import numpy as np
import cv2
import torch
import torchvision.transforms.functional as TF

import sys as _sys
from keyword import iskeyword as _iskeyword
from operator import itemgetter as _itemgetter

from segment_anything import SamPredictor

from comfy import model_management


################################################################################
### namedtuple
################################################################################

try:
    from _collections import _tuplegetter
except ImportError:
    _tuplegetter = lambda index, doc: property(_itemgetter(index), doc=doc)

def namedtuple(typename, field_names, *, rename=False, defaults=None, module=None):
    """Returns a new subclass of tuple with named fields.

    >>> Point = namedtuple('Point', ['x', 'y'])
    >>> Point.__doc__                   # docstring for the new class
    'Point(x, y)'
    >>> p = Point(11, y=22)             # instantiate with positional args or keywords
    >>> p[0] + p[1]                     # indexable like a plain tuple
    33
    >>> x, y = p                        # unpack like a regular tuple
    >>> x, y
    (11, 22)
    >>> p.x + p.y                       # fields also accessible by name
    33
    >>> d = p._asdict()                 # convert to a dictionary
    >>> d['x']
    11
    >>> Point(**d)                      # convert from a dictionary
    Point(x=11, y=22)
    >>> p._replace(x=100)               # _replace() is like str.replace() but targets named fields
    Point(x=100, y=22)

    """

    # Validate the field names.  At the user's option, either generate an error
    # message or automatically replace the field name with a valid name.
    if isinstance(field_names, str):
        field_names = field_names.replace(',', ' ').split()
    field_names = list(map(str, field_names))
    typename = _sys.intern(str(typename))

    if rename:
        seen = set()
        for index, name in enumerate(field_names):
            if (not name.isidentifier()
                or _iskeyword(name)
                or name.startswith('_')
                or name in seen):
                field_names[index] = f'_{index}'
            seen.add(name)

    for name in [typename] + field_names:
        if type(name) is not str:
            raise TypeError('Type names and field names must be strings')
        if not name.isidentifier():
            raise ValueError('Type names and field names must be valid '
                             f'identifiers: {name!r}')
        if _iskeyword(name):
            raise ValueError('Type names and field names cannot be a '
                             f'keyword: {name!r}')

    seen = set()
    for name in field_names:
        if name.startswith('_') and not rename:
            raise ValueError('Field names cannot start with an underscore: '
                             f'{name!r}')
        if name in seen:
            raise ValueError(f'Encountered duplicate field name: {name!r}')
        seen.add(name)

    field_defaults = {}
    if defaults is not None:
        defaults = tuple(defaults)
        if len(defaults) > len(field_names):
            raise TypeError('Got more default values than field names')
        field_defaults = dict(reversed(list(zip(reversed(field_names),
                                                reversed(defaults)))))

    # Variables used in the methods and docstrings
    field_names = tuple(map(_sys.intern, field_names))
    num_fields = len(field_names)
    arg_list = ', '.join(field_names)
    if num_fields == 1:
        arg_list += ','
    repr_fmt = '(' + ', '.join(f'{name}=%r' for name in field_names) + ')'
    tuple_new = tuple.__new__
    _dict, _tuple, _len, _map, _zip = dict, tuple, len, map, zip

    # Create all the named tuple methods to be added to the class namespace

    namespace = {
        '_tuple_new': tuple_new,
        '__builtins__': {},
        '__name__': f'namedtuple_{typename}',
    }
    code = f'lambda _cls, {arg_list}: _tuple_new(_cls, ({arg_list}))'
    __new__ = eval(code, namespace)
    __new__.__name__ = '__new__'
    __new__.__doc__ = f'Create new instance of {typename}({arg_list})'
    if defaults is not None:
        __new__.__defaults__ = defaults

    @classmethod
    def _make(cls, iterable):
        result = tuple_new(cls, iterable)
        if _len(result) != num_fields:
            raise TypeError(f'Expected {num_fields} arguments, got {len(result)}')
        return result

    _make.__func__.__doc__ = (f'Make a new {typename} object from a sequence '
                              'or iterable')

    def _replace(self, /, **kwds):
        result = self._make(_map(kwds.pop, field_names, self))
        if kwds:
            raise ValueError(f'Got unexpected field names: {list(kwds)!r}')
        return result

