"""
Fixed MatAnyone Tensor Utilities
Ensures all tensor operations remain in tensor format
"""

import torch
import torch.nn.functional as F
import numpy as np
from typing import Tuple, Union


def pad_divide_by(in_tensor: torch.Tensor, d: int) -> Tuple[torch.Tensor, Tuple[int, int, int, int]]:
    """
    FIXED VERSION: Pad tensor to be divisible by d
    
    Args:
        in_tensor: Input tensor (..., H, W)
        d: Divisor value
    
    Returns:
        padded_tensor: Padded tensor
        pad_info: Padding information (left, right, top, bottom)
    """
    if not isinstance(in_tensor, torch.Tensor):
        raise TypeError(f"Expected torch.Tensor, got {type(in_tensor)} - this is the source of F.pad() errors!")
    
    # Get spatial dimensions
    h, w = in_tensor.shape[-2:]
    
    # Calculate required padding
    new_h = ((h + d - 1) // d) * d
    new_w = ((w + d - 1) // d) * d
    
    pad_h = new_h - h
    pad_w = new_w - w
    
    # Split padding evenly on both sides
    pad_top = pad_h // 2
    pad_bottom = pad_h - pad_top
    pad_left = pad_w // 2
    pad_right = pad_w - pad_left
    
    # PyTorch padding format: (left, right, top, bottom)
    pad_array = (pad_left, pad_right, pad_top, pad_bottom)
    
    # CRITICAL: Ensure input is tensor before F.pad
    out = F.pad(in_tensor, pad_array, mode='reflect')
    
    return out, pad_array


def unpad_tensor(padded_tensor: torch.Tensor, pad_info: Tuple[int, int, int, int]) -> torch.Tensor:
    """
    Remove padding from tensor
    
    Args:
        padded_tensor: Padded tensor
        pad_info: Padding information (left, right, top, bottom)
    
    Returns:
        unpadded_tensor: Original size tensor
    """
    if not isinstance(padded_tensor, torch.Tensor):
        raise TypeError(f"Expected torch.Tensor, got {type(padded_tensor)}")
    
    pad_left, pad_right, pad_top, pad_bottom = pad_info
    
    # Get current dimensions
    h, w = padded_tensor.shape[-2:]
    
    # Calculate crop boundaries
    top = pad_top
    bottom = h - pad_bottom if pad_bottom > 0 else h
    left = pad_left  
    right = w - pad_right if pad_right > 0 else w
    
    # Crop tensor
    unpadded = padded_tensor[..., top:bottom, left:right]
    
    return unpadded


def ensure_tensor(input_data: Union[torch.Tensor, np.ndarray], device: torch.device = None) -> torch.Tensor:
    """
    Convert input to tensor if needed and move to device
    
    Args:
        input_data: Input data (tensor or numpy array)
        device: Target device
    
    Returns:
        torch.Tensor: Converted tensor
    """
    if isinstance(input_data, np.ndarray):
        tensor = torch.from_numpy(input_data).float()
    elif isinstance(input_data, torch.Tensor):
        tensor = input_data.float()
    else:
        raise TypeError(f"Unsupported input type: {type(input_data)}")
    
    if device is not None:
        tensor = tensor.to(device)
    
    return tensor


def normalize_tensor(tensor: torch.Tensor, target_range: Tuple[float, float] = (0.0, 1.0)) -> torch.Tensor:
    """
    Normalize tensor to target range
    
    Args:
        tensor: Input tensor
        target_range: Target (min, max) range
    
    Returns:
        torch.Tensor: Normalized tensor
    """
    if not isinstance(tensor, torch.Tensor):
        raise TypeError(f"Expected torch.Tensor, got {type(tensor)}")
    
    min_val, max_val = target_range
    
    # Normalize to [0, 1] first
    tensor_min = tensor.min()
    tensor_max = tensor.max()
    
    if tensor_max > tensor_min:
        normalized = (tensor - tensor_min) / (tensor_max - tensor_min)
    else:
        normalized = tensor - tensor_min
    
    # Scale to target range
    scaled = normalized * (max_val - min_val) + min_val
    
    return scaled


def resize_tensor(tensor: torch.Tensor, 
                 size: Tuple[int, int], 
                 mode: str = 'bilinear',
                 align_corners: bool = False) -> torch.Tensor:
    """
    Resize tensor while maintaining tensor format
    
    Args:
        tensor: Input tensor (C, H, W) or (B, C, H, W)
        size: Target (height, width)
        mode: Interpolation mode
        align_corners: Align corners flag
    
    Returns:
        torch.Tensor: Resized tensor
    """
    if not isinstance(tensor, torch.Tensor):
        raise TypeError(f"Expected torch.Tensor, got {type(tensor)}")
    
    original_dims = tensor.ndim
    
    # Add batch dimension if needed
    if tensor.ndim == 3:
        tensor = tensor.unsqueeze(0)
    
    # Resize
    resized = F.interpolate(tensor, size=size, mode=mode, align_corners=align_corners)
    
    # Remove batch dimension if it was added
    if original_dims == 3:
        resized = resized.squeeze(0)
    
    return resized


def safe_tensor_operation(func):
    """
    Decorator to ensure tensor operations receive tensor inputs
    """
    def wrapper(*args, **kwargs):
        # Check all args are tensors
        for i, arg in enumerate(args):
            if hasattr(arg, 'shape') and not isinstance(arg, torch.Tensor):
                raise TypeError(f"Argument {i} must be torch.Tensor, got {type(arg)}")
        
        return func(*args, **kwargs)
    
    return wrapper


@safe_tensor_operation  
def tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:
    """
    Safely convert tensor to numpy array
    
    Args:
        tensor: Input tensor
    
    Returns:
        np.ndarray: Numpy array
    """
    if tensor.requires_grad:
        tensor = tensor.detach()
    
    if tensor.is_cuda:
        tensor = tensor.cpu()
    
    return tensor.numpy()


def validate_tensor_shapes(*tensors: torch.Tensor, expected_dims: int = None) -> bool:
    """
    Validate tensor shapes are compatible
    
    Args:
        tensors: Input tensors to validate
        expected_dims: Expected number of dimensions
    
    Returns:
        bool: True if valid
    """
    if not tensors:
        return True
    
    if expected_dims is not None:
        for tensor in tensors:
            if tensor.ndim != expected_dims:
                raise ValueError(f"Expected {expected_dims}D tensor, got {tensor.ndim}D")
    
    # Check spatial dimensions match (last 2 dims)
    reference_shape = tensors[0].shape[-2:]
    for tensor in tensors[1:]:
        if tensor.shape[-2:] != reference_shape:
            raise ValueError(f"Spatial dimensions mismatch: {reference_shape} vs {tensor.shape[-2:]}")
    
    return True