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import asyncio
import copy
import json
import os
import queue
import shutil
import time
import unittest

import numpy
import pytest
import tritonserver

try:
    import cupy
except ImportError:
    cupy = None

try:
    import torch

    if not torch.cuda.is_available():
        torch = None
except ImportError:
    torch = None

module_directory = os.path.split(os.path.abspath(__file__))[0]
test_model_directory = os.path.abspath(
    os.path.join(module_directory, "test_api_models")
)
test_logs_directory = os.path.abspath(os.path.join(module_directory, "test_api_logs"))

shutil.rmtree(test_logs_directory, ignore_errors=True)

os.makedirs(test_logs_directory)

server_options = tritonserver.Options(
    server_id="TestServer",
    model_repository=test_model_directory,
    log_verbose=6,
    log_error=True,
    log_warn=True,
    log_info=True,
    exit_on_error=True,
    strict_model_config=False,
    model_control_mode=tritonserver.ModelControlMode.EXPLICIT,
    exit_timeout=30,
)


class ModelTests(unittest.TestCase):
    def setup_method(self, method):
        self._server_options = copy.copy(server_options)
        self._server_options.log_file = os.path.join(
            test_logs_directory, method.__name__ + ".server.log"
        )

    def test_create_request(self):
        server = tritonserver.Server(self._server_options).start(wait_until_ready=True)

        request = server.models()["test"].create_request()

        request = tritonserver.InferenceRequest(server.model("test"))


class AllocatorTests(unittest.TestCase):
    class MockMemoryAllocator(tritonserver.MemoryAllocator):
        def __init__(self):
            pass

        def allocate(self, *args, **kwargs):
            raise Exception("foo")

    def setup_method(self, method):
        self._server_options = copy.copy(server_options)
        self._server_options.log_file = os.path.join(
            test_logs_directory, method.__name__ + ".server.log"
        )

    @pytest.mark.skipif(cupy is None, reason="Skipping gpu memory, cupy not installed")
    def test_memory_fallback_to_cpu(self):
        server = tritonserver.Server(self._server_options).start(wait_until_ready=True)

        self.assertTrue(server.ready())

        allocator = tritonserver.default_memory_allocators[tritonserver.MemoryType.GPU]

        del tritonserver.default_memory_allocators[tritonserver.MemoryType.GPU]

        server.load(
            "test",
            {
                "config": json.dumps(
                    {
                        "backend": "python",
                        "parameters": {
                            "decoupled": {"string_value": "False"},
                            "request_gpu_memory": {"string_value": "True"},
                        },
                    }
                )
            },
        )

        fp16_input = numpy.random.rand(1, 100).astype(dtype=numpy.float16)

        for response in server.model("test").infer(
            inputs={"fp16_input": fp16_input},
        ):
            self.assertEqual(
                response.outputs["fp16_output"].memory_type, tritonserver.MemoryType.CPU
            )
            fp16_output = numpy.from_dlpack(response.outputs["fp16_output"])
            self.assertEqual(fp16_input[0][0], fp16_output[0][0])

        tritonserver.default_memory_allocators[tritonserver.MemoryType.GPU] = allocator

    def test_memory_allocator_exception(self):
        server = tritonserver.Server(self._server_options).start(wait_until_ready=True)

        self.assertTrue(server.ready())

        server.load(
            "test",
            {
                "config": json.dumps(
                    {
                        "backend": "python",
                        "parameters": {"decoupled": {"string_value": "False"}},
                    }
                )
            },
        )

        with self.assertRaises(tritonserver.InternalError):
            for response in server.model("test").infer(
                inputs={
                    "string_input": tritonserver.Tensor.from_string_array([["hello"]])
                },
                output_memory_type="gpu",
                output_memory_allocator=AllocatorTests.MockMemoryAllocator(),
            ):
                pass

    def test_unsupported_memory_type(self):
        server = tritonserver.Server(self._server_options).start(wait_until_ready=True)

        self.assertTrue(server.ready())

        server.load(
            "test",
            {
                "config": json.dumps(
                    {
                        "backend": "python",
                        "parameters": {"decoupled": {"string_value": "False"}},
                    }
                )
            },
        )

        if tritonserver.MemoryType.GPU in tritonserver.default_memory_allocators:
            allocator = tritonserver.default_memory_allocators[
                tritonserver.MemoryType.GPU
            ]

            del tritonserver.default_memory_allocators[tritonserver.MemoryType.GPU]
        else:
            allocator = None

        with self.assertRaises(tritonserver.InvalidArgumentError):
            for response in server.model("test").infer(
                inputs={
                    "string_input": tritonserver.Tensor.from_string_array([["hello"]])
                },
                output_memory_type="gpu",
            ):
                pass

        if allocator is not None:
            tritonserver.default_memory_allocators[
                tritonserver.MemoryType.GPU
            ] = allocator

