import os
import cv2
import numpy as np
import torch
import torch.nn as nn
from PIL import Image
from moviepy.editor import VideoFileClip
import logging

logger = logging.getLogger(__name__)

class BMTPreprocessor:
    """
    Handles video and audio preprocessing for BMT feature extraction.
    """
    def __init__(self, target_sr=16000, target_fps=25):
        self.target_sr = target_sr
        self.target_fps = target_fps

    def extract_audio(self, video_path, output_audio_path):
        """
        Extracts audio from video and saves as WAV.
        """
        try:
            video = VideoFileClip(video_path)
            if video.audio:
                video.audio.write_audiofile(output_audio_path, fps=self.target_sr, logger=None)
                return True
            else:
                logger.warning(f"No audio track found in {video_path}")
                return False
        except Exception as e:
            logger.error(f"Audio extraction failed: {e}")
            return False

    def sample_video_frames(self, video_path, resize=(224, 224)):
        """
        Samples frames from video at the target FPS.
        """
        frames = []
        cap = cv2.VideoCapture(video_path)
        fps = cap.get(cv2.CAP_PROP_FPS)
        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        
        # Calculate step to match target FPS
        step = max(1, int(fps / self.target_fps))
        
        count = 0
        while True:
            ret, frame = cap.read()
            if not ret:
                break
            
            if count % step == 0:
                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                frame = cv2.resize(frame, resize)
                frames.append(frame)
            count += 1
            
        cap.release()
        return np.stack(frames) if frames else None

class I3D(nn.Module):
    """
    Simplified Inflated 3D ConvNet (I3D) architecture shell.
    In real production, this would load the full architecture.
    """
    def __init__(self, num_classes=400, in_channels=3):
        super(I3D, self).__init__()
        # This is a simplified placeholder. 
        # The actual model has many Inception modules.
        # We will use this to wrap the loaded .pt weights.
        self.conv3d_1a_7x7 = nn.Conv3d(in_channels, 64, kernel_size=(7, 7, 7), stride=(2, 2, 2), padding=(3, 3, 3))
        # ... more layers ...
        self.avg_pool = nn.AdaptiveAvgPool3d((1, 1, 1))
        self.logits = nn.Conv3d(1024, num_classes, kernel_size=(1, 1, 1))

    def forward(self, x):
        # Forward pass returning the 1024-dim feature vector before the logits layer
        # [Batch, 1024, T, H, W] -> AvgPool -> [Batch, 1024, 1, 1, 1]
        pass

class VGGish(nn.Module):
    """
    Simplified VGGish shell.
    """
    def __init__(self):
        super(VGGish, self).__init__()
        # ... layer definitions ...
        pass

    def forward(self, x):
        # Returning 128-dim embedding
        pass