# File name: model.py
import json
import os
import numpy as np
import torch
from starlette.requests import Request
from PIL import Image
import ray
from ray import serve
from clip_retrieval.load_clip import load_clip, get_tokenizer 
# from clip_retrieval.clip_client import ClipClient, Modality

# @serve.deployment(num_replicas=2, ray_actor_options={"num_cpus": 0.2, "num_gpus": 0.2})
# @serve.deployment(num_replicas=1, ray_actor_options={"num_cpus": 0.2, "num_gpus": 0.0})
@serve.deployment(num_replicas=10, ray_actor_options={"num_cpus": .2, "num_gpus": 0.0})
class CLIPTransform:
    def __init__(self):
        # os.environ["OMP_NUM_THREADS"] = "20"
        # torch.set_num_threads(20)
        # Load model
        self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
        self._clip_model="ViT-L/14"
        self._clip_model_id ="laion5B-L-14"
        self.model, self.preprocess = load_clip(self._clip_model, use_jit=True, device=self.device)
        self.tokenizer = get_tokenizer(self._clip_model)

        print ("using device", self.device)        

    def text_to_embeddings(self, prompt):
        text = self.tokenizer([prompt]).to(self.device)
        with torch.no_grad():
            prompt_embededdings = self.model.encode_text(text)
        prompt_embededdings /= prompt_embededdings.norm(dim=-1, keepdim=True)
        return(prompt_embededdings)
    
    def image_to_embeddings(self, input_im):
        input_im = Image.fromarray(input_im)
        prepro = self.preprocess(input_im).unsqueeze(0).to(self.device)
        with torch.no_grad():
            image_embeddings = self.model.encode_image(prepro)
        image_embeddings /= image_embeddings.norm(dim=-1, keepdim=True)
        return(image_embeddings)

    def preprocessed_image_to_emdeddings(self, prepro):
        with torch.no_grad():
            image_embeddings = self.model.encode_image(prepro)
        image_embeddings /= image_embeddings.norm(dim=-1, keepdim=True)
        return(image_embeddings)    

    async def __call__(self, http_request: Request) -> str:
        request = await http_request.json()
        # print(type(request))
        # print(str(request))
        # switch based if we are using text or image
        embeddings = None
        if "text" in request:
            prompt = request["text"]
            embeddings = self.text_to_embeddings(prompt)
        elif "image" in request:
            image_url = request["image_url"]
            # download image from url
            import requests
            from io import BytesIO
            input_image = Image.open(BytesIO(image_url))
            input_image = input_image.convert('RGB')
            input_image = np.array(input_image)
            embeddings = self.image_to_embeddings(input_image)
        elif "preprocessed_image" in request:
            prepro = request["preprocessed_image"]
            # create torch tensor on the device
            prepro = torch.tensor(prepro).to(self.device)
            embeddings = self.preprocessed_image_to_emdeddings(prepro)
        else:
            raise Exception("Invalid request")
        return embeddings.cpu().numpy().tolist()

deployment_graph = CLIPTransform.bind()