import towhee
from towhee.dc2 import AutoPipes, AutoConfig
from towhee import triton_client, ops

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=int, default=-1)

args = parser.parse_args()

device = 'cuda:' + str(args.device) if args.device >= 0 else 'cpu'
model_name = args.model
# model_name = 'paraphrase-albert-small-v2'
# model_name = 'all-MiniLM-L6-v2'
# model_name = 'all-mpnet-base-v2'
# model_name = 'distilbert-base-uncased'

# p = (
#     pipe.input('text')
#         .map('text', 'vec', ops.sentence_embedding.transformers(model_name=model_name, device='cuda:3'))
#         .output('vec')
# )

conf = AutoConfig.load_config('sentence_embedding')
conf.model = model_name
conf.device = args.device
p = AutoPipes.pipeline('sentence_embedding', conf)

text = 'Hello, world.'
out1 = p(text).get()[0]
print('Pipe: OK')

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

if args.onnx:
    op = ops.sentence_embedding.transformers(model_name=model_name, device='cpu').get_op()
    # if not os.path.exists('test.onnx'):
    op.save_model('onnx', 'test.onnx')
    sess = onnxruntime.InferenceSession('test.onnx',
                                        providers=['CUDAExecutionProvider'])
    inputs = op.tokenizer([text], padding=True, truncation=True, return_tensors='np')
    # inputs = {}
    # for k, v in tokens.items():
    #     if k in op.onnx_config['inputs'].keys():
    #         inputs[k] = v
    out3 = sess.run(None, input_feed=dict(inputs))
    torch_inputs = {}
    for k, v in inputs.items():
        torch_inputs[k] = torch.from_numpy(v).to(device)
    out3 = op.post_proc(torch.from_numpy(out3[0]), torch_inputs).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):
                tokens = op.tokenizer([text], padding=True, truncation=True, return_tensors='pt')
                outs = sess.run(None, input_feed=dict(inputs))
                op.post_proc(torch.from_numpy(outs[0]).to(device), torch_inputs)
            end = time.time()
            q = args.num / (end - start)
            qps.append(q)
        print('Onnx qps:', mean(qps))