import towhee
from towhee.dc2 import pipe, ops
from towhee import triton_client

import onnxruntime
import numpy
import torch
from statistics import mean

import time
import argparse

import os
import re
import warnings
import logging
from transformers import logging as t_logging

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
warnings.filterwarnings("ignore")
t_logging.set_verbosity_error()

parser = argparse.ArgumentParser()
parser.add_argument('--model', required=True, type=str)
parser.add_argument('--pipe', action='store_true')
parser.add_argument('--triton', action='store_true')
parser.add_argument('--onnx', action='store_true')
parser.add_argument('--atol', type=float, default=1e-3)
parser.add_argument('--num', type=int, default=100)
parser.add_argument('--device', type=str, default='cpu')
args = parser.parse_args()

device_id = 0 if args.device in ['cpu', 'cuda'] else int(args.device[-1])
model_name = args.model
# model_name = 'resnet50'
# model_name = 'vgg16'
# model_name = 'deit3_base_patch16_224'
# model_name = 'deit_tiny_patch16_224'
# model_name = 'deit_base_distilled_patch16_224'
# model_name = 'convnext_base'
# model_name = 'vit_base_patch16_224'
# model_name = 'tf_efficientnet_b5'


p = (
    pipe.input('url')
        .map('url', 'img', ops.image_decode.cv2_rgb())
        .map('img', 'vec', ops.image_embedding.timm(model_name=model_name, device=args.device))
        .output('vec')
)

data = '../towhee.jpeg'
out1 = p(data).get()[0]
print('Pipe: OK')

if args.num and args.pipe:
    qps = []
    for _ in range(10):
        start = time.time()
        p.batch([data] * args.num)
        # for _ in range(args.num):
        #     p(data)
        end = time.time()
        q = args.num / (end - start)
        qps.append(q)
    print('Pipe qps:', mean(qps))

if args.triton:
    client = triton_client.Client(url='localhost:8000')
    out2 = client(data)[0][0]
    print('Triton: OK')

    if numpy.allclose(out1, out2, atol=args.atol):
        print('Check accuracy: OK')
    else:
        max_diff = numpy.abs(out1 - out2).max()
        min_diff = numpy.abs(out1 - out2).min()
        mean_diff = numpy.abs(out1 - out2).mean()
        print(f'Check accuracy: atol is larger than {args.atol}.')
        print(f'Maximum absolute difference is {max_diff}.')
        print(f'Minimum absolute difference is {min_diff}.')
        print(f'Mean difference is {mean_diff}.')

    if args.num:
        qps = []
        for _ in range(10):
            start = time.time()
            client.batch([data] * args.num)
            end = time.time()
            q = args.num / (end - start)
            qps.append(q)
        print('Triton qps:', mean(qps))

if args.onnx:
    op = ops.image_embedding.timm(model_name=model_name, device='cpu').get_op()
    decoder = ops.image_decode.cv2_rgb().get_op()
    # if not os.path.exists('test.onnx'):
    op.save_model('onnx', 'test.onnx')
    sess = onnxruntime.InferenceSession('test.onnx',
                                        providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
    onnx.set_providers(['CUDAExecutionProvider'], [{'device_id': device_id}])
    inputs = decoder(data)
    inputs = op.convert_img(inputs)
    inputs = op.tfms(inputs).unsqueeze(0)
    out3 = sess.run(None, input_feed={'input_0': inputs.cpu().detach().numpy()})[0]
    op.device = 'cuda' if args.device != 'cpu' else 'cpu'
    out3 = op.post_proc(torch.from_numpy(out3)).cpu().detach().numpy()
    print('Onnx: OK')
    if numpy.allclose(out1, out3, atol=args.atol):
        print('Check accuracy: OK')
    else:
        max_diff = numpy.abs(out1 - out3).max()
        min_diff = numpy.abs(out1 - out3).min()
        mean_diff = numpy.abs(out1 - out3).mean()
        print(f'Check accuracy: atol is larger than {args.atol}.')
        print(f'Maximum absolute difference is {max_diff}.')
        print(f'Minimum absolute difference is {min_diff}.')
        print(f'Mean difference is {mean_diff}.')

    if args.num:
        qps = []
        for _ in range(10):
            start = time.time()
            for _ in range(args.num):
                inputs = decoder(data)
                inputs = op.convert_img(inputs)
                inputs = op.tfms(inputs).unsqueeze(0)
                outs = sess.run(None, input_feed={'input_0': inputs.cpu().detach().numpy()})[0]
                outs = op.post_proc(torch.from_numpy(outs))
            end = time.time()
            q = args.num / (end - start)
            qps.append(q)
        print('Onnx qps:', mean(qps))