    _replace.__doc__ = (f'Return a new {typename} object replacing specified '
                        'fields with new values')

    def __repr__(self):
        'Return a nicely formatted representation string'
        return self.__class__.__name__ + repr_fmt % self

    def _asdict(self):
        'Return a new dict which maps field names to their values.'
        return _dict(_zip(self._fields, self))

    def __getnewargs__(self):
        'Return self as a plain tuple.  Used by copy and pickle.'
        return _tuple(self)

    # Modify function metadata to help with introspection and debugging
    for method in (
        __new__,
        _make.__func__,
        _replace,
        __repr__,
        _asdict,
        __getnewargs__,
    ):
        method.__qualname__ = f'{typename}.{method.__name__}'

    # Build-up the class namespace dictionary
    # and use type() to build the result class
    class_namespace = {
        '__doc__': f'{typename}({arg_list})',
        '__slots__': (),
        '_fields': field_names,
        '_field_defaults': field_defaults,
        '__new__': __new__,
        '_make': _make,
        '_replace': _replace,
        '__repr__': __repr__,
        '_asdict': _asdict,
        '__getnewargs__': __getnewargs__,
        '__match_args__': field_names,
    }
    for index, name in enumerate(field_names):
        doc = _sys.intern(f'Alias for field number {index}')
        class_namespace[name] = _tuplegetter(index, doc)

    result = type(typename, (tuple,), class_namespace)

    # For pickling to work, the __module__ variable needs to be set to the frame
    # where the named tuple is created.  Bypass this step in environments where
    # sys._getframe is not defined (Jython for example) or sys._getframe is not
    # defined for arguments greater than 0 (IronPython), or where the user has
    # specified a particular module.
    if module is None:
        try:
            module = _sys._getframe(1).f_globals.get('__name__', '__main__')
        except (AttributeError, ValueError):
            pass
    if module is not None:
        result.__module__ = module

    return result


SEG = namedtuple("SEG",
                 ['cropped_image', 'cropped_mask', 'confidence', 'crop_region', 'bbox', 'label', 'control_net_wrapper'],
                 defaults=[None])

def crop_ndarray4(npimg, crop_region):
    x1 = crop_region[0]
    y1 = crop_region[1]
    x2 = crop_region[2]
    y2 = crop_region[3]

    cropped = npimg[:, y1:y2, x1:x2, :]

    return cropped

crop_tensor4 = crop_ndarray4

def crop_ndarray2(npimg, crop_region):
    x1 = crop_region[0]
    y1 = crop_region[1]
    x2 = crop_region[2]
    y2 = crop_region[3]

    cropped = npimg[y1:y2, x1:x2]

    return cropped

def crop_image(image, crop_region):
    return crop_tensor4(image, crop_region)

def normalize_region(limit, startp, size):
    if startp < 0:
        new_endp = min(limit, size)
        new_startp = 0
    elif startp + size > limit:
        new_startp = max(0, limit - size)
        new_endp = limit
    else:
        new_startp = startp
        new_endp = min(limit, startp+size)

    return int(new_startp), int(new_endp)

def make_crop_region(w, h, bbox, crop_factor, crop_min_size=None):
    x1 = bbox[0]
    y1 = bbox[1]
    x2 = bbox[2]
    y2 = bbox[3]

    bbox_w = x2 - x1
    bbox_h = y2 - y1

    crop_w = bbox_w * crop_factor
    crop_h = bbox_h * crop_factor

    if crop_min_size is not None:
        crop_w = max(crop_min_size, crop_w)
        crop_h = max(crop_min_size, crop_h)

    kernel_x = x1 + bbox_w / 2
    kernel_y = y1 + bbox_h / 2

    new_x1 = int(kernel_x - crop_w / 2)
    new_y1 = int(kernel_y - crop_h / 2)

