import sys

sys.path.insert(0, "/home/thebears/source/models/yolov7")
import time
from datetime import datetime
import cv2
import numpy as np
from pymediainfo import MediaInfo
import inspect
import open_clip
import sys
import torch
import yaml
from models.experimental import attempt_load
from utils.general import check_img_size, non_max_suppression
from torchvision import transforms

device = torch.device("cuda")

pretrained_name = "webli"
#model_name = "ViT-SO400M-16-SigLIP2-512"
#model_name = 'ViT-SO400M-14-SigLIP-384'

clip_model, _, clip_preprocess_og = open_clip.create_model_and_transforms(
    model_name, pretrained=pretrained_name
)
tokenizer = open_clip.get_tokenizer('hf-hub:timm/'+model_name)
labels_list = ["A bird with a brown head and black body", "A bird with a black head and black body"]
text = tokenizer(labels_list, context_length=clip_model.context_length)

import torch.nn.functional as F
with torch.no_grad():
    text_features = clip_model.encode_text(text).detach().cpu()
    text_features = F.normalize(text_features, dim=-1).detach().cpu()
# %%

clip_model = clip_model.half().to(device)
clip_dtype = next(clip_model.parameters()).dtype
clip_img_size = clip_preprocess_og.transforms[0].size
_ = clip_model.encode_image(
    torch.rand(1, 3, *clip_img_size, dtype=clip_dtype, device=device)
)
clip_preprocess = transforms.Compose([clip_preprocess_og.transforms[x] for x in [0, 3]])

det_root_path = "/home/thebears/source/model_weights"
det_model_weights_root = os.path.join(det_root_path, "yolov7")
det_model_weights_path = os.path.join(det_model_weights_root, "best.pt")
det_data_yaml_path = os.path.join(det_model_weights_root, "inaturalist.yaml")
det_model = attempt_load(det_model_weights_path, map_location=device)
det_model = det_model.half().to(device)

det_dtype = next(det_model.parameters()).dtype
det_imgsz = 1280
det_stride = int(det_model.stride.max())
det_imgsz = check_img_size(det_imgsz, s=det_stride)
_ = det_model(torch.zeros(1, 3, det_imgsz, det_imgsz, dtype=det_dtype).to(device))

with open(det_data_yaml_path, "r") as ff:
    det_model_info = yaml.safe_load(ff)
    det_labels = det_model_info["names"]




array_score = clip_array
frame_numbers = [x[0] for x in array_score]
frame_values = [x[1] for x in array_score]
frame_as_tensor = (
    torch.from_numpy(np.stack(frame_values)[:, :, :, 0:3])
    .to(torch.float16)
    .to(device)
    .permute([0, 3, 1, 2])
)
def score_frames_det(array_score):
    frame_numbers = [x[0] for x in array_score]
    frame_values = [x[1] for x in array_score]
    frame_as_tensor = (
        torch.from_numpy(np.stack(frame_values)[:, :, :, 0:3])
        .to(torch.float16)
        .to(device)
        .permute([0, 3, 1, 2])
    )

    with torch.no_grad():
        frame_for_model = det_vid_preprocess(frame_as_tensor).div(255)[:,[2,1,0],:,:]
        det_preds = det_model(frame_for_model)[0]
        det_pred_post_nms = non_max_suppression(det_preds,0.25, 0.5)
        det_cpu_pred = [x.detach().cpu().numpy() for x in det_pred_post_nms]

#        frame_for_clip = clip_preprocess(frame_as_tensor[:,[0,1,2],:,:])
#        clip_pred = clip_model.encode_image(frame_for_clip).detach().cpu().numpy()

    return {"det": det_cpu_pred, "fr#": frame_numbers}

def score_frames_clip(array_score):
    frame_numbers = [x[0] for x in array_score]
    frame_values = [x[1] for x in array_score]
    frame_as_tensor = (
        torch.from_numpy(np.stack(frame_values)[:, :, :, 0:3])
        .to(torch.float16)
        .to(device)
        .permute([0, 3, 1, 2])
    )

    with torch.no_grad():
#        frame_for_model = det_vid_preprocess(frame_as_tensor).div(255)[:,[2,1,0],:,:]
#        det_preds = det_model(frame_for_model)[0]
#        det_pred_post_nms = non_max_suppression(det_preds,0.25, 0.5)
#        det_cpu_pred = [x.detach().cpu().numpy() for x in det_pred_post_nms]

        frame_for_clip = clip_preprocess(frame_as_tensor[:,[0,1,2],:,:])
        clip_pred = clip_model.encode_image(frame_for_clip).detach().cpu().numpy()


    return {"clip": clip_pred, "fr#": frame_numbers}





with torch.no_grad():
    frame_for_model = det_vid_preprocess(frame_as_tensor).div(255)[:,[2,1,0],:,:]
    det_preds = det_model(frame_for_model)[0]
    det_pred_post_nms = non_max_suppression(det_preds,0.25, 0.5)
    det_cpu_pred = [x.detach().cpu().numpy() for x in det_pred_post_nms]
    frame_for_clip = frame_as_tensor.div(255)
    
    frame_for_clip = clip_preprocess(frame_for_clip[:,(2,1,0),:,:])
    clip_pred = clip_model.encode_image(frame_for_clip).detach().cpu().numpy()

score_result = {"det": det_cpu_pred, "clip": clip_pred, "fr#": frame_numbers}


clip_orin = F.normalize(torch.from_numpy(score_result['clip']))
clip_tree = F.normalize(torch.from_numpy(saved_emb))
print(np.dot(clip_tree, clip_orin.T))


mvo = mean_vec_out[0]
ooo = frame_for_clip[0].cpu().numpy()

plt.close('all')
fig = plt.figure()

ax1 = fig.add_subplot(3,2,1)
ax1.imshow(mvo[0])
ax2 = fig.add_subplot(3,2,2)
ax2.imshow(ooo[0])
ax3 = fig.add_subplot(3,2,3)
ax3.imshow(mvo[1])
ax4 = fig.add_subplot(3,2,4)
ax4.imshow(ooo[1])
ax5 = fig.add_subplot(3,2,5)
ax5.imshow(mvo[2])
ax6 = fig.add_subplot(3,2,6)
ax6.imshow(ooo[2])
fig.show()

