"""
Copyright © 2023 Howard Hughes Medical Institute, Authored by Carsen Stringer and Marius Pachitariu.
"""

import os, sys, time, shutil, tempfile, datetime, pathlib, subprocess
import logging
import numpy as np
from tqdm import trange, tqdm
from urllib.parse import urlparse
import tempfile
import cv2
from scipy.stats import mode
import fastremap
from . import transforms, dynamics, utils, plot, metrics, resnet_torch

import torch
from torch import nn
from torch.utils import mkldnn as mkldnn_utils

TORCH_ENABLED = True

core_logger = logging.getLogger(__name__)
tqdm_out = utils.TqdmToLogger(core_logger, level=logging.INFO)


def use_gpu(gpu_number=0, use_torch=True):
    """ 
    Check if GPU is available for use.

    Args:
        gpu_number (int): The index of the GPU to be used. Default is 0.
        use_torch (bool): Whether to use PyTorch for GPU check. Default is True.

    Returns:
        bool: True if GPU is available, False otherwise.

    Raises:
        ValueError: If use_torch is False, as cellpose only runs with PyTorch now.
    """
    if use_torch:
        return _use_gpu_torch(gpu_number)
    else:
        raise ValueError("cellpose only runs with PyTorch now")


def _use_gpu_torch(gpu_number=0):
    """
    Checks if CUDA or MPS is available and working with PyTorch.

    Args:
        gpu_number (int): The GPU device number to use (default is 0).

    Returns:
        bool: True if CUDA or MPS is available and working, False otherwise.
    """
    try:
        device = torch.device("cuda:" + str(gpu_number))
        _ = torch.zeros((1,1)).to(device)
        core_logger.info("** TORCH CUDA version installed and working. **")
        return True
    except:
        pass
    try:
        device = torch.device('mps:' + str(gpu_number))
        _ = torch.zeros((1,1)).to(device)
        core_logger.info('** TORCH MPS version installed and working. **')
        return True
    except:
        core_logger.info('Neither TORCH CUDA nor MPS version not installed/working.')
        return False


def assign_device(use_torch=True, gpu=False, device=0):
    """
    Assigns the device (CPU or GPU or mps) to be used for computation.

    Args:
        use_torch (bool, optional): Whether to use torch for GPU detection. Defaults to True.
        gpu (bool, optional): Whether to use GPU for computation. Defaults to False.
        device (int or str, optional): The device index or name to be used. Defaults to 0.

    Returns:
        torch.device, bool (True if GPU is used, False otherwise)
    """

    if isinstance(device, str):
        if device != "mps" or not(gpu and torch.backends.mps.is_available()):
            device = int(device)
    if gpu and use_gpu(use_torch=True):
        try:
            if torch.cuda.is_available():
                device = torch.device(f'cuda:{device}')
                core_logger.info(">>>> using GPU (CUDA)")
                gpu = True
                cpu = False
        except:
            gpu = False
            cpu = True
        try:
            if torch.backends.mps.is_available():
                device = torch.device('mps')
                core_logger.info(">>>> using GPU (MPS)")
                gpu = True
                cpu = False
        except:
            gpu = False
            cpu = True
    else:
        device = torch.device('cpu')
        core_logger.info('>>>> using CPU')
        gpu = False
        cpu = True
    
    if cpu:
        device = torch.device("cpu")
        core_logger.info(">>>> using CPU")
        gpu = False
    return device, gpu


def check_mkl(use_torch=True):
    """
    Checks if MKL-DNN is enabled and working.

    Args:
        use_torch (bool, optional): Whether to use torch. Defaults to True.

    Returns:
        bool: True if MKL-DNN is enabled, False otherwise.
    """
    mkl_enabled = torch.backends.mkldnn.is_available()
    if mkl_enabled:
        mkl_enabled = True
    else:
        core_logger.info(
            "WARNING: MKL version on torch not working/installed - CPU version will be slightly slower."
        )
        core_logger.info(
            "see https://pytorch.org/docs/stable/backends.html?highlight=mkl")
    return mkl_enabled


def _to_device(x, device):
    """
    Converts the input tensor or numpy array to the specified device.

    Args:
        x (torch.Tensor or numpy.ndarray): The input tensor or numpy array.
        device (torch.device): The target device.

    Returns:
        torch.Tensor: The converted tensor on the specified device.
    """
    if not isinstance(x, torch.Tensor):
        X = torch.from_numpy(x).to(device, dtype=torch.float32)
        return X
    else:
        return x


def _from_device(X):
    """
    Converts a PyTorch tensor from the device to a NumPy array on the CPU.

