"""
Retrieval modes for EEG semantic decoding.

Each mode is a callable class that takes:
    embedding_index: EmbeddingIndex
    nexus_conn: sqlite3 connection
    semantic_embedding: torch.Tensor  (the current predicted text embedding)

And returns:
    list of strings (lines to print), or empty list (suppress output this frame)

Drop into EEGSemanticProcessor by replacing the find_similar_messages + _print_unique_lines
path with:   mode.step(semantic_embedding) -> lines
"""

import numpy as np
import torch
import hashlib
import random
from collections import deque


def fix_encoding(s):
    if not s:
        return s
    if isinstance(s, str):
        b = s.encode('utf-8', 'surrogateescape')
    else:
        b = s
    fixed = b.decode('utf-8', 'replace')
    if 'ì' in s or 'í' in s or 'ï' in s:
        return ""
    return fixed


def _retrieve(embedding_index, nexus_conn, embedding_np, k=64, assistant_only=False):
    """Shared retrieval helper. Returns list of (content, distance) tuples."""
    if len(embedding_np.shape) == 1:
        embedding_np = embedding_np.reshape(1, -1)

    distances, indices = embedding_index.search(embedding_np, k)
    distances = distances.flatten()
    indices = indices.flatten()

    cursor = nexus_conn.cursor()
    query = "SELECT content FROM messages WHERE id = ?"
    if assistant_only:
        query += " AND role = 'assistant'"

    results = []
    for msg_id, dist in zip(indices, distances):
        cursor.execute(query, (int(msg_id),))
        row = cursor.fetchone()
        if row and row[0]:
            results.append((row[0], float(dist)))
    return results


def _lines_from_messages(messages, max_lines=60):
    """Extract individual lines from message contents, deduplicated."""
    lines = []
    seen = set()
    for content in messages:
        for line in content.splitlines():
            line = line.strip()
            if not line:
                continue
            line = fix_encoding(line)
            if not line:
                continue
            if line not in seen:
                seen.add(line)
                lines.append(line)
            if len(lines) >= max_lines:
                return lines
    return lines


class FloodMode:
    """
    Original behavior: retrieve k candidates, sample, deduplicate against
    recent windows. Fast, noisy, good for raw stream-of-consciousness.
    """

    def __init__(self, embedding_index, nexus_conn, search_k=180, final_k=90,
                 sample_size=42, last_n=3):
        self.embedding_index = embedding_index
        self.nexus_conn = nexus_conn
        self.search_k = search_k
        self.final_k = final_k
        self.sample_size = sample_size
        self.previous_sets = deque(maxlen=last_n)

    def step(self, semantic_embedding):
        emb_np = semantic_embedding.detach().cpu().numpy()
        results = _retrieve(self.embedding_index, self.nexus_conn, emb_np,
                           k=self.search_k)

        messages = [content for content, _ in results[:self.final_k]]
        if not messages:
            return []

        sample = random.sample(messages, min(self.sample_size, len(messages)))

        current_lines = set()
        for msg in sample:
            for line in msg.splitlines():
                line = line.strip()
                if line:
                    current_lines.add(line)

        unique = current_lines.copy()
        for prev in self.previous_sets:
            unique -= prev
        self.previous_sets.append(current_lines)

        unique = [l for l in map(fix_encoding, unique) if l]
        return sorted(unique)


class DriftMode:
    """
    Emit output only when the semantic pointer moves significantly.
    Retrieves based on the *direction* of movement (current - previous),
    added to the current position. This amplifies whatever the signal
    is shifting toward.

    Parameters:
        move_threshold: minimum cosine distance between consecutive
                        embeddings to trigger output
        amplify: how much to weight the delta (1.0 = pure direction,
                 0.0 = pure position)
        search_k: candidates to retrieve
        cooldown: minimum steps between outputs
    """

    def __init__(self, embedding_index, nexus_conn, search_k=64,
                 move_threshold=0.05, amplify=0.5, cooldown=3, max_lines=30):
        self.embedding_index = embedding_index
        self.nexus_conn = nexus_conn
        self.search_k = search_k
        self.move_threshold = move_threshold
        self.amplify = amplify
        self.cooldown = cooldown
        self.max_lines = max_lines

        self.prev_embedding = None
        self.steps_since_emit = 0
        self.prev_lines = set()

    def step(self, semantic_embedding):
        emb_np = semantic_embedding.detach().cpu().numpy().flatten()

        # Normalize
        norm = np.linalg.norm(emb_np)
        if norm > 0:
            emb_normed = emb_np / norm
        else:
            emb_normed = emb_np

        self.steps_since_emit += 1

        if self.prev_embedding is None:
            self.prev_embedding = emb_normed
            return []

        # Compute movement
        cos_sim = np.dot(emb_normed, self.prev_embedding)
        cos_dist = 1.0 - cos_sim

        if cos_dist < self.move_threshold or self.steps_since_emit < self.cooldown:
            return []

