#!/usr/bin/env python
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import argparse
import numpy as np

import tritonclient.http as httpclient
from tritonclient.utils import InferenceServerException

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('-u',
                        '--url',
                        type=str,
                        required=False,
                        default='localhost:8000',
                        help='Inference server URL. Default is localhost:8000.')
    FLAGS = parser.parse_args()

    # For the HTTP client, need to specify large enough concurrency to
    # issue all the inference requests to the server in parallel. For
    # this example we want to be able to send 2 requests concurrently.
    try:
        concurrent_request_count = 2
        triton_client = httpclient.InferenceServerClient(
            url=FLAGS.url, concurrency=concurrent_request_count)
    except Exception as e:
        print("channel creation failed: " + str(e))
        sys.exit(1)

    # Send 2 requests to the batching model. Because these are sent
    # asynchronously and Triton's dynamic batcher is configured to
    # delay up to 5 seconds when forming a batch for this model, we
    # expect these 2 requests to be batched within Triton and sent to
    # the backend as a single batch.
    #
    # The recommended backend can handle any model with 1 input and 1
    # output as long as the input and output datatype and shape are
    # the same. The batching model uses datatype FP32 and shape
    # [ 4, 4 ].
    print('\n=========')
    async_requests = []

    input0_data = np.array([[[ 1.0, 1.1, 1.2, 1.3 ],
                             [ 2.0, 2.1, 2.2, 2.3 ],
                             [ 3.0, 3.1, 3.2, 3.3 ],
                             [ 4.0, 4.1, 4.2, 4.3 ]]], dtype=np.float32)
    print('Sending request to batching model: input = {}'.format(input0_data))
    inputs = [ httpclient.InferInput('INPUT', [1, 4, 4], "FP32") ]
    inputs[0].set_data_from_numpy(input0_data)
    async_requests.append(triton_client.async_infer('batching', inputs))

    input0_data = np.array([[[ 10.0, 10.1, 10.2, 10.3 ],
                             [ 20.0, 20.1, 20.2, 20.3 ],
                             [ 30.0, 30.1, 30.2, 30.3 ],
                             [ 40.0, 40.1, 40.2, 40.3 ]]], dtype=np.float32)
    print('Sending request to batching model: input = {}'.format(input0_data))
    inputs = [ httpclient.InferInput('INPUT', [1, 4, 4], "FP32") ]
    inputs[0].set_data_from_numpy(input0_data)
    async_requests.append(triton_client.async_infer('batching', inputs))

    for async_request in async_requests:
        # Get the result from the initiated asynchronous inference
        # request. This call will block till the server responds.
        result = async_request.get_result()
        print('Response: {}'.format(result.get_response()))
        print('OUTPUT = {}'.format(result.as_numpy('OUTPUT')))
