"""Scene-specific validation — post-schema semantic checks for Layer 2 output."""

from dataclasses import dataclass
from typing import Literal
from difflib import SequenceMatcher
from src.schemas.scene_array import SceneArray


@dataclass
class ValidationWarning:
    type: Literal[
        "duplicate_scene_number",
        "similar_character_names",
        "scene_count_deviation",
        "empty_characters",
        "unknown_values",
    ]
    scene_number: int | None
    message: str


def validate_scenes(scene_array: SceneArray, heading_count: int) -> list[ValidationWarning]:
    """Run semantic validation checks on extracted scenes.

    Args:
        scene_array: Validated SceneArray from Layer 2.
        heading_count: Number of scene_heading elements from Layer 1 output.

    Returns:
        List of warnings. Empty list means all checks passed.
    """
    warnings: list[ValidationWarning] = []

    # 1. Duplicate scene numbers (already caught by SceneArray validator, but double-check)
    seen_numbers: dict[int, int] = {}
    for scene in scene_array.scenes:
        if scene.scene_number in seen_numbers:
            warnings.append(ValidationWarning(
                type="duplicate_scene_number",
                scene_number=scene.scene_number,
                message=f"Scene number {scene.scene_number} appears more than once",
            ))
        seen_numbers[scene.scene_number] = seen_numbers.get(scene.scene_number, 0) + 1

    # 2. Similar character names (possible duplicates)
    all_characters: set[str] = set()
    for scene in scene_array.scenes:
        all_characters.update(scene.characters_present)

    char_list = sorted(all_characters)
    for i, name_a in enumerate(char_list):
        for name_b in char_list[i + 1:]:
            ratio = SequenceMatcher(None, name_a.upper(), name_b.upper()).ratio()
            if ratio > 0.8 and name_a != name_b:
                warnings.append(ValidationWarning(
                    type="similar_character_names",
                    scene_number=None,
                    message=f"Possible duplicate characters: '{name_a}' and '{name_b}' (similarity: {ratio:.0%})",
                ))

    # 3. Scene count deviation
    extracted_count = len(scene_array.scenes)
    if heading_count > 0:
        deviation = abs(extracted_count - heading_count) / heading_count
        if deviation > 0.20:
            warnings.append(ValidationWarning(
                type="scene_count_deviation",
                scene_number=None,
                message=f"Extracted {extracted_count} scenes but Layer 1 found {heading_count} scene headings (deviation: {deviation:.0%})",
            ))

    # 4. Empty characters_present
    for scene in scene_array.scenes:
        if not scene.characters_present:
            warnings.append(ValidationWarning(
                type="empty_characters",
                scene_number=scene.scene_number,
                message=f"Scene {scene.scene_number} has no characters listed",
            ))

    # 5. UNKNOWN values
    for scene in scene_array.scenes:
        scene_dict = scene.model_dump()
        for key, value in scene_dict.items():
            if value == "UNKNOWN" or (isinstance(value, list) and "UNKNOWN" in value):
                warnings.append(ValidationWarning(
                    type="unknown_values",
                    scene_number=scene.scene_number,
                    message=f"Scene {scene.scene_number} field '{key}' contains UNKNOWN",
                ))

    return warnings