"""
Core inference functions for CRISPR Array Detection.

Provides:
- predict_sequence(): Per-position CRISPR probability scores
- embed_sequence(): Hidden state embeddings for state-dynamics visualization
"""

import logging
from typing import Optional, Literal
from dataclasses import dataclass

import numpy as np
import tensorflow as tf

from .model_loader import get_model, get_embedding_model
from .tokenizer import encode_sequence, create_windows, WINDOW_SIZE

logger = logging.getLogger(__name__)


@dataclass
class PredictionResult:
    """Result of CRISPR prediction."""
    positions: list[int]
    probabilities: list[float]
    overall_score: float
    sequence_length: int
    num_windows: int


@dataclass
class EmbeddingResult:
    """Result of embedding extraction."""
    embedding: list[float]
    method: str
    embedding_dim: int
    sequence_length: int


@dataclass
class TrajectoryResult:
    """Result of trajectory embedding extraction."""
    embeddings: list[list[float]]
    positions: list[int]
    method: str
    embedding_dim: int
    sequence_length: int
    num_windows: int


def cast_for_model(X: np.ndarray, expected_dtype: tf.dtypes.DType) -> np.ndarray:
    """Cast input array to the dtype expected by the model."""
    if expected_dtype.is_floating:
        return X.astype(np.float32, copy=False)
    if expected_dtype == tf.int64:
        return X.astype(np.int64, copy=False)
    return X.astype(np.int32, copy=False)


def predict_batch(
    model: tf.keras.Model,
    windows: np.ndarray,
    batch_size: int = 32
) -> np.ndarray:
    """
    Run prediction on batched windows.

    Args:
        model: Keras model
        windows: Array of shape (N, window_size)
        batch_size: Batch size for inference

    Returns:
        Predictions of shape (N, window_size) with probabilities
    """
    if batch_size <= 0:
        raise ValueError("batch_size must be a positive integer")

    expected_dtype = model.inputs[0].dtype
    windows = cast_for_model(windows, expected_dtype)

    n_windows = len(windows)
    window_size = windows.shape[1]

    # Pre-allocate output
    predictions = np.empty((n_windows, window_size), dtype=np.float32)

    for i in range(0, n_windows, batch_size):
        batch = windows[i:i + batch_size]
        pred = model(batch, training=False)

        # Handle different output shapes
        if len(pred.shape) == 3:
            if pred.shape[-1] == 1:
                pred = tf.squeeze(pred, axis=-1)
            else:
                # Multi-class output - take class 1 probability (CRISPR positive)
                pred = pred[..., 1] if pred.shape[-1] > 1 else pred[..., 0]

        pred = tf.clip_by_value(pred, 0.0, 1.0).numpy()
        predictions[i:i + len(batch)] = pred

    return predictions


def aggregate_predictions(
    predictions: np.ndarray,
    starts: np.ndarray,
    seq_length: int,
    window_size: int = WINDOW_SIZE,
    aggregation: Literal["mean", "max"] = "mean"
) -> np.ndarray:
    """
    Aggregate overlapping window predictions into per-position scores.

    Args:
        predictions: Array of shape (N, window_size) with per-window predictions
        starts: Start positions of each window
        seq_length: Total sequence length
        window_size: Size of each window
        aggregation: Aggregation method ("mean" or "max")

    Returns:
        Per-position probability array of shape (seq_length,)
    """
    if aggregation not in {"mean", "max"}:
        raise ValueError("aggregation must be 'mean' or 'max'")

    scores = np.zeros(seq_length, dtype=np.float32)
    counts = np.zeros(seq_length, dtype=np.int32)

    for i, start in enumerate(starts):
        end = min(start + window_size, seq_length)
        pred_len = end - start

        if aggregation == "max":
            scores[start:end] = np.maximum(scores[start:end], predictions[i, :pred_len])
        else:  # mean
            scores[start:end] += predictions[i, :pred_len]
            counts[start:end] += 1

    if aggregation == "mean":
        # Avoid division by zero
        counts = np.maximum(counts, 1)
        scores = scores / counts

    return scores


def predict_sequence(
    sequence: str,
    stride: int = 100,
    batch_size: int = 32,
    aggregation: Literal["mean", "max"] = "mean",
    model: Optional[tf.keras.Model] = None
) -> PredictionResult:
    """
    Predict CRISPR array probability for each position in a sequence.