    Args:
        X (torch.Tensor): The input PyTorch tensor.

    Returns:
        numpy.ndarray: The converted NumPy array.
    """
    x = X.detach().cpu().numpy()
    return x


def _forward(net, x):
    """Converts images to torch tensors, runs the network model, and returns numpy arrays.

    Args:
        net (torch.nn.Module): The network model.
        x (numpy.ndarray): The input images.

    Returns:
        Tuple[numpy.ndarray, numpy.ndarray]: The output predictions (flows and cellprob) and style features.
    """
    X = _to_device(x, net.device)
    net.eval()
    if net.mkldnn:
        net = mkldnn_utils.to_mkldnn(net)
    with torch.no_grad():
        y, style = net(X)[:2]
    del X
    y = _from_device(y)
    style = _from_device(style)
    return y, style


def run_net(net, imgi, batch_size=8, augment=False, tile_overlap=0.1, bsize=224,
            rsz=None):
    """ 
    Run network on stack of images.
    
    (faster if augment is False)

    Args:
        net (class): cellpose network (model.net)
        imgi (np.ndarray): The input image or stack of images of size [Lz x Ly x Lx x nchan].
        batch_size (int, optional): Number of tiles to run in a batch. Defaults to 8.
        rsz (float, optional): Resize coefficient(s) for image. Defaults to 1.0.
        augment (bool, optional): Tiles image with overlapping tiles and flips overlapped regions to augment. Defaults to False.
        tile_overlap (float, optional): Fraction of overlap of tiles when computing flows. Defaults to 0.1.
        bsize (int, optional): Size of tiles to use in pixels [bsize x bsize]. Defaults to 224.

    Returns:
        Tuple[numpy.ndarray, numpy.ndarray]: outputs of network y and style. If tiled `y` is averaged in tile overlaps. Size of [Ly x Lx x 3] or [Lz x Ly x Lx x 3].
            y[...,0] is Y flow; y[...,1] is X flow; y[...,2] is cell probability. 
            style is a 1D array of size 256 summarizing the style of the image, if tiled `style` is averaged over tiles.
    """
    # run network
    nout = net.nout
    Lz, Ly0, Lx0, nchan = imgi.shape 
    if rsz is not None:
        if not isinstance(rsz, list) and not isinstance(rsz, np.ndarray):
            rsz = [rsz, rsz]
        Lyr, Lxr = int(Ly0 * rsz[0]), int(Lx0 * rsz[1])
    else:
        Lyr, Lxr = Ly0, Lx0
    ypad1, ypad2, xpad1, xpad2 = transforms.get_pad_yx(Lyr, Lxr)
    pads = np.array([[0, 0], [ypad1, ypad2], [xpad1, xpad2]])
    Ly, Lx = Lyr + ypad1 + ypad2, Lxr + xpad1 + xpad2
    if augment:
        ny = max(2, int(np.ceil(2. * Ly / bsize)))
        nx = max(2, int(np.ceil(2. * Lx / bsize)))
        ly, lx = bsize, bsize
    else:
        ny = 1 if Ly <= bsize else int(np.ceil((1. + 2 * tile_overlap) * Ly / bsize))
        nx = 1 if Lx <= bsize else int(np.ceil((1. + 2 * tile_overlap) * Lx / bsize))
        ly, lx = min(bsize, Ly), min(bsize, Lx)
    yf = np.zeros((Lz, nout, Ly, Lx), "float32")
    styles = np.zeros((Lz, 256), "float32")
    
    # run multiple slices at the same time
    ntiles = ny * nx
    nimgs = max(1, batch_size // ntiles) # number of imgs to run in the same batch
    niter = int(np.ceil(Lz / nimgs))
    ziterator = (trange(niter, file=tqdm_out, mininterval=30) 
                    if niter > 10 or Lz > 1 else range(niter))
    for k in ziterator:
        inds = np.arange(k * nimgs, min(Lz, (k + 1) * nimgs))
        IMGa = np.zeros((ntiles * len(inds), nchan, ly, lx), "float32")
        for i, b in enumerate(inds):
            # pad image for net so Ly and Lx are divisible by 4
            imgb = transforms.resize_image(imgi[b], rsz=rsz) if rsz is not None else imgi[b].copy()
            imgb = np.pad(imgb.transpose(2,0,1), pads, mode="constant")
            IMG, ysub, xsub, Ly, Lx = transforms.make_tiles(
                imgb, bsize=bsize, augment=augment,
                tile_overlap=tile_overlap)
            IMGa[i * ntiles : (i+1) * ntiles] = np.reshape(IMG, 
                                            (ny * nx, nchan, ly, lx))
        
