import project_path

import torch
from tqdm import tqdm
from functools import partial
import numpy as np
import json
import time
from unittest.mock import patch

# assumes yolov5 on sys.path
from lib.yolov5.models.experimental import attempt_load
from lib.yolov5.utils.torch_utils import select_device
from lib.yolov5.utils.general import clip_boxes, scale_boxes, xywh2xyxy
from lib.yolov5.utils.metrics import box_iou
import torch
import torchvision

from lib.fish_eye.tracker import Tracker


### Configuration options
WEIGHTS = 'models/v5m_896_300best.pt'
# will need to configure these based on GPU hardware
BATCH_SIZE = 32

CONF_THRES = 0.3 # detection
NMS_IOU  = 0.3 # NMS IOU
MIN_LENGTH = 0.3 # minimum fish length, in meters
MAX_AGE = 20 # time until missing fish get's new id
IOU_THRES = 0.01 # IOU threshold for tracking
MIN_HITS = 11 # minimum number of frames with a specific fish for it to count
###

def norm(bbox, w, h):
    """
    Normalize a bounding box.
    Args:
        bbox: list of length 4. Can be [x,y,w,h] or [x0,y0,x1,y1]
        w: image width
        h: image height
    """
    bb = bbox.copy()
    bb[0] /= w
    bb[1] /= h
    bb[2] /= w
    bb[3] /= h
    return bb

def do_full_inference(dataloader, image_meter_width, image_meter_height, gp=None, weights=WEIGHTS):
    
    model, device = setup_model(weights)

    load = False
    save = False

    if load:
        with open('static/example/inference_output.json', 'r') as f:
            json_object = json.load(f)
            inference = json_object['inference']
            width = json_object['width']
            height = json_object['height']
    else:
        inference, width, height = do_detection(dataloader, model, device, gp=gp)

    if save:
        json_object = {
            'inference': inference,
            'width': width,
            'height': height
        }
        json_text = json.dumps(json_object, indent=4)
        with open('static/example/inference_output.json', 'w') as f:
            f.write(json_text)
        return


    all_preds, real_width, real_height = do_suppression(dataloader, inference, width, height, gp=gp)

    results = do_tracking(all_preds, image_meter_width, image_meter_height, gp=gp)

    return results
    

def setup_model(weights_fp=WEIGHTS, imgsz=896, batch_size=32):
    if torch.cuda.is_available():
        device = select_device('0', batch_size=batch_size)
    else:
        print("CUDA not available. Using CPU inference.")
        device = select_device('cpu', batch_size=batch_size)
    
    # Setup model for inference
    model = attempt_load(weights_fp, device=device)
    half = device.type != 'cpu'  # half precision only supported on CUDA
    if half:
        model.half()
    model.eval()
    
    # Create dataloader for batched inference
    img = torch.zeros((1, 3, imgsz, imgsz), device=device)
    _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
    
    return model, device
                       
def do_detection(dataloader, model, device, gp=None, batch_size=BATCH_SIZE, verbose=True):
    """
    Args:
        frames_dir: a directory containing frames to be evaluated
        image_meter_width: the width of each image, in meters (used for fish length calculation)
        gp: a callback function which takes as input 1 parameter, (int) percent complete
        prep_for_marking: re-index fish for manual marking output
    """

    if (gp): gp(0, "Detection...")

    inference = []
    # Run detection
    with tqdm(total=len(dataloader)*batch_size, desc="Running detection", ncols=0, disable=not verbose) as pbar:
        for batch_i, (img, _, shapes) in enumerate(dataloader):

            if gp: gp(batch_i / len(dataloader), pbar.__str__())
            img = img.to(device, non_blocking=True)
            img = img.half() if device.type != 'cpu' else img.float()  # uint8 to fp16/32
            img /= 255.0  # 0 - 255 to 0.0 - 1.0
            size = tuple(img.shape)
            nb, _, height, width = size  # batch size, channels, height, width

            # Run model & NMS
            with torch.no_grad():
                inf_out, _ = model(img, augment=False) 

            inference.append(inf_out)
            pbar.update(1*batch_size)

    return inference, width, height

def do_suppression(dataloader, inference, width, height, gp=None, batch_size=BATCH_SIZE, conf_thres=CONF_THRES, iou_thres=NMS_IOU, verbose=True):
    """
    Args:
        frames_dir: a directory containing frames to be evaluated
        image_meter_width: the width of each image, in meters (used for fish length calculation)
        gp: a callback function which takes as input 1 parameter, (int) percent complete
        prep_for_marking: re-index fish for manual marking output
    """
    
    if (gp): gp(0, "Suppression...")
    # keep predictions to feed them ordered into the Tracker
    # TODO: how to deal with large files?
    all_preds = {}
    with tqdm(total=len(dataloader)*batch_size, desc="Running suppression", ncols=0, disable=not verbose) as pbar:
        for batch_i, (img, _, shapes) in enumerate(dataloader):

            if gp: gp(batch_i / len(dataloader), pbar.__str__())

            inf_out = inference[batch_i]
            with torch.no_grad():
                output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres)