    # make sure position in (w,h)
    new_x1, new_x2 = normalize_region(w, new_x1, crop_w)
    new_y1, new_y2 = normalize_region(h, new_y1, crop_h)

    return [new_x1, new_y1, new_x2, new_y2]

def create_segmasks(results):
    bboxs = results[1]
    segms = results[2]
    confidence = results[3]

    results = []
    for i in range(len(segms)):
        item = (bboxs[i], segms[i].astype(np.float32), confidence[i])
        results.append(item)
    return results

def dilate_masks(segmasks, dilation_factor, iter=1):
    if dilation_factor == 0:
        return segmasks

    dilated_masks = []
    kernel = np.ones((abs(dilation_factor), abs(dilation_factor)), np.uint8)

    kernel = cv2.UMat(kernel)

    for i in range(len(segmasks)):
        cv2_mask = segmasks[i][1]

        cv2_mask = cv2.UMat(cv2_mask)

        if dilation_factor > 0:
            dilated_mask = cv2.dilate(cv2_mask, kernel, iter)
        else:
            dilated_mask = cv2.erode(cv2_mask, kernel, iter)

        dilated_mask = dilated_mask.get()

        item = (segmasks[i][0], dilated_mask, segmasks[i][2])
        dilated_masks.append(item)

    return dilated_masks

def is_same_device(a, b):
    a_device = torch.device(a) if isinstance(a, str) else a
    b_device = torch.device(b) if isinstance(b, str) else b
    return a_device.type == b_device.type and a_device.index == b_device.index

class SafeToGPU:
    def __init__(self, size):
        self.size = size

    def to_device(self, obj, device):
        if is_same_device(device, 'cpu'):
            obj.to(device)
        else:
            if is_same_device(obj.device, 'cpu'):  # cpu to gpu
                model_management.free_memory(self.size * 1.3, device)
                if model_management.get_free_memory(device) > self.size * 1.3:
                    try:
                        obj.to(device)
                    except:
                        print(f"WARN: The model is not moved to the '{device}' due to insufficient memory. [1]")
                else:
                    print(f"WARN: The model is not moved to the '{device}' due to insufficient memory. [2]")

def center_of_bbox(bbox):
    w, h = bbox[2] - bbox[0], bbox[3] - bbox[1]
    return bbox[0] + w/2, bbox[1] + h/2

def sam_predict(predictor, points, plabs, bbox, threshold):
    point_coords = None if not points else np.array(points)
    point_labels = None if not plabs else np.array(plabs)

    box = np.array([bbox]) if bbox is not None else None

    cur_masks, scores, _ = predictor.predict(point_coords=point_coords, point_labels=point_labels, box=box)

    total_masks = []

    selected = False
    max_score = 0
    max_mask = None
    for idx in range(len(scores)):
        if scores[idx] > max_score:
            max_score = scores[idx]
            max_mask = cur_masks[idx]

        if scores[idx] >= threshold:
            selected = True
            total_masks.append(cur_masks[idx])
        else:
            pass

    if not selected and max_mask is not None:
        total_masks.append(max_mask)

    return total_masks

def make_2d_mask(mask):
    if len(mask.shape) == 4:
        return mask.squeeze(0).squeeze(0)

    elif len(mask.shape) == 3:
        return mask.squeeze(0)

    return mask

def gen_detection_hints_from_mask_area(x, y, mask, threshold, use_negative):
    mask = make_2d_mask(mask)

    points = []
    plabs = []

    # minimum sampling step >= 3
    y_step = max(3, int(mask.shape[0] / 20))
    x_step = max(3, int(mask.shape[1] / 20))

    for i in range(0, len(mask), y_step):
        for j in range(0, len(mask[i]), x_step):
            if mask[i][j] > threshold:
                points.append((x + j, y + i))
                plabs.append(1)
            elif use_negative and mask[i][j] == 0:
                points.append((x + j, y + i))
                plabs.append(0)

    return points, plabs

def gen_negative_hints(w, h, x1, y1, x2, y2):
    npoints = []
    nplabs = []

