"""
Labor Force Participation Rate Dataset Generator for Sub-Saharan Africa

Parameter Evidence Table:
| Parameter | Source | Year | Value |
|-----------|--------|------|-------|
| LFPR total SSA | ILO | 2023 | 67.2% |
| Male LFPR | ILO | 2023 | 73.8% |
| Female LFPR | ILO | 2023 | 60.9% |
| Youth LFPR 15-24 | ILO | 2023 | 52.4% |
| Urban LFPR | World Bank | 2023 | 72.1% |
| Rural LFPR | World Bank | 2023 | 64.5% |
| Gender gap LFPR | ILO | 2023 | 12.9pp |

Countries: Nigeria, Kenya, Ethiopia, Ghana, South Africa, Tanzania, Uganda, 
           Rwanda, Mozambique, Zambia, Malawi, Senegal, Ivory Coast, Cameroon, Burkina Faso
Years: 2018-2025
Scenarios: low_burden (n=4000), moderate (n=5000), high (n=6000)
Seeds: 42, 43, 44
"""

import numpy as np
import pandas as pd
from typing import Literal


COUNTRIES = [
    "Nigeria", "Kenya", "Ethiopia", "Ghana", "South Africa", "Tanzania",
    "Uganda", "Rwanda", "Mozambique", "Zambia", "Malawi", "Senegal",
    "Ivory Coast", "Cameroon", "Burkina Faso"
]

COUNTRY_CODES = {
    "Nigeria": "NGA", "Kenya": "KEN", "Ethiopia": "ETH", "Ghana": "GHA",
    "South Africa": "ZAF", "Tanzania": "TZA", "Uganda": "UGA", "Rwanda": "RWA",
    "Mozambique": "MOZ", "Zambia": "ZMB", "Malawi": "MWI", "Senegal": "SEN",
    "Ivory Coast": "CIV", "Cameroon": "CMR", "Burkina Faso": "BFA"
}

YEARS = list(range(2018, 2026))
GENDERS = ["male", "female"]
AGE_GROUPS = ["15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", 
              "50-54", "55-59", "60-64"]
URBAN_RURAL = ["urban", "rural"]
EDUCATION_LEVELS = ["none", "primary", "secondary", "tertiary"]


def dag_sample_lfpr(node: str, parent_values: dict, rng: np.random.Generator, 
                     year: int, country: str) -> any:
    if node == "lfp_status":
        gender = parent_values.get("gender", "male")
        age_group = parent_values.get("age_group", "25-29")
        urban_rural = parent_values.get("urban_rural", "urban")
        
        base_rate = 0.672
        
        if gender == "female":
            base_rate -= 0.063
        else:
            base_rate += 0.066
            
        if "15-19" in age_group:
            base_rate -= 0.25
        elif "20-24" in age_group:
            base_rate -= 0.10
        elif "60-64" in age_group:
            base_rate -= 0.15
            
        if urban_rural == "rural":
            base_rate -= 0.076
        
        base_rate = max(0.3, min(0.85, base_rate))
        
        if rng.random() < base_rate:
            return "in_labor_force"
        else:
            return "not_in_labor_force"
    
    elif node == "labor_force_status":
        lfp = parent_values.get("lfp_status", "in_labor_force")
        
        if lfp == "not_in_labor_force":
            return rng.choice(["student", "homemaker", "retired", "discouraged", "other_inactive"], 
                             p=[0.25, 0.35, 0.10, 0.15, 0.15])
        else:
            return rng.choice(["employed", "unemployed"], p=[0.88, 0.12])
    
    elif node == "employment_type":
        emp_status = parent_values.get("labor_force_status", "employed")
        
        if emp_status != "employed":
            return "not_applicable"
        
        return rng.choice(["full_time", "part_time", "self_employed", "casual"], 
                         p=[0.45, 0.15, 0.30, 0.10])
    
    elif node == "work_hours":
        emp_type = parent_values.get("employment_type", "full_time")
        
        if emp_type == "full_time":
            return rng.choice(list(range(35, 50)))
        elif emp_type == "part_time":
            return rng.choice(list(range(1, 35)))
        else:
            return rng.choice(list(range(20, 60)))
    
    return None


def generate_labor_force_participation_data(
    scenario: Literal["low_burden", "moderate", "high_burden"],
    seed: int,
    country: str = None
) -> pd.DataFrame:
    """Generate labor force participation dataset for SSA."""
    n_samples = {"low_burden": 4000, "moderate": 5000, "high_burden": 6000}[scenario]
    
    rng = np.random.default_rng(seed)
    
    if country:
        countries = [country]
    else:
        countries = COUNTRIES
    
    records = []
    samples_per_country = n_samples // len(countries)
    
    for cntry in countries:
        for _ in range(samples_per_country):
            year = rng.choice(YEARS)
            gender = rng.choice(GENDERS)
            age_group = rng.choice(AGE_GROUPS)
            urban_rural = rng.choice(URBAN_RURAL)
            education = rng.choice(EDUCATION_LEVELS)
            
            parent_values = {
                "gender": gender,
                "age_group": age_group,
                "urban_rural": urban_rural
            }
            
            lfp_status = dag_sample_lfpr("lfp_status", parent_values, rng, year, cntry)
            parent_values["lfp_status"] = lfp_status
            
            lf_status = dag_sample_lfpr("labor_force_status", parent_values, rng, year, cntry)
            parent_values["labor_force_status"] = lf_status
            
            emp_type = dag_sample_lfpr("employment_type", parent_values, rng, year, cntry)
            parent_values["employment_type"] = emp_type
            
            work_hours = dag_sample_lfpr("work_hours", parent_values, rng, year, cntry)
            
            records.append({
                "country": cntry,
                "country_code": COUNTRY_CODES[cntry],
                "year": year,
                "gender": gender,
                "age_group": age_group,
                "urban_rural": urban_rural,
                "education_level": education,
                "in_labor_force": lfp_status == "in_labor_force",
                "labor_force_status": lf_status,
                "employment_type": emp_type,
                "weekly_hours": work_hours,
                "is_underemployed": work_hours < 35 if work_hours else False,
                "scenario": scenario
            })
    
    df = pd.DataFrame(records)
    df.attrs["seed"] = seed
    df.attrs["scenario"] = scenario
    df.attrs["source"] = "ILO, World Bank"
    
    return df


if __name__ == "__main__":
    for scenario in ["low_burden", "moderate", "high_burden"]:
        for seed in [42, 43, 44]:
            df = generate_labor_force_participation_data(scenario, seed)
            filename = f"labor_force_participation_{scenario}_seed{seed}.csv"
            df.to_csv(filename, index=False)
            print(f"Created {filename}: {len(df)} records")