        ya = np.zeros((IMGa.shape[0], nout, ly, lx), "float32")
        stylea = np.zeros((IMGa.shape[0], 256), "float32")
        for j in range(0, IMGa.shape[0], batch_size):
            bslc = slice(j, min(j + batch_size, IMGa.shape[0]))
            ya[bslc], stylea[bslc] = _forward(net, IMGa[bslc])
        for i, b in enumerate(inds):
            y = ya[i * ntiles : (i + 1) * ntiles]
            if augment:
                y = np.reshape(y, (ny, nx, 3, ly, lx))
                y = transforms.unaugment_tiles(y)
                y = np.reshape(y, (-1, 3, ly, lx))
            yfi = transforms.average_tiles(y, ysub, xsub, Ly, Lx)
            yf[b] = yfi[:, :imgb.shape[-2], :imgb.shape[-1]]
            stylei = stylea[i * ntiles:(i + 1) * ntiles].sum(axis=0)
            stylei /= (stylei**2).sum()**0.5
            styles[b] = stylei
    # slices from padding
    yf = yf[:, :, ypad1 : Ly-ypad2, xpad1 : Lx-xpad2]
    yf = yf.transpose(0,2,3,1)   
    return yf, np.array(styles)


def run_3D(net, imgs, batch_size=8, augment=False,
           tile_overlap=0.1, bsize=224, net_ortho=None,
           progress=None):
    """ 
    Run network on image z-stack.
    
    (faster if augment is False)

    Args:
        imgs (np.ndarray): The input image stack of size [Lz x Ly x Lx x nchan].
        batch_size (int, optional): Number of tiles to run in a batch. Defaults to 8.
        rsz (float, optional): Resize coefficient(s) for image. Defaults to 1.0.
        anisotropy (float, optional): for 3D segmentation, optional rescaling factor (e.g. set to 2.0 if Z is sampled half as dense as X or Y). Defaults to None.
        augment (bool, optional): Tiles image with overlapping tiles and flips overlapped regions to augment. Defaults to False.
        tile_overlap (float, optional): Fraction of overlap of tiles when computing flows. Defaults to 0.1.
        bsize (int, optional): Size of tiles to use in pixels [bsize x bsize]. Defaults to 224.
        net_ortho (class, optional): cellpose network for orthogonal ZY and ZX planes. Defaults to None.
        progress (QProgressBar, optional): pyqt progress bar. Defaults to None.

    Returns:
        Tuple[numpy.ndarray, numpy.ndarray]: outputs of network y and style. If tiled `y` is averaged in tile overlaps. Size of [Ly x Lx x 3] or [Lz x Ly x Lx x 3].
            y[...,0] is Z flow; y[...,1] is Y flow; y[...,2] is X flow; y[...,3] is cell probability. 
            style is a 1D array of size 256 summarizing the style of the image, if tiled `style` is averaged over tiles.
    """
    sstr = ["YX", "ZY", "ZX"]
    pm = [(0, 1, 2, 3), (1, 0, 2, 3), (2, 0, 1, 3)]
    ipm = [(0, 1, 2), (1, 0, 2), (1, 2, 0)]
    cp = [(1, 2), (0, 2), (0, 1)]
    cpy = [(0, 1), (0, 1), (0, 1)]
    shape = imgs.shape[:-1]
    #cellprob = np.zeros(shape, "float32")
    yf = np.zeros((*shape, 4), "float32")
    for p in range(3):
        xsl = imgs.transpose(pm[p])
        # per image
        core_logger.info("running %s: %d planes of size (%d, %d)" %
                         (sstr[p], shape[pm[p][0]], shape[pm[p][1]], shape[pm[p][2]]))
        y, style = run_net(net if p==0 or net_ortho is None else net_ortho, 
                           xsl, batch_size=batch_size, augment=augment, 
                           bsize=bsize, tile_overlap=tile_overlap, 
                           rsz=None)
        yf[..., -1] += y[..., -1].transpose(ipm[p])
        for j in range(2):
            yf[..., cp[p][j]] += y[..., cpy[p][j]].transpose(ipm[p])
        y = None; del y
    
        if progress is not None:
            progress.setValue(25 + 15 * p)
    
    return yf, style