"""
WebAssembly SIMD support for the Neurenix framework.

This module provides SIMD (Single Instruction, Multiple Data) acceleration
for WebAssembly, enabling vectorized operations for improved performance
in browser environments.
"""

from typing import Optional, List, Tuple, Union, Dict, Any
import numpy as np

from neurenix.tensor import Tensor
from neurenix.device import Device, DeviceType

def is_simd_supported() -> bool:
    """
    Check if WebAssembly SIMD is supported in the current environment.
    
    Returns:
        bool: True if WebAssembly SIMD is supported, False otherwise.
    """
    try:
        import js
        return hasattr(js.WebAssembly, 'Feature') and js.WebAssembly.Feature.simd
    except ImportError:
        try:
            import emscripten
            return bool(emscripten.run_script_int(
                "typeof WebAssembly.Feature !== 'undefined' && WebAssembly.Feature.simd ? 1 : 0"
            ))
        except ImportError:
            return False

def enable_simd_optimizations() -> bool:
    """
    Enable SIMD optimizations for WebAssembly if available.
    
    Returns:
        bool: True if SIMD optimizations were enabled, False otherwise.
    """
    if not is_simd_supported():
        return False
    
    try:
        import js
        js.globalThis.NEURENIX_SIMD_ENABLED = True
        return True
    except ImportError:
        try:
            import emscripten
            emscripten.run_script("globalThis.NEURENIX_SIMD_ENABLED = true;")
            return True
        except ImportError:
            return False

def simd_matmul(a: Tensor, b: Tensor) -> Tensor:
    """
    Perform matrix multiplication using SIMD instructions if available.
    
    Args:
        a: First tensor
        b: Second tensor
        
    Returns:
        Tensor: Result of matrix multiplication
    """
    if not is_simd_supported() or a.device.device_type != DeviceType.WEBGPU:
        return a @ b
    
    try:
        from neurenix.binding import wasm_simd_matmul
        return wasm_simd_matmul(a, b)
    except (ImportError, AttributeError):
        return a @ b

def simd_conv2d(input: Tensor, weight: Tensor, bias: Optional[Tensor] = None, 
                stride: Tuple[int, int] = (1, 1), padding: Tuple[int, int] = (0, 0),
                dilation: Tuple[int, int] = (1, 1), groups: int = 1) -> Tensor:
    """
    Perform 2D convolution using SIMD instructions if available.
    
    Args:
        input: Input tensor of shape (N, C_in, H_in, W_in)
        weight: Weight tensor of shape (C_out, C_in/groups, H_kernel, W_kernel)
        bias: Optional bias tensor of shape (C_out)
        stride: Stride of the convolution
        padding: Padding added to all sides of the input
        dilation: Spacing between kernel elements
        groups: Number of blocked connections from input to output channels
        
    Returns:
        Tensor: Result of convolution
    """
    if not is_simd_supported() or input.device.device_type != DeviceType.WEBGPU:
        from neurenix.nn.functional import conv2d
        return conv2d(input, weight, bias, stride, padding, dilation, groups)
    
    try:
        from neurenix.binding import wasm_simd_conv2d
        return wasm_simd_conv2d(input, weight, bias, stride, padding, dilation, groups)
    except (ImportError, AttributeError):
        from neurenix.nn.functional import conv2d
        return conv2d(input, weight, bias, stride, padding, dilation, groups)

def simd_batch_norm(input: Tensor, running_mean: Tensor, running_var: Tensor,
                   weight: Optional[Tensor] = None, bias: Optional[Tensor] = None,
                   training: bool = False, momentum: float = 0.1, eps: float = 1e-5) -> Tensor:
    """
    Perform batch normalization using SIMD instructions if available.
    
    Args:
        input: Input tensor of shape (N, C, *)
        running_mean: Running mean tensor of shape (C)
        running_var: Running variance tensor of shape (C)
        weight: Optional scale tensor of shape (C)
        bias: Optional bias tensor of shape (C)
        training: Whether in training mode
        momentum: Momentum value for running stats
        eps: Small constant for numerical stability
        
    Returns:
        Tensor: Normalized tensor
    """
    if not is_simd_supported() or input.device.device_type != DeviceType.WEBGPU:
        from neurenix.nn.functional import batch_norm
        return batch_norm(input, running_mean, running_var, weight, bias, training, momentum, eps)
    
    try:
        from neurenix.binding import wasm_simd_batch_norm
        return wasm_simd_batch_norm(input, running_mean, running_var, weight, bias, training, momentum, eps)
    except (ImportError, AttributeError):
        from neurenix.nn.functional import batch_norm
        return batch_norm(input, running_mean, running_var, weight, bias, training, momentum, eps)
