"""
Labor Productivity Dataset Generator for Sub-Saharan Africa

Parameter Evidence Table:
| Parameter | Source | Year | Value |
|-----------|--------|------|-------|
| Labor productivity growth | ILO | 2023 | 2.1% |
| GDP per worker SSA | World Bank | 2023 | $8,200 |
| Agriculture productivity | FAO | 2023 | $3,800 |
| Manufacturing productivity | UNIDO | 2023 | $12,500 |
| Services productivity | World Bank | 2023 | $15,400 |
| Total factor productivity | UN | 2023 | 0.8% |

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))
SECTORS = ["agriculture", "mining", "manufacturing", "construction", 
           "wholesale_retail", "transport", "services"]
FIRM_SIZES = ["micro", "small", "medium", "large"]


def dag_sample_productivity(node: str, parent_values: dict, rng: np.random.Generator,
                           year: int, country: str) -> any:
    if node == "sector":
        return rng.choice(SECTORS, p=[0.28, 0.04, 0.10, 0.06, 0.22, 0.08, 0.22])
    
    elif node == "firm_size":
        return rng.choice(FIRM_SIZES, p=[0.65, 0.20, 0.10, 0.05])
    
    elif node == "technology_level":
        sector = parent_values.get("sector", "services")
        
        if sector in ["manufacturing", "mining"]:
            return rng.choice(["low", "medium", "high"], p=[0.35, 0.45, 0.20])
        elif sector in ["transport", "services"]:
            return rng.choice(["low", "medium", "high"], p=[0.40, 0.40, 0.20])
        else:
            return rng.choice(["low", "medium", "high"], p=[0.65, 0.28, 0.07])
    
    elif node == "export_orientation":
        sector = parent_values.get("sector", "services")
        
        if sector == "manufacturing":
            return rng.choice(["none", "domestic", "export"], p=[0.45, 0.35, 0.20])
        elif sector == "agriculture":
            return rng.choice(["none", "domestic", "export"], p=[0.55, 0.35, 0.10])
        else:
            return rng.choice(["none", "domestic", "export"], p=[0.75, 0.20, 0.05])
    
    return None


def calculate_productivity(parent_values: dict, rng: np.random.Generator, 
                           year: int, country: str) -> float:
    sector = parent_values.get("sector", "services")
    firm_size = parent_values.get("firm_size", "small")
    tech_level = parent_values.get("technology_level", "low")
    export = parent_values.get("export_orientation", "none")
    
    sector_productivity = {
        "agriculture": 3800, "mining": 25000, "manufacturing": 12500,
        "construction": 8500, "wholesale_retail": 6500, "transport": 11000,
        "services": 15400
    }
    
    firm_multipliers = {"micro": 0.45, "small": 0.75, "medium": 1.0, "large": 1.35}
    tech_multipliers = {"low": 0.75, "medium": 1.0, "high": 1.45}
    export_multipliers = {"none": 0.85, "domestic": 1.0, "export": 1.25}
    
    base = sector_productivity.get(sector, 10000)
    base *= firm_multipliers.get(firm_size, 1.0)
    base *= tech_multipliers.get(tech_level, 1.0)
    base *= export_multipliers.get(export, 1.0)
    
    return base * rng.lognormal(0, 0.3)


def generate_labor_productivity_data(
    scenario: Literal["low_burden", "moderate", "high_burden"],
    seed: int,
    country: str = None
) -> pd.DataFrame:
    """Generate labor productivity 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)
            
            sector = dag_sample_productivity("sector", {}, rng, year, cntry)
            firm_size = dag_sample_productivity("firm_size", {}, rng, year, cntry)
            
            parent_values = {"sector": sector, "firm_size": firm_size}
            tech_level = dag_sample_productivity("technology_level", parent_values, rng, year, cntry)
            export = dag_sample_productivity("export_orientation", parent_values, rng, year, cntry)
            
            parent_values["technology_level"] = tech_level
            parent_values["export_orientation"] = export
            
            productivity = calculate_productivity(parent_values, rng, year, cntry)
            
            records.append({
                "country": cntry,
                "country_code": COUNTRY_CODES[cntry],
                "year": year,
                "sector": sector,
                "firm_size": firm_size,
                "technology_level": tech_level,
                "export_orientation": export,
                "labor_productivity_usd": round(productivity, 2),
                "value_added_per_worker": round(productivity * 0.65, 2),
                "output_per_hour": round(productivity / 2000, 2),
                "scenario": scenario
            })
    
    df = pd.DataFrame(records)
    df.attrs["seed"] = seed
    df.attrs["scenario"] = scenario
    df.attrs["source"] = "ILO, World Bank, FAO, UNIDO"
    
    return df


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