            # Format results
            for si, pred in enumerate(output):
                # Clip boxes to image bounds and resize to input shape
                clip_boxes(pred, (height, width))
                box = pred[:, :4].clone()  # xyxy
                confs = pred[:, 4].clone().tolist()
                scale_boxes(img[si].shape[1:], box, shapes[si][0], shapes[si][1])  # to original shape
                
                # get boxes into tracker input format - normalized xyxy with confidence score
                # confidence score currently not used by tracker; set to 1.0
                boxes = None
                if box.shape[0]:
                    real_width = shapes[si][0][1]
                    real_height = shapes[si][0][0]
                    do_norm = partial(norm, w=shapes[si][0][1], h=shapes[si][0][0])
                    normed = list((map(do_norm, box[:, :4].tolist())))
                    boxes = np.stack([ [*bb, conf] for bb, conf in zip(normed, confs) ])
                frame_num = (batch_i, si)
                all_preds[frame_num] = boxes

            pbar.update(1*batch_size)
         
    return all_preds, real_width, real_height

def do_tracking(all_preds, image_meter_width, image_meter_height, gp=None, max_age=MAX_AGE, iou_thres=IOU_THRES, min_hits=MIN_HITS, min_length=MIN_LENGTH, verbose=True): 

    if (gp): gp(0, "Tracking...")

    # Initialize tracker
    clip_info = {
        'start_frame': 0,
        'end_frame': len(all_preds),
        'image_meter_width': image_meter_width,
        'image_meter_height': image_meter_height
    }
    tracker = Tracker(clip_info, args={ 'max_age': max_age, 'min_hits': 0, 'iou_threshold': iou_thres}, min_hits=min_hits)
    
    # Run tracking
    with tqdm(total=len(all_preds), desc="Running tracking", ncols=0, disable=not verbose) as pbar:
        for i, key in enumerate(sorted(all_preds.keys())):
            if gp: gp(i / len(all_preds), pbar.__str__())
            boxes = all_preds[key]
            if boxes is not None:
                tracker.update(boxes)
            else:
                tracker.update()
            pbar.update(1)

    json_data = tracker.finalize(min_length=min_length)

    return json_data


@patch('json.encoder.c_make_encoder', None)
def json_dump_round_float(some_object, out_path, num_digits=4):
    """Write a json file to disk with a specified level of precision.
    See: https://gist.github.com/Sukonnik-Illia/ed9b2bec1821cad437d1b8adb17406a3
    """
    # saving original method
    of = json.encoder._make_iterencode
    def inner(*args, **kwargs):
        args = list(args)
        # fifth argument is float formater which will we replace
        fmt_str = '{:.' + str(num_digits) + 'f}'
        args[4] = lambda o: fmt_str.format(o)
        return of(*args, **kwargs)
    
    with patch('json.encoder._make_iterencode', wraps=inner):
        return json.dump(some_object, open(out_path, 'w'), indent=2)
    


def non_max_suppression(
        prediction,
        conf_thres=0.25,
        iou_thres=0.45,
        max_det=300,
):
    """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections

    Returns:
         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
    """

    # Checks
    assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
    assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
    if isinstance(prediction, (list, tuple)):  # YOLOv5 model in validation model, output = (inference_out, loss_out)
        prediction = prediction[0]  # select only inference output

    device = prediction.device
    mps = 'mps' in device.type  # Apple MPS
    if mps:  # MPS not fully supported yet, convert tensors to CPU before NMS
        prediction = prediction.cpu()
    bs = prediction.shape[0]  # batch size
    xc = prediction[..., 4] > conf_thres  # candidates

    # Settings
    # min_wh = 2  # (pixels) minimum box width and height
    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
    redundant = True  # require redundant detections
    merge = False  # use merge-NMS

    output = [torch.zeros((0, 6), device=prediction.device)] * bs
    for xi, x in enumerate(prediction):  # image index, image inference

            
        # Keep boxes that pass confidence threshold
        x = x[xc[xi]]  # confidence

        # If none remain process next image
        if not x.shape[0]:
            continue
            
        # Compute conf
        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf


        # Box/Mask
        box = xywh2xyxy(x[:, :4])  # center_x, center_y, width, height) to (x1, y1, x2, y2)
        mask = x[:, 6:]  # zero columns if no masks

        # Detections matrix nx6 (xyxy, conf, cls)
        conf, j = x[:, 5:6].max(1, keepdim=True)
        x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]


        # Check shape
        n = x.shape[0]  # number of boxes
        if not n:  # no boxes
            continue
        x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence and remove excess boxes

        # Batched NMS
        boxes  = x[:, :4]  # boxes (offset by class), scores
        scores = x[:, 4]
        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS

        i = i[:max_det]  # limit detections
        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
            weights = iou * scores[None]  # box weights
            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
            if redundant:
                i = i[iou.sum(1) > 1]  # require redundancy

        output[xi] = x[i]
        if mps:
            output[xi] = output[xi].to(device)
        
        logging = False

    return output