    Args:
        sequence: DNA sequence string
        stride: Step size between sliding windows (default 100)
        batch_size: Batch size for inference (default 32)
        aggregation: How to aggregate overlapping predictions ("mean" or "max")
        model: Optional model instance (uses singleton if not provided)

    Returns:
        PredictionResult with per-position probabilities
    """
    if aggregation not in {"mean", "max"}:
        raise ValueError("aggregation must be 'mean' or 'max'")
    if batch_size <= 0:
        raise ValueError("batch_size must be a positive integer")

    # Tokenize sequence
    tokens = encode_sequence(sequence)
    seq_length = len(tokens)

    # Create sliding windows
    windows, starts = create_windows(tokens, window_size=WINDOW_SIZE, stride=stride)

    if model is None:
        model = get_model()

    logger.info(f"Processing sequence: {seq_length} bp, {len(windows)} windows (stride={stride})")

    # Run batched prediction
    predictions = predict_batch(model, windows, batch_size=batch_size)

    # Aggregate to per-position scores
    scores = aggregate_predictions(
        predictions, starts, seq_length,
        window_size=WINDOW_SIZE, aggregation=aggregation
    )

    # Calculate overall score (mean of all positions)
    overall_score = float(np.mean(scores))

    return PredictionResult(
        positions=list(range(seq_length)),
        probabilities=[float(p) for p in scores],
        overall_score=overall_score,
        sequence_length=seq_length,
        num_windows=len(windows)
    )


def embed_batch(
    model: tf.keras.Model,
    windows: np.ndarray,
    batch_size: int = 32
) -> np.ndarray:
    """
    Extract embeddings from batched windows.

    Args:
        model: Embedding model
        windows: Array of shape (N, window_size)
        batch_size: Batch size for inference

    Returns:
        Embeddings of shape (N, window_size, embed_dim) or (N, embed_dim)
    """
    if batch_size <= 0:
        raise ValueError("batch_size must be a positive integer")

    expected_dtype = model.inputs[0].dtype
    windows = cast_for_model(windows, expected_dtype)

    n_windows = len(windows)
    embeddings = []

    for i in range(0, n_windows, batch_size):
        batch = windows[i:i + batch_size]
        emb = model(batch, training=False).numpy()
        embeddings.append(emb)

    return np.concatenate(embeddings, axis=0)


def embed_sequence(
    sequence: str,
    mode: Literal["mean", "cls", "max", "trajectory"] = "mean",
    stride: int = 100,
    batch_size: int = 32,
    model: Optional[tf.keras.Model] = None
) -> EmbeddingResult | TrajectoryResult:
    """
    Extract hidden state embeddings from a sequence.

    Args:
        sequence: DNA sequence string
        mode: Embedding mode:
            - "mean": Mean pool over all positions and windows
            - "cls": Use first position embedding (similar to BERT [CLS])
            - "max": Max pool over all positions
            - "trajectory": Return per-window embeddings for state-dynamics
        stride: Step size between windows (for trajectory mode)
        batch_size: Batch size for inference
        model: Optional embedding model (uses singleton if not provided)

    Returns:
        EmbeddingResult (for mean/cls/max) or TrajectoryResult (for trajectory)
    """
    if mode not in {"mean", "cls", "max", "trajectory"}:
        raise ValueError("mode must be one of: mean, cls, max, trajectory")
    if batch_size <= 0:
        raise ValueError("batch_size must be a positive integer")