# %%


raw_vec_out
mean_vec_out

# %%
file_to_score = "/home/thebears/source/ml_code/short.mp4"
vec_file = '/home/thebears/source/ml_code/short.npz'
out = np.load(vec_file)

mean_vec_path = '/home/thebears/source/ml_code/as_np_mean.npy'
mean_vec_out = np.load(mean_vec_path)

raw_vec_path =  '/home/thebears/source/ml_code/as_np_raw.npy'
raw_vec_out = np.load(raw_vec_path)

saved_fr = list(out['frame_numbers'])
saved_emb = out['embeds']
import numpy as np



def get_video_info(file_path):
    file_info = MediaInfo.parse(file_path)
    video_info = None
    frame_count = 0
    if len(file_info.video_tracks) > 0:        video_info = file_info.video_tracks[0]

    video_info.frame_count = int(video_info.frame_count)
    return video_info


video_info = get_video_info(file_to_score)
vid_decoder = "h264parse"
if video_info.format.lower() == "HEVC".lower():
    vid_decoder = "h265parse"


gst_cmd = "filesrc location={file_to_score} ! qtdemux name=demux demux.video_0 ! queue ! {vid_decoder} ! nvv4l2decoder ! nvvidconv ! videoscale method=1 add-borders=false ! video/x-raw,width=1280,height=1280 ! appsink sync=false".format(
    file_to_score=file_to_score, vid_decoder=vid_decoder
)

# gst_cmd = "filesrc location={file_to_score} ! qtdemux name=demux demux.video_0 ! queue ! {vid_decoder} ! nvv4l2decoder ! nvvidconv ! videoscale method=1 add-borders=false !  appsink sync=false".format(file_to_score=file_to_score, vid_decoder=vid_decoder)

cap_handle = cv2.VideoCapture(gst_cmd, cv2.CAP_GSTREAMER)

target_max = det_imgsz
vid_h = video_info.height
vid_w = video_info.width

if vid_h > vid_w:
    target_h = target_max
    target_w = target_max * vid_w / vid_h
elif vid_h == vid_w:
    target_h = target_max
    target_w = target_max
elif vid_h < vid_w:
    target_h = target_max * vid_h / vid_w
    target_w = target_max

target_h = int(target_h)
target_w = int(target_w)

pad_amt = [None, None, None, None]
if target_w % det_stride != 0:
    off = det_stride - target_w % det_stride
    new_w = target_w + off
    pad_diff = new_w - target_w
    pad_left = round(pad_diff / 2)
    pad_right = pad_diff - pad_left
    pad_amt[0] = pad_left
    pad_amt[2] = pad_right
else:
    pad_amt[0] = 0
    pad_amt[2] = 0

if target_h % det_stride != 0:
    off = det_stride - target_h % det_stride
    new_h = target_h + off
    pad_diff = new_h - target_h
    pad_up = round(pad_diff / 2)
    pad_down = pad_diff - pad_up
    pad_amt[1] = pad_up
    pad_amt[3] = pad_down
else:
    pad_amt[1] = 0
    pad_amt[3] = 0


det_vid_preprocess = transforms.Compose(
    [transforms.Resize((target_h, target_w)), transforms.Pad(pad_amt, fill=127)]
)

batch_size = 6
clip_interval  = 10
array_score = list()
final_output = dict()
final_output["start_score_time"] = time.time()
final_output["num_frames"] = video_info.frame_count
st = time.time()
frame_numbers = list()
det_results = list()
clip_results = list()

clip_array = list()

for i in range(video_info.frame_count):
    success, frame_matrix = cap_handle.read()
    
    clip_array.append((i, frame_matrix))
        
    if not success:
        break


    array_score.append((i, frame_matrix))

    if len(array_score) >= batch_size:
        score_result = score_frames(array_score)
        

        det_results.extend(score_result["det"])
        clip_results.extend(score_result["clip"])
        frame_numbers.extend(score_result["fr#"])
        array_score = list()

    if not (i % clip_interval):
        print('do_clip')

if len(array_score) > 0:
    score_result = score_frames(array_score)
    det_results.extend(score_result["det"])
    clip_results.extend(score_result["clip"])
    frame_numbers.extend(score_result["fr#"])

cap_handle.release()
et = time.time()

final_output["end_score_time"] = time.time()
final_output["video"] = {
    "w": vid_w,
    "h": vid_h,
    "path": file_to_score,
    "target_w": target_w,
    "target_h": target_h,
    "pad_amt": pad_amt,
}

try:
    final_output['scoring_fps'] = final_output['num_frames']/ (final_output['end_score_time'] - final_output['start_score_time'])
except Exception as e:
    pass

final_output['scores'] = list()

for frame_number, frame in zip(frame_numbers, det_results):
    cframe_dict = dict()
    cframe_dict['frame'] = frame_number
    cframe_dict['score_number'] = frame_number
    cframe_dict['detections'] = list()

    for det in frame:
        data = dict()
        data['coords'] = [float(x) for x in list(det[0:4])]
        data['score'] = float(det[4])
        data['idx'] = int(det[5])

        try:
            data['name'] = det_labels[data['idx']]
        except:
            data['name'] = 'Code failed'

        cframe_dict['detections'].append(data)

    final_output['scores'].append(cframe_dict)