"""
Two-Tower Configuration

Architecture constants for Isengard (User Tower) and Mordor (Wine Tower).
"""

# =============================================================================
# EMBEDDING DIMENSIONS
# =============================================================================

# Input embedding dimension (google-text-embedding-004)
EMBEDDING_DIM = 768

# Output vector dimensions for both towers (must match for dot product)
USER_VECTOR_DIM = 128
WINE_VECTOR_DIM = 128

# Hidden layer dimension
HIDDEN_DIM = 256

# =============================================================================
# CATEGORICAL FEATURES
# =============================================================================

# Feature list matching constants.py CATEGORICAL_FEATURES
CATEGORICAL_FEATURES = [
    "color",
    "type",
    "style",
    "climate_type",
    "climate_band",
    "vintage_band",
]

# Categorical feature vocabulary sizes (approximate, for one-hot encoding)
CATEGORICAL_VOCAB_SIZES = {
    "color": 5,  # red, white, rosé, orange, sparkling
    "type": 4,  # still, sparkling, fortified, dessert
    "style": 10,  # Natural, Organic, Biodynamic, etc.
    "climate_type": 4,  # cool, moderate, warm, hot
    "climate_band": 4,  # cool, moderate, warm, hot
    "vintage_band": 4,  # young, developing, mature, non_vintage
}

# Total categorical encoding dimension
CATEGORICAL_ENCODING_DIM = sum(CATEGORICAL_VOCAB_SIZES.values())  # ~31

# =============================================================================
# TRAINING PARAMETERS
# =============================================================================

TRIPLET_MARGIN = 0.2  # Margin for triplet loss
LEARNING_RATE = 1e-4
BATCH_SIZE = 32
POSITIVE_RATING_THRESHOLD = 4.0  # Ratings >= 4 are positive samples

# =============================================================================
# HUGGINGFACE INFERENCE
# =============================================================================

# Model ID on HuggingFace Hub (for model upload/download)
HF_MODEL_ID = "swirl/two-tower-recommender"

# Inference Endpoint URL is read from settings.HF_TWO_TOWER_ENDPOINT_URL
# API Token is read from settings.HF_API_TOKEN (same as CLIP)