# config.py
import os

# HuggingFace settings
HF_TOKEN = os.getenv("HF_TOKEN")
LEADERBOARD_DATASET = "Sunbird/salt-translation-leaderboard"
TEST_SET_DATASET = "Sunbird/salt-translation-test-set"
SALT_DATASET = "sunbird/salt"

# Language settings - ALL UG40 LANGUAGES
ALL_UG40_LANGUAGES = ["ach", "eng", "lgg", "lug", "nyn", "rny", "teo", "swa"]

LANGUAGE_NAMES = {
    "ach": "Acholi",
    "eng": "English",
    "lgg": "Lugbara",
    "lug": "Luganda",
    "nyn": "Runyankole",
    "rny": "Runyoro",
    "teo": "Ateso",
    "swa": "Swahili",
}

# Google Translate supported subset (for fair comparison)
GOOGLE_SUPPORTED_LANGUAGES = ["lug", "ach", "swa", "eng"]

# Google Translate language mapping
GOOGLE_LANG_MAP = {"lug": "lg", "ach": "ach", "swa": "sw", "eng": "en"}

# SCIENTIFIC EVALUATION TRACKS
EVALUATION_TRACKS = {
    "google_comparable": {
        "name": "Google-Comparable Track",
        "description": "Models evaluated only on language pairs supported by Google Translate",
        "languages": GOOGLE_SUPPORTED_LANGUAGES,
        "min_samples_per_pair": 50,
        "statistical_power": 0.8,
        "significance_level": 0.05,
    },
    "ug40_complete": {
        "name": "UG40-Complete Track",
        "description": "Models evaluated on all UG40 language pairs",
        "languages": ALL_UG40_LANGUAGES,
        "min_samples_per_pair": 30,
        "statistical_power": 0.8,
        "significance_level": 0.05,
    },
    "language_pair_matrix": {
        "name": "Language-Pair Matrix",
        "description": "Individual language pair analysis with statistical significance",
        "languages": ALL_UG40_LANGUAGES,
        "min_samples_per_pair": 20,
        "statistical_power": 0.7,
        "significance_level": 0.05,
    },
}

# MODEL CATEGORIES
MODEL_CATEGORIES = {
    "commercial": {
        "name": "Commercial Systems",
        "description": "Production translation systems",
        "examples": ["google_translate", "azure_translator"],
        "color": "#1f77b4",
    },
    "research": {
        "name": "Research Models",
        "description": "Academic and research institution models",
        "examples": ["nllb", "m2m100"],
        "color": "#ff7f0e",
    },
    "baseline": {
        "name": "Baseline Models",
        "description": "Simple baseline and reference models",
        "examples": ["word_lookup", "frequency_baseline"],
        "color": "#2ca02c",
    },
    "community": {
        "name": "Community Submissions",
        "description": "User-submitted models and fine-tuned variants",
        "examples": ["user_submission"],
        "color": "#d62728",
    },
}

# STATISTICAL SETTINGS
STATISTICAL_CONFIG = {
    "confidence_level": 0.95,
    "bootstrap_samples": 1000,
    "min_samples_for_ci": 20,
    "effect_size_thresholds": {
        "small": 0.2,
        "medium": 0.5,
        "large": 0.8,
    },
    "multiple_testing_correction": "bonferroni",
    "outlier_detection": {
        "method": "iqr",
        "factor": 1.5,
    },
}

# METRICS CONFIGURATION - Enhanced for statistical analysis
METRICS_CONFIG = {
    "primary_metrics": ["bleu", "chrf", "quality_score"],
    "secondary_metrics": ["rouge1", "rouge2", "rougeL", "cer", "wer", "len_ratio"],
    "display_precision": 4,
    "quality_score_components": ["bleu", "chrf", "cer", "wer", "rouge1", "rougeL"],
    "error_metrics": ["cer", "wer"],  # Lower is better
    "score_metrics": ["bleu", "chrf", "quality_score", "rouge1", "rouge2", "rougeL"],
    "statistical_metrics": [
        "mean",
        "std",
        "median",
        "ci_lower",
        "ci_upper",
        "p_value",
        "effect_size",
    ],
}

# VALIDATION REQUIREMENTS
VALIDATION_CONFIG = {
    "min_samples_per_track": {
        "google_comparable": 200,
        "ug40_complete": 400,
        "language_pair_matrix": 50,
    },
    "max_missing_rate": 0.05,  # 5% missing predictions allowed
    "quality_thresholds": {
        "min_valid_predictions": 0.95,
        "max_duplicate_rate": 0.1,
        "min_avg_length": 3,
        "max_avg_length": 500,
    },
}

# UI CONFIGURATION
UI_CONFIG = {
    "title": "🏆 SALT Translation Leaderboard - Scientific Edition",
    "description": """
    Rigorous evaluation of translation models on Ugandan languages with statistical significance testing.
    Three evaluation tracks ensure fair comparison across different model capabilities and language support.
    """,
    "tracks": {
        "google_comparable": {
            "tab_name": "🤖 Google-Comparable Track",
            "icon": "🤖",
            "color": "#1f77b4",
        },
        "ug40_complete": {
            "tab_name": "🌍 UG40-Complete Track",
            "icon": "🌍",
            "color": "#ff7f0e",
        },
        "language_pair_matrix": {
            "tab_name": "📊 Language-Pair Matrix",
            "icon": "📊",
            "color": "#2ca02c",
        },
    },
}

# CHART CONFIGURATION - Research-grade styling
CHART_CONFIG = {
    "statistical_colorscale": "RdYlBu_r",
    "category_colors": {cat: info["color"] for cat, info in MODEL_CATEGORIES.items()},
    "heatmap_config": {
        "colorscale": "Viridis",
        "show_values": True,
        "font_size": 10,
    },
    "confidence_interval_config": {
        "alpha": 0.3,
        "line_width": 2,
        "marker_size": 8,
    },
    "statistical_plot_config": {
        "height": 600,
        "width": 800,
        "margin": {"l": 100, "r": 50, "t": 50, "b": 100},
    },
}

# FILE FORMAT SPECIFICATIONS
PREDICTION_FORMAT = {
    "required_columns": ["sample_id", "prediction"],
    "optional_columns": ["model_name", "confidence", "category"],
    "file_types": [".csv", ".tsv", ".json"],
    "category_detection": {
        "google": ["google", "translate"],
        "nllb": ["nllb", "meta"],
        "m2m": ["m2m", "facebook"],
        "baseline": ["baseline", "simple", "lookup"],
    },
}

# EVALUATION SETTINGS
MAX_TEST_SAMPLES = 500  # Per language pair
MIN_SAMPLES_PER_PAIR = 10  # Minimum for basic statistics
SAMPLE_SIZE_RECOMMENDATIONS = {
    "basic_comparison": 50,
    "statistical_significance": 100,
    "publication_quality": 200,
}