examples."""
        # RGB benchmark structure from examples
        rgb_tasks = [
            "RNA-mRNA",
            "RNA-SNMD", 
            "RNA-SNMR",
            "RNA-SSP-Archive2",
            "RNA-SSP-bpRNA",
            "RNA-SSP-rnastralign"
        ]
        
        for task in rgb_tasks:
            assert isinstance(task, str)
            assert "RNA" in task
            assert len(task) > 3

    def test_eterna_dataset_pattern(self):
        """Test Eterna dataset pattern from RNA design examples."""
        # Pattern from eterna100_vienna2.txt usage
        def load_eterna_pattern():
            """Mock Eterna dataset loading pattern."""
            # This would normally read from eterna100_vienna2.txt
            mock_eterna_data = [
                "(((...)))",
                "(((())))",
                "........",
                "((..))"
            ]
            return mock_eterna_data
        
        eterna_structures = load_eterna_pattern()
        
        for structure in eterna_structures:
            assert isinstance(structure, str)
            assert all(c in "()." for c in structure)

    def test_solved_sequences_format(self):
        """Test solved sequences format from RNA design examples."""
        # Format from solved_sequences.json in RNA design
        solved_format = {
            "puzzle_1": {
                "sequence": "AUCG",
                "structure": "(..)",
                "energy": -5.2
            },
            "puzzle_2": {
                "sequence": "AUGC", 
                "structure": "().",
                "energy": -3.1
            }
        }
        
        for puzzle_id, data in solved_format.items():
            assert isinstance(puzzle_id, str)
            assert "sequence" in data
            assert "structure" in data
            assert "energy" in data
            assert isinstance(data["energy"], (int, float))

    def test_data_loading_error_handling(self):
        """Test error handling patterns from examples."""
        def safe_load_pattern(file_path):
            """Safe loading pattern from examples."""
            try:
                # Mock successful load
                return [{"seq": "AUCG", "label": "(..)"}]
            except FileNotFoundError:
                return []
            except json.JSONDecodeError:
                return []
            except Exception as e:
                print(f"Unexpected error: {e}")
                return []
        
        # Test error handling
        result = safe_load_pattern("nonexistent.json")
        assert isinstance(result, list) 