    # Tokenize sequence
    tokens = encode_sequence(sequence)
    seq_length = len(tokens)

    # Create windows
    windows, starts = create_windows(tokens, window_size=WINDOW_SIZE, stride=stride)

    if model is None:
        model = get_embedding_model()

    logger.info(f"Extracting embeddings: {seq_length} bp, {len(windows)} windows")

    # Get embeddings (shape: N, window_size, embed_dim)
    embeddings = embed_batch(model, windows, batch_size=batch_size)

    if mode == "trajectory":
        # Return per-window mean-pooled embeddings for state-dynamics
        # Shape: (N, embed_dim) after mean over positions
        window_embeddings = np.mean(embeddings, axis=1)

        return TrajectoryResult(
            embeddings=[emb.tolist() for emb in window_embeddings],
            positions=[int(s + WINDOW_SIZE // 2) for s in starts],  # Center position
            method="mean_pooled_per_window",
            embedding_dim=int(window_embeddings.shape[-1]),
            sequence_length=seq_length,
            num_windows=len(windows)
        )

    # Aggregate embeddings across all windows and positions
    if mode == "cls":
        # Take first position from first window
        final_embedding = embeddings[0, 0, :]
        method = "first_position_first_window"
    elif mode == "max":
        # Max pool across all positions and windows
        final_embedding = np.max(embeddings, axis=(0, 1))
        method = "max_pooled_all"
    else:  # mean
        # Mean pool across all positions and windows
        final_embedding = np.mean(embeddings, axis=(0, 1))
        method = "mean_pooled_all"

    return EmbeddingResult(
        embedding=final_embedding.tolist(),
        method=method,
        embedding_dim=int(final_embedding.shape[0]),
        sequence_length=seq_length
    )


def detect_crispr_regions(
    sequence: str,
    threshold: float = 0.3,
    min_length: int = 160,
    merge_gap: int = 80,
    stride: int = 100,
    model: Optional[tf.keras.Model] = None,
    prediction_result: Optional[PredictionResult] = None
) -> list[dict]:
    """
    Detect CRISPR array regions in a sequence.

    Args:
        sequence: DNA sequence string
        threshold: Probability threshold for calling CRISPR (default 0.3)
        min_length: Minimum region length in bp (default 160)
        merge_gap: Maximum gap to merge adjacent regions (default 80)
        stride: Sliding window stride (default 100)
        model: Optional model instance

    Returns:
        List of detected regions with coordinates and scores
    """
    if not 0.0 <= threshold <= 1.0:
        raise ValueError("threshold must be between 0 and 1")
    if min_length < 1:
        raise ValueError("min_length must be at least 1")
    if merge_gap < 0:
        raise ValueError("merge_gap must be non-negative")

    # Get per-position predictions, or reuse a caller-provided result to avoid
    # running the model twice in UI flows that need both scores and regions.
    result = prediction_result or predict_sequence(sequence, stride=stride, model=model)
    scores = np.array(result.probabilities)

    # Threshold to binary mask
    mask = scores >= threshold

    # Find contiguous regions
    regions = []
    in_region = False
    start = 0

    for i, is_crispr in enumerate(mask):
        if is_crispr and not in_region:
            start = i
            in_region = True
        elif not is_crispr and in_region:
            regions.append((start, i))
            in_region = False

    if in_region:
        regions.append((start, len(mask)))

    # Merge nearby regions
    if regions:
        merged = [list(regions[0])]
        for s, e in regions[1:]:
            prev_s, prev_e = merged[-1]
            if s <= prev_e + merge_gap:
                merged[-1][1] = e
            else:
                merged.append([s, e])
        regions = [(s, e) for s, e in merged]

    # Filter by length and compute scores
    detected = []
    for i, (start, end) in enumerate(regions):
        length = end - start
        if length >= min_length:
            region_scores = scores[start:end]
            detected.append({
                "region_id": i + 1,
                "start": int(start + 1),  # 1-based
                "end": int(end),  # 1-based inclusive
                "length": length,
                "max_score": float(np.max(region_scores)),
                "mean_score": float(np.mean(region_scores)),
            })

    return detected