import random

import numpy as np
import cv2
import os
import io
import oss2
from PIL import Image

import dashscope
from dashscope import MultiModalConversation

from http import HTTPStatus
import re
import requests
from .log import logger
import concurrent.futures

dashscope.api_key = os.getenv("API_KEY_QW")
# oss
access_key_id = os.getenv("ACCESS_KEY_ID")
access_key_secret = os.getenv("ACCESS_KEY_SECRET")
bucket_name = os.getenv("BUCKET_NAME")
endpoint = os.getenv("ENDPOINT")

# The original Space depends on a private Alibaba Cloud OSS bucket
# (ACCESS_KEY_ID / ACCESS_KEY_SECRET / BUCKET_NAME / ENDPOINT secrets) that
# isn't available here. Without this guard, `oss2.Bucket(...)` raises
# AttributeError: 'NoneType' object has no attribute 'strip' at import time,
# crashing the whole app before it ever reaches the (unrelated) CUDA bug this
# duplicate is meant to repro. Degrade to `bucket = None` instead so import
# succeeds; only the OSS-upload path (unreachable without those secrets)
# fails at call time with a clear error.
if access_key_id and access_key_secret and bucket_name and endpoint:
    bucket = oss2.Bucket(oss2.Auth(access_key_id, access_key_secret), endpoint, bucket_name)
else:
    logger.error(
        "OSS credentials (ACCESS_KEY_ID/ACCESS_KEY_SECRET/BUCKET_NAME/ENDPOINT) "
        "are not set; image-upload features will be disabled."
    )
    bucket = None
oss_path = "ashui"
oss_path_img_gallery = "ashui_img_gallery"

def download_img_pil(index, img_url):
    # print(img_url)
    r = requests.get(img_url, stream=True)
    if r.status_code == 200:
        img = Image.open(io.BytesIO(r.content))
        return (index, img)
    else:
        logger.error(f"Fail to download: {img_url}")


def download_images(img_urls, batch_size):
    imgs_pil = [None] * batch_size
    # worker_results = []
    with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
        to_do = []
        for i, url in enumerate(img_urls):
            future = executor.submit(download_img_pil, i, url)
            to_do.append(future)

        for future in concurrent.futures.as_completed(to_do):
            ret = future.result()
            # worker_results.append(ret)
            index, img_pil = ret
            imgs_pil[index] = img_pil  # 按顺序排列url，后续下载关联的图片或者svg需要使用

    return imgs_pil

def upload_np_2_oss(input_image, name="cache.png", gallery=False):
    if bucket is None:
        raise RuntimeError(
            "OSS bucket is not configured (missing ACCESS_KEY_ID/ACCESS_KEY_SECRET/"
            "BUCKET_NAME/ENDPOINT secrets); cannot upload images."
        )
    imgByteArr = io.BytesIO()
    Image.fromarray(input_image).save(imgByteArr, format="PNG")
    imgByteArr = imgByteArr.getvalue()

    if gallery:
        path = oss_path_img_gallery
    else:
        path = oss_path

    bucket.put_object(path+"/"+name, imgByteArr)  # data为数据，可以是图片
    ret = bucket.sign_url('GET', path+"/"+name, 60*60*24)  # 返回值为链接，参数依次为，方法/oss上文件路径/过期时间(s)
    del imgByteArr
    return ret


def call_with_messages(prompt):
    messages = [
        {'role': 'user', 'content': prompt}]
    response = dashscope.Generation.call(
        'qwen-14b-chat',
        messages=messages,
        result_format='message',  # set the result is message format.
    )
    if response.status_code == HTTPStatus.OK:
        return response['output']["choices"][0]["message"]['content']
    else:
        print('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
            response.request_id, response.status_code,
            response.code, response.message
        ))
        return None

def HWC3(x):
    assert x.dtype == np.uint8
    if x.ndim == 2:
        x = x[:, :, None]
    assert x.ndim == 3
    H, W, C = x.shape
    assert C == 1 or C == 3 or C == 4
    if C == 3:
        return x
    if C == 1:
        return np.concatenate([x, x, x], axis=2)
    if C == 4:
        color = x[:, :, 0:3].astype(np.float32)
        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
        y = color * alpha + 255.0 * (1.0 - alpha)
        y = y.clip(0, 255).astype(np.uint8)
        return y


def resize_image(input_image, resolution):
    H, W, C = input_image.shape
    H = float(H)
    W = float(W)
    k = float(resolution) / min(H, W)
    H *= k
    W *= k
    H = int(np.round(H / 64.0)) * 64
    W = int(np.round(W / 64.0)) * 64
    img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
    return img


def nms(x, t, s):
    x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)

    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)

    y = np.zeros_like(x)

    for f in [f1, f2, f3, f4]:
        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)

    z = np.zeros_like(y, dtype=np.uint8)
    z[y > t] = 255
    return z


def make_noise_disk(H, W, C, F):
    noise = np.random.uniform(low=0, high=1, size=((H // F) + 2, (W // F) + 2, C))
    noise = cv2.resize(noise, (W + 2 * F, H + 2 * F), interpolation=cv2.INTER_CUBIC)
    noise = noise[F: F + H, F: F + W]
    noise -= np.min(noise)
    noise /= np.max(noise)
    if C == 1:
        noise = noise[:, :, None]
    return noise


def min_max_norm(x):
    x -= np.min(x)
    x /= np.maximum(np.max(x), 1e-5)
    return x


def safe_step(x, step=2):
    y = x.astype(np.float32) * float(step + 1)
    y = y.astype(np.int32).astype(np.float32) / float(step)
    return y


def img2mask(img, H, W, low=10, high=90):
    assert img.ndim == 3 or img.ndim == 2
    assert img.dtype == np.uint8

    if img.ndim == 3:
        y = img[:, :, random.randrange(0, img.shape[2])]
    else:
        y = img

    y = cv2.resize(y, (W, H), interpolation=cv2.INTER_CUBIC)

    if random.uniform(0, 1) < 0.5:
        y = 255 - y

    return y < np.percentile(y, random.randrange(low, high))