        # Direction of movement
        delta = emb_normed - self.prev_embedding
        delta_norm = np.linalg.norm(delta)
        if delta_norm > 0:
            delta = delta / delta_norm

        # Query = current position + amplified direction
        query = emb_normed + self.amplify * delta
        query_norm = np.linalg.norm(query)
        if query_norm > 0:
            query = query / query_norm

        self.prev_embedding = emb_normed
        self.steps_since_emit = 0

        results = _retrieve(self.embedding_index, self.nexus_conn,
                           query.reshape(1, -1), k=self.search_k)

        messages = [content for content, _ in results]
        lines = _lines_from_messages(messages, self.max_lines)

        # Remove lines seen in previous emission
        lines = [l for l in lines if l not in self.prev_lines]
        self.prev_lines = set(lines)

        return lines


class FocusMode:
    """
    Maintain an exponential moving average of embeddings. Only emit
    when the centroid shifts enough. Surfaces the persistent underlying
    theme rather than moment-to-moment noise.

    Parameters:
        alpha: EMA smoothing factor (lower = smoother, more stable)
        shift_threshold: minimum cosine distance of centroid movement to emit
        search_k: candidates to retrieve
        top_n: how many top results to show (closest to centroid)
    """

    def __init__(self, embedding_index, nexus_conn, search_k=48,
                 alpha=0.15, shift_threshold=0.02, top_n=20, max_lines=25):
        self.embedding_index = embedding_index
        self.nexus_conn = nexus_conn
        self.search_k = search_k
        self.alpha = alpha
        self.shift_threshold = shift_threshold
        self.top_n = top_n
        self.max_lines = max_lines

        self.centroid = None
        self.last_emit_centroid = None
        self.prev_lines = set()

    def step(self, semantic_embedding):
        emb_np = semantic_embedding.detach().cpu().numpy().flatten()

        norm = np.linalg.norm(emb_np)
        if norm > 0:
            emb_normed = emb_np / norm
        else:
            emb_normed = emb_np

        # Update EMA centroid
        if self.centroid is None:
            self.centroid = emb_normed.copy()
            self.last_emit_centroid = emb_normed.copy()
            return []

        self.centroid = self.alpha * emb_normed + (1.0 - self.alpha) * self.centroid

        # Re-normalize centroid
        c_norm = np.linalg.norm(self.centroid)
        if c_norm > 0:
            centroid_normed = self.centroid / c_norm
        else:
            centroid_normed = self.centroid

        # Check if centroid has shifted enough since last emission
        cos_sim = np.dot(centroid_normed, self.last_emit_centroid)
        cos_dist = 1.0 - cos_sim

        if cos_dist < self.shift_threshold:
            return []

        self.last_emit_centroid = centroid_normed.copy()

        # Retrieve based on smoothed centroid
        results = _retrieve(self.embedding_index, self.nexus_conn,
                           centroid_normed.reshape(1, -1), k=self.search_k)

        messages = [content for content, _ in results[:self.top_n]]
        lines = _lines_from_messages(messages, self.max_lines)

        # Deduplicate against previous emission
        lines = [l for l in lines if l not in self.prev_lines]
        self.prev_lines = set(_lines_from_messages(
            [content for content, _ in results[:self.top_n]], self.max_lines))

        return lines


class LayeredMode:
    """
    Run multiple timescales simultaneously. Show three sections:

      [fast]  — what just changed (high threshold, small k)
      [mid]   — recent theme (EMA with medium alpha)
      [slow]  — deep undercurrent (EMA with low alpha)

    Each layer only emits its section when its own threshold is crossed.
    At least one layer must fire for any output.
    """

    def __init__(self, embedding_index, nexus_conn, search_k=48, max_lines_per_layer=10):
        self.layers = {
            'fast': DriftMode(embedding_index, nexus_conn, search_k=search_k,
                             move_threshold=0.08, amplify=0.7, cooldown=1,
                             max_lines=max_lines_per_layer),
            'mid': FocusMode(embedding_index, nexus_conn, search_k=search_k,
                            alpha=0.25, shift_threshold=0.03, top_n=16,
                            max_lines=max_lines_per_layer),
            'slow': FocusMode(embedding_index, nexus_conn, search_k=search_k,
                             alpha=0.05, shift_threshold=0.015, top_n=12,
                             max_lines=max_lines_per_layer),
        }

    def step(self, semantic_embedding):
        sections = {}
        for name, layer in self.layers.items():
            lines = layer.step(semantic_embedding)
            if lines:
                sections[name] = lines

        if not sections:
            return []

        output = []
        for name in ['fast', 'mid', 'slow']:
            if name in sections:
                output.append(f"── {name} ──")
                output.extend(sections[name])
                output.append("")

        return output