    # minimum sampling step >= 3
    y_step = max(3, int(w / 20))
    x_step = max(3, int(h / 20))

    for i in range(10, h - 10, y_step):
        for j in range(10, w - 10, x_step):
            if not (x1 - 10 <= j and j <= x2 + 10 and y1 - 10 <= i and i <= y2 + 10):
                npoints.append((j, i))
                nplabs.append(0)

    return npoints, nplabs

def generate_detection_hints(image, seg, center, detection_hint, dilated_bbox, mask_hint_threshold, use_small_negative,
                             mask_hint_use_negative):
    [x1, y1, x2, y2] = dilated_bbox

    points = []
    plabs = []
    if detection_hint == "center-1":
        points.append(center)
        plabs = [1]  # 1 = foreground point, 0 = background point

    elif detection_hint == "horizontal-2":
        gap = (x2 - x1) / 3
        points.append((x1 + gap, center[1]))
        points.append((x1 + gap * 2, center[1]))
        plabs = [1, 1]

    elif detection_hint == "vertical-2":
        gap = (y2 - y1) / 3
        points.append((center[0], y1 + gap))
        points.append((center[0], y1 + gap * 2))
        plabs = [1, 1]

    elif detection_hint == "rect-4":
        x_gap = (x2 - x1) / 3
        y_gap = (y2 - y1) / 3
        points.append((x1 + x_gap, center[1]))
        points.append((x1 + x_gap * 2, center[1]))
        points.append((center[0], y1 + y_gap))
        points.append((center[0], y1 + y_gap * 2))
        plabs = [1, 1, 1, 1]

    elif detection_hint == "diamond-4":
        x_gap = (x2 - x1) / 3
        y_gap = (y2 - y1) / 3
        points.append((x1 + x_gap, y1 + y_gap))
        points.append((x1 + x_gap * 2, y1 + y_gap))
        points.append((x1 + x_gap, y1 + y_gap * 2))
        points.append((x1 + x_gap * 2, y1 + y_gap * 2))
        plabs = [1, 1, 1, 1]

    elif detection_hint == "mask-point-bbox":
        center = center_of_bbox(seg.bbox)
        points.append(center)
        plabs = [1]

    elif detection_hint == "mask-area":
        points, plabs = gen_detection_hints_from_mask_area(seg.crop_region[0], seg.crop_region[1],
                                                           seg.cropped_mask,
                                                           mask_hint_threshold, use_small_negative)

    if mask_hint_use_negative == "Outter":
        npoints, nplabs = gen_negative_hints(image.shape[0], image.shape[1],
                                             seg.crop_region[0], seg.crop_region[1],
                                             seg.crop_region[2], seg.crop_region[3])

        points += npoints
        plabs += nplabs

    return points, plabs

def combine_masks2(masks):
    if len(masks) == 0:
        return None
    else:
        initial_cv2_mask = np.array(masks[0]).astype(np.uint8)
        combined_cv2_mask = initial_cv2_mask

        for i in range(1, len(masks)):
            cv2_mask = np.array(masks[i]).astype(np.uint8)

            if combined_cv2_mask.shape == cv2_mask.shape:
                combined_cv2_mask = cv2.bitwise_or(combined_cv2_mask, cv2_mask)
            else:
                # do nothing - incompatible mask
                pass

        mask = torch.from_numpy(combined_cv2_mask)
        return mask

def dilate_mask(mask, dilation_factor, iter=1):
    if dilation_factor == 0:
        return make_2d_mask(mask)

    mask = make_2d_mask(mask)

    kernel = np.ones((abs(dilation_factor), abs(dilation_factor)), np.uint8)

    mask = cv2.UMat(mask)
    kernel = cv2.UMat(kernel)

    if dilation_factor > 0:
        result = cv2.dilate(mask, kernel, iter)
    else:
        result = cv2.erode(mask, kernel, iter)

    return result.get()

def convert_and_stack_masks(masks):
    if len(masks) == 0:
        return None

    mask_tensors = []
    for mask in masks:
        mask_array = np.array(mask, dtype=np.uint8)
        mask_tensor = torch.from_numpy(mask_array)
        mask_tensors.append(mask_tensor)

    stacked_masks = torch.stack(mask_tensors, dim=0)
    stacked_masks = stacked_masks.unsqueeze(1)

    return stacked_masks

def merge_and_stack_masks(stacked_masks, group_size):
    if stacked_masks is None:
        return None

    num_masks = stacked_masks.size(0)
    merged_masks = []

    for i in range(0, num_masks, group_size):
        subset_masks = stacked_masks[i:i + group_size]
        merged_mask = torch.any(subset_masks, dim=0)
        merged_masks.append(merged_mask)

