"""
Validation logic for Scenar Creator.
Extracted from scenar/core.py — validate_inputs, validate_excel_template, overlap detection.
"""

import pandas as pd
from datetime import datetime
import logging

logger = logging.getLogger(__name__)

DEFAULT_COLOR = "#ffffff"
MAX_FILE_SIZE_MB = 10
REQUIRED_COLUMNS = ["Datum", "Zacatek", "Konec", "Program", "Typ", "Garant", "Poznamka"]


class ScenarsError(Exception):
    """Base exception for Scenar Creator."""
    pass


class ValidationError(ScenarsError):
    """Raised when input validation fails."""
    pass


class TemplateError(ScenarsError):
    """Raised when Excel template is invalid."""
    pass


def validate_inputs(title: str, detail: str, file_size: int) -> None:
    """Validate user inputs for security and sanity."""
    if not title or not isinstance(title, str):
        raise ValidationError("Title is required and must be a string")
    if len(title.strip()) == 0:
        raise ValidationError("Title cannot be empty")
    if len(title) > 200:
        raise ValidationError("Title is too long (max 200 characters)")

    if not detail or not isinstance(detail, str):
        raise ValidationError("Detail is required and must be a string")
    if len(detail.strip()) == 0:
        raise ValidationError("Detail cannot be empty")
    if len(detail) > 500:
        raise ValidationError("Detail is too long (max 500 characters)")

    if file_size > MAX_FILE_SIZE_MB * 1024 * 1024:
        raise ValidationError(f"File size exceeds {MAX_FILE_SIZE_MB} MB limit")


def normalize_time(time_str: str):
    """Parse time string in formats %H:%M or %H:%M:%S."""
    for fmt in ('%H:%M', '%H:%M:%S'):
        try:
            return datetime.strptime(time_str, fmt).time()
        except ValueError:
            continue
    return None


def validate_excel_template(df: pd.DataFrame) -> None:
    """Validate that Excel has required columns."""
    missing_cols = set(REQUIRED_COLUMNS) - set(df.columns)
    if missing_cols:
        raise TemplateError(
            f"Excel template missing required columns: {', '.join(missing_cols)}. "
            f"Expected: {', '.join(REQUIRED_COLUMNS)}"
        )