print('hello')

# %%
file_path = '/srv/ftp/sidefeeder/2024/01/26/sidefeeder_01_20240126103234.mp4'

from score_videos_embedding import *

# %%
score_video(file_path)
# %%
file_path = os.path.normpath(file_path)
nv_vid = nv_video(file_path, 32, frame_interval=10)
vec_path = get_vec_path(nv_vid.vid_path)
transforms = vis_processors["eval"].transform.transforms
all_results = list()
if True:
    tensor_np, frame_num = nv_vid.get_next_batch()
    tensor = tensor_np.permute([0, 3, 1, 2]).to(torch.float32) / 255
    tensor_pre1 = transforms[0](tensor)
    tensor_pre2 = transforms[1](tensor_pre1)
    tensor_mean = transforms[4](tensor_pre2)

sample = {"image": tensor_mean}
with torch.no_grad():
    imp = model.extract_features(sample)
    vector_to_store = imp.cpu().numpy()

all_results.append([vector_to_store, frame_num])

with torch.no_grad():
    imp_text = model.extract_features(sample_text)
# %%

fwe = torch.nn.functional.cosine_similarity(imp, imp_text[3,:])
# %%

L = imp_text[-1].cpu().numpy().tolist()
client = QdrantClient(host="localhost", grpc_port=6334, prefer_grpc=True)
# %%
results = client.search(collection_name = collection_name,
                                query_vector = L, limit=1, with_vectors=True)


results[0].payload['filepath'] == file_path

vec_s = imp[frame_num.index(389)]
vpath = get_vec_path(file_path)
# %%
import numpy as np
out = np.load(vpath)

out['embeds']
idx_look = out['frame_numbers'].tolist().index(389)
# %%
out = score_video(file_path)
print(np.average(np.abs(out[0][0] - imp[0].detach().cpu().numpy())))
# %%
from qdrant_client import QdrantClient
client = QdrantClient(host="localhost", grpc_port=6334, prefer_grpc=True)
# %%

L = np.random.random(512).tolist()
# %%
from qdrant_client.models import Filter, FieldCondition, Range, MatchText
from datetime import datetime, timedelta




collection_name = 'nuggets_clip'

query = 'hello'
max_age = 1
max_date = datetime.now()
min_date = max_date - timedelta(days=max_age)


days_step = (max_date- min_date).days
date_arrays = list()
for i in range(days_step):
    date_arrays.append(min_date + timedelta(days=i))

string_filter = list()
for cand_date in date_arrays:
    string_filter.append(cand_date.strftime('_%Y%m%d'))

should_list = list()
for str_filt in string_filter:
    ccond  = FieldCondition(key='filepath',match=MatchText(text=str_filt))
    should_list.append(ccond)
# %%
must = [FieldCondition(key="filepath",match=MatchText(text="sidefeeder")),
        FieldCondition(key="filepath",match=MatchText(text="_20240207"))
        ]

results = client.search(collection_name = collection_name, query_vector = L, limit=10,
                        query_filter = Filter(should=should_list))