    if len(merged_masks) > 0:
        merged_masks = torch.stack(merged_masks, dim=0)

    return merged_masks

def make_sam_mask_segmented(sam_model, segs, image, detection_hint, dilation,
                            threshold, bbox_expansion, mask_hint_threshold, mask_hint_use_negative):
    if sam_model.is_auto_mode:
        device = model_management.get_torch_device()
        sam_model.safe_to.to_device(sam_model, device=device)

    try:
        predictor = SamPredictor(sam_model)
        image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
        predictor.set_image(image, "RGB")

        total_masks = []

        use_small_negative = mask_hint_use_negative == "Small"

        # seg_shape = segs[0]
        segs = segs[1]
        if detection_hint == "mask-points":
            points = []
            plabs = []

            for i in range(len(segs)):
                bbox = segs[i].bbox
                center = center_of_bbox(bbox)
                points.append(center)

                # small point is background, big point is foreground
                if use_small_negative and bbox[2] - bbox[0] < 10:
                    plabs.append(0)
                else:
                    plabs.append(1)

            detected_masks = sam_predict(predictor, points, plabs, None, threshold)
            total_masks += detected_masks

        else:
            for i in range(len(segs)):
                bbox = segs[i].bbox
                center = center_of_bbox(bbox)
                x1 = max(bbox[0] - bbox_expansion, 0)
                y1 = max(bbox[1] - bbox_expansion, 0)
                x2 = min(bbox[2] + bbox_expansion, image.shape[1])
                y2 = min(bbox[3] + bbox_expansion, image.shape[0])

                dilated_bbox = [x1, y1, x2, y2]

                points, plabs = generate_detection_hints(image, segs[i], center, detection_hint, dilated_bbox,
                                                         mask_hint_threshold, use_small_negative,
                                                         mask_hint_use_negative)

                detected_masks = sam_predict(predictor, points, plabs, dilated_bbox, threshold)

                total_masks += detected_masks

        # merge every collected masks
        mask = combine_masks2(total_masks)

    finally:
        if sam_model.is_auto_mode:
            sam_model.cpu()

        pass

    mask_working_device = torch.device("cpu")

    if mask is not None:
        mask = mask.float()
        mask = dilate_mask(mask.cpu().numpy(), dilation)
        mask = torch.from_numpy(mask)
        mask = mask.to(device=mask_working_device)
    else:
        # Extracting batch, height and width
        height, width, _ = image.shape
        mask = torch.zeros(
            (height, width), dtype=torch.float32, device=mask_working_device
        )  # empty mask

    stacked_masks = convert_and_stack_masks(total_masks)

    return (mask, merge_and_stack_masks(stacked_masks, group_size=3))

def tensor2mask(t: torch.Tensor) -> torch.Tensor:
    size = t.size()
    if (len(size) < 4):
        return t
    if size[3] == 1:
        return t[:,:,:,0]
    elif size[3] == 4:
        # Not sure what the right thing to do here is. Going to try to be a little smart and use alpha unless all alpha is 1 in case we'll fallback to RGB behavior
        if torch.min(t[:, :, :, 3]).item() != 1.:
            return t[:,:,:,3]
    return TF.rgb_to_grayscale(tensor2rgb(t).permute(0,3,1,2), num_output_channels=1)[:,0,:,:]

def tensor2rgb(t: torch.Tensor) -> torch.Tensor:
    size = t.size()
    if (len(size) < 4):
        return t.unsqueeze(3).repeat(1, 1, 1, 3)
    if size[3] == 1:
        return t.repeat(1, 1, 1, 3)
    elif size[3] == 4:
        return t[:, :, :, :3]
    else:
        return t

def tensor2rgba(t: torch.Tensor) -> torch.Tensor:
    size = t.size()
    if (len(size) < 4):
        return t.unsqueeze(3).repeat(1, 1, 1, 4)
    elif size[3] == 1:
        return t.repeat(1, 1, 1, 4)
    elif size[3] == 3:
        alpha_tensor = torch.ones((size[0], size[1], size[2], 1))
        return torch.cat((t, alpha_tensor), dim=3)
    else:
        return t