#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@Project ：trinity_client
@File    ：service_att_recognition.py
@Author  ：周成融
@Date    ：2023/7/26 12:01:05
@detail  ：
"""
import json
import logging
import time

import redis
import tritonclient.grpc as grpcclient
import numpy as np
from minio import Minio
from tritonclient.utils import np_to_triton_dtype

from app.core.config import *
from app.schemas.generate_image import GenerateImageModel
from app.service.generate_image.utils.upload_sd_image import upload_png_sd
from app.service.utils.generate_uuid import generate_uuid

logger = logging.getLogger()


class GenerateImage:
    def __init__(self, request_data):
        self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
        self.grpc_client = grpcclient.InferenceServerClient(url=GI_MODEL_URL)
        self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
        self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
        self.channel = self.connection.channel()
        if request_data.mode == "txt2img":
            self.image = np.random.randint(0, 256, (1024, 1024, 3), dtype=np.uint8)
        else:
            self.image = request_data.image
        self.tasks_id = request_data.tasks_id
        self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
        self.prompt = request_data.prompt
        self.mode = request_data.mode
        self.batch_size = 1
        self.category = request_data.category
        self.index = 0

    def __del__(self):
        self.redis_client.close()
        self.grpc_client.close()
        self.connection.close()

    def __call__(self, *args, **kwargs):
        self.generate_data = json.dumps({'status': 'PENDING', 'message': "pending", 'data': ''})
        self.redis_client.set(self.tasks_id, self.generate_data)

    def callback(self, result, error):
        if error:
            generate_data = json.dumps({'status': 'FAILURE', 'message': f"{error}", 'data': f"{error}"})
            self.redis_client.set(self.tasks_id, generate_data)
        else:
            image_result = result.as_numpy("generated_image")[0]
            image_url = upload_png_sd(image_result, user_id=self.user_id, category=f"{self.category}", object_name=f"{self.tasks_id}.png")
            generate_data = json.dumps({'status': 'SUCCESS', 'message': 'success', 'data': f'{image_url}'})
            self.redis_client.set(self.tasks_id, generate_data)

    def read_tasks_status(self):
        status_data = json.loads(self.redis_client.get(self.tasks_id))
        return status_data

    def infer(self, inputs):
        return self.grpc_client.async_infer(
            model_name=GI_MODEL_NAME,
            inputs=inputs,
            callback=self.callback
        )

    def get_result(self):
        prompts = [self.prompt] * self.batch_size
        modes = [self.mode] * self.batch_size
        images = [self.image.astype(np.float16)] * self.batch_size

        text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
        mode_obj = np.array(modes, dtype="object").reshape((-1, 1))
        image_obj = np.array(images, dtype=np.float16).reshape((-1, 1024, 1024, 3))

        input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
        input_image = grpcclient.InferInput("input_image", image_obj.shape, "FP16")
        input_mode = grpcclient.InferInput("mode", mode_obj.shape, np_to_triton_dtype(text_obj.dtype))

        input_text.set_data_from_numpy(text_obj)
        input_image.set_data_from_numpy(image_obj)
        input_mode.set_data_from_numpy(mode_obj)

        inputs = [input_text, input_image, input_mode]
        ctx = self.infer(inputs)
        time_out = 60
        while time_out > 0:
            generate_data = self.read_tasks_status()
            if generate_data['status'] in ["REVOKED", "FAILURE"]:
                ctx.cancel()
                self.channel.basic_publish(exchange='', routing_key=GI_RABBITMQ_QUEUES, body=json.dumps(generate_data))
                logger.info(f" [x] Sent {json.dumps(generate_data, indent=4)}")
                break
            elif generate_data['status'] == "SUCCESS":
                self.channel.basic_publish(exchange='', routing_key=GI_RABBITMQ_QUEUES, body=json.dumps(generate_data))
                logger.info(f" [x] Sent {json.dumps(generate_data, indent=4)}")
                break
            time_out -= 1
            time.sleep(0.1)
        return self.read_tasks_status()


def infer_cancel(tasks_id):
    redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
    data = {'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'}
    generate_data = json.dumps({'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'})
    redis_client.set(tasks_id, generate_data)
    return data


if __name__ == '__main__':
    rd = GenerateImageModel(
        tasks_id="123-89",
        prompt='skeleton sitting by the side of a river looking soulful, concert poster, 4k, artistic',
        image_url="",
        mode='txt2img',
        category="test"
    )
    server = GenerateImage(rd)
    print(server.get_result())