#!/bin/env python

""" 
Plan:
   Read in "dictionary" for list of words
   Read in pre-calculated "proper" embedding for each word from safetensor file
   Prompt user for a word from the list
   Generate a tensor array of distance to all the other known words
   Print out the 20 closest ones
"""


import sys
import json
import torch
from safetensors import safe_open

from transformers import CLIPProcessor,CLIPModel

clipsrc="openai/clip-vit-large-patch14"
processor=None
model=None

if len(sys.argv) == 2:
    embed_file=sys.argv[1]
else:
    print("You have to give name of embeddings file")
    sys.exit(1)

device=torch.device("cuda")


def init():
    global processor
    global model
    # Load the processor and model
    print("loading processor from "+clipsrc,file=sys.stderr)
    processor = CLIPProcessor.from_pretrained(clipsrc)
    print("done",file=sys.stderr)
    print("loading model from "+clipsrc,file=sys.stderr)
    model = CLIPModel.from_pretrained(clipsrc)
    print("done",file=sys.stderr)

    model = model.to(device)



print("read in words from dictionary now",file=sys.stderr)
with open("dictionary","r") as f:
    tokendict = f.readlines()
    wordlist = [token.strip() for token in tokendict]  # Remove trailing newlines
print(len(wordlist),"lines read")

print("read in embeddings now",file=sys.stderr)
model = safe_open(embed_file,framework="pt",device="cuda")
embs=model.get_tensor("embeddings")
embs.to(device)
print("Shape of loaded embeds =",embs.shape)

def standard_embed_calc(text):
    if processor == None:
        init()

    inputs = processor(text=text, return_tensors="pt")
    inputs.to(device)
    with torch.no_grad():
        text_features = model.get_text_features(**inputs)
    embedding = text_features[0]
    return embedding


def print_distances(targetemb):
    targetdistances = torch.cdist( targetemb.unsqueeze(0), embs, p=2)

    print("shape of distances...",targetdistances.shape)

    smallest_distances, smallest_indices = torch.topk(targetdistances[0], 20, largest=False)

    smallest_distances=smallest_distances.tolist()
    smallest_indices=smallest_indices.tolist()
    for d,i in zip(smallest_distances,smallest_indices):
        print(wordlist[i],"(",d,")")



# Find 10 closest tokens to targetword.
# Will include the word itself
def find_closest(targetword):
    try:
        targetindex=wordlist.index(targetword)
        targetemb=embs[targetindex]
        print_distances(targetemb)
        return
    except ValueError:
        print(targetword,"not found in cache")


    print("Now doing with full calc embed")
    targetemb=standard_embed_calc(targetword)
    print_distances(targetemb)


while True:
    input_text=input("Input a word now:")
    find_closest(input_text)