# WiFi DensePose Implementation - Core Neural Network Architecture
# Based on "DensePose From WiFi" by Carnegie Mellon University

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
from typing import Dict, List, Tuple, Optional
from collections import OrderedDict

# CSI Phase Sanitization Module
class CSIPhaseProcessor:
    """
    Processes raw CSI phase data through unwrapping, filtering, and linear fitting
    Based on the phase sanitization methodology from the paper
    """
    
    def __init__(self, num_subcarriers: int = 30):
        self.num_subcarriers = num_subcarriers
    
    def unwrap_phase(self, phase_data: np.ndarray) -> np.ndarray:
        """
        Unwrap phase values to handle discontinuities
        Args:
            phase_data: Raw phase data of shape (samples, frequencies, antennas, antennas)
        Returns:
            Unwrapped phase data
        """
        unwrapped = np.copy(phase_data)
        
        for i in range(1, phase_data.shape[1]):  # Along frequency dimension
            diff = unwrapped[:, i] - unwrapped[:, i-1]
            
            # Apply unwrapping logic
            unwrapped[:, i] = np.where(diff > np.pi, 
                                     unwrapped[:, i-1] + diff - 2*np.pi,
                                     unwrapped[:, i])
            unwrapped[:, i] = np.where(diff < -np.pi,
                                     unwrapped[:, i-1] + diff + 2*np.pi,
                                     unwrapped[:, i])
        
        return unwrapped
    
    def apply_filters(self, phase_data: np.ndarray) -> np.ndarray:
        """
        Apply median and uniform filters to eliminate outliers
        """
        # Simple moving average as approximation for filters
        filtered = np.copy(phase_data)
        
        # Apply simple smoothing in time dimension
        for i in range(1, phase_data.shape[0]-1):
            filtered[i] = (phase_data[i-1] + phase_data[i] + phase_data[i+1]) / 3
        
        # Apply smoothing in frequency dimension
        for i in range(1, phase_data.shape[1]-1):
            filtered[:, i] = (filtered[:, i-1] + filtered[:, i] + filtered[:, i+1]) / 3
        
        return filtered
    
    def linear_fitting(self, phase_data: np.ndarray) -> np.ndarray:
        """
        Apply linear fitting to remove systematic phase drift
        """
        fitted_data = np.copy(phase_data)
        F = self.num_subcarriers
        
        for sample_idx in range(phase_data.shape[0]):
            for ant_i in range(phase_data.shape[2]):
                for ant_j in range(phase_data.shape[3]):
                    phase_seq = phase_data[sample_idx, :, ant_i, ant_j]
                    
                    # Calculate linear coefficients
                    alpha1 = (phase_seq[-1] - phase_seq[0]) / (2 * np.pi * F)
                    alpha0 = np.mean(phase_seq)
                    
                    # Apply linear fitting
                    frequencies = np.arange(1, F + 1)
                    linear_trend = alpha1 * frequencies + alpha0
                    fitted_data[sample_idx, :, ant_i, ant_j] = phase_seq - linear_trend
        
        return fitted_data
    
    def sanitize_phase(self, raw_phase: np.ndarray) -> np.ndarray:
        """
        Complete phase sanitization pipeline
        """
        # Step 1: Unwrap phase
        unwrapped = self.unwrap_phase(raw_phase)
        
        # Step 2: Apply filters
        filtered = self.apply_filters(unwrapped)
        
        # Step 3: Linear fitting
        sanitized = self.linear_fitting(filtered)
        
        return sanitized

# Modality Translation Network
class ModalityTranslationNetwork(nn.Module):
    """
    Translates CSI domain features to spatial domain features
    Input: 150x3x3 amplitude and phase tensors
    Output: 3x720x1280 feature map
    """
    
    def __init__(self, input_dim: int = 1350, hidden_dim: int = 512, output_height: int = 720, output_width: int = 1280):
        super(ModalityTranslationNetwork, self).__init__()
        
        self.input_dim = input_dim
        self.output_height = output_height
        self.output_width = output_width
        
        # Amplitude encoder
        self.amplitude_encoder = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim//2),
            nn.ReLU(),
            nn.Linear(hidden_dim//2, hidden_dim//4),
            nn.ReLU()
        )
        
        # Phase encoder
        self.phase_encoder = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim//2),
            nn.ReLU(),
            nn.Linear(hidden_dim//2, hidden_dim//4),
            nn.ReLU()
        )
        
        # Feature fusion
        self.fusion_mlp = nn.Sequential(
            nn.Linear(hidden_dim//2, hidden_dim//4),
            nn.ReLU(),
            nn.Linear(hidden_dim//4, 24*24),  # Reshape to 24x24
            nn.ReLU()
        )
        
        # Spatial processing
        self.spatial_conv = nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d((6, 6))  # Compress to 6x6
        )
        
        # Upsampling to target resolution
        self.upsample = nn.Sequential(
            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),  # 12x12
            nn.ReLU(),
            nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),   # 24x24
            nn.ReLU(),
            nn.ConvTranspose2d(32, 16, kernel_size=4, stride=2, padding=1),   # 48x48
            nn.ReLU(),
            nn.ConvTranspose2d(16, 8, kernel_size=4, stride=2, padding=1),    # 96x96
            nn.ReLU(),
        )
        
        # Final upsampling to target size
        self.final_upsample = nn.ConvTranspose2d(8, 3, kernel_size=1)
        
    def forward(self, amplitude_tensor: torch.Tensor, phase_tensor: torch.Tensor) -> torch.Tensor:
        batch_size = amplitude_tensor.shape[0]
        
        # Flatten input tensors
        amplitude_flat = amplitude_tensor.view(batch_size, -1)  # [B, 1350]
        phase_flat = phase_tensor.view(batch_size, -1)          # [B, 1350]
        
        # Encode features
        amp_features = self.amplitude_encoder(amplitude_flat)   # [B, 128]
        phase_features = self.phase_encoder(phase_flat)         # [B, 128]
        
        # Fuse features
        fused_features = torch.cat([amp_features, phase_features], dim=1)  # [B, 256]
        spatial_features = self.fusion_mlp(fused_features)      # [B, 576]
        
        # Reshape to 2D feature map
        spatial_map = spatial_features.view(batch_size, 1, 24, 24)  # [B, 1, 24, 24]
        
        # Apply spatial convolutions
        conv_features = self.spatial_conv(spatial_map)          # [B, 128, 6, 6]
        
        # Upsample
        upsampled = self.upsample(conv_features)                # [B, 8, 96, 96]
        
        # Final upsampling using interpolation to reach target size
        final_features = self.final_upsample(upsampled)         # [B, 3, 96, 96]
        
        # Interpolate to target resolution
        output = F.interpolate(final_features, size=(self.output_height, self.output_width), 
                             mode='bilinear', align_corners=False)
        
        return output

print("Modality Translation Network implementation completed!")
print("Input: 150x3x3 amplitude and phase tensors")
print("Output: 3x720x1280 feature map")