    @pytest.mark.skipif(torch is None, reason="Skipping test, torch not installed")
    def test_allocate_on_cpu_and_reshape(self):
        allocator = tritonserver.default_memory_allocators[tritonserver.MemoryType.CPU]

        memory_buffer = allocator.allocate(
            memory_type=tritonserver.MemoryType.CPU, memory_type_id=0, size=200
        )

        cpu_array = memory_buffer.owner

        self.assertEqual(memory_buffer.size, 200)

        fp32_size = int(memory_buffer.size / 4)

        tensor = tritonserver.Tensor(
            tritonserver.DataType.FP32, shape=[fp32_size], memory_buffer=memory_buffer
        )

        cpu_fp32_array = numpy.from_dlpack(tensor)
        self.assertEqual(cpu_array.ctypes.data, cpu_fp32_array.ctypes.data)
        self.assertEqual(cpu_fp32_array.dtype, numpy.float32)
        self.assertEqual(cpu_fp32_array.nbytes, 200)

    @pytest.mark.skipif(cupy is None, reason="Skipping gpu memory, cupy not installed")
    @pytest.mark.skipif(torch is None, reason="Skipping test, torch not installed")
    def test_allocate_on_gpu_and_reshape(self):
        if cupy is None:
            return

        allocator = tritonserver.default_memory_allocators[tritonserver.MemoryType.GPU]

        memory_buffer = allocator.allocate(
            memory_type=tritonserver.MemoryType.GPU, memory_type_id=0, size=200
        )

        gpu_array = memory_buffer.owner

        gpu_array = cupy.empty([10, 20], dtype=cupy.uint8)
        memory_buffer = tritonserver.MemoryBuffer.from_dlpack(gpu_array)

        self.assertEqual(memory_buffer.size, 200)

        fp32_size = int(memory_buffer.size / 4)

        tensor = tritonserver.Tensor(
            tritonserver.DataType.FP32, shape=[fp32_size], memory_buffer=memory_buffer
        )

        gpu_fp32_array = cupy.from_dlpack(tensor)
        self.assertEqual(
            gpu_array.__cuda_array_interface__["data"][0],
            gpu_fp32_array.__cuda_array_interface__["data"][0],
        )
        self.assertEqual(gpu_fp32_array.dtype, cupy.float32)
        self.assertEqual(gpu_fp32_array.nbytes, 200)

        torch_fp32_tensor = torch.from_dlpack(tensor)
        self.assertEqual(torch_fp32_tensor.dtype, torch.float32)
        self.assertEqual(
            torch_fp32_tensor.data_ptr(), gpu_array.__cuda_array_interface__["data"][0]
        )
        self.assertEqual(torch_fp32_tensor.nbytes, 200)


class TensorTests(unittest.TestCase):
    @pytest.mark.skipif(cupy is None, reason="Skipping gpu memory, cupy not installed")
    def test_cpu_to_gpu(self):
        if cupy is None:
            return
        cpu_array = numpy.random.rand(1, 3, 100, 100).astype(numpy.float32)
        cpu_tensor = tritonserver.Tensor.from_dlpack(cpu_array)
        gpu_tensor = cpu_tensor.to_device("gpu:0")
        gpu_array = cupy.from_dlpack(gpu_tensor)

        self.assertEqual(gpu_array.device, cupy.cuda.Device(0))

        numpy.testing.assert_array_equal(cpu_array, gpu_array.get())

        memory_buffer = tritonserver.MemoryBuffer.from_dlpack(gpu_array)

        self.assertEqual(
            gpu_array.__cuda_array_interface__["data"][0], memory_buffer.data_ptr
        )

    @pytest.mark.skipif(
        torch is None, reason="Skipping gpu memory, torch not installed"
    )
    @pytest.mark.skipif(cupy is None, reason="Skipping gpu memory, cupy not installed")
    def test_gpu_tensor_from_dl_pack(self):
        if cupy is None or torch is None:
            return
        cupy_array = cupy.ones([100]).astype(cupy.float64)
        tensor = tritonserver.Tensor.from_dlpack(cupy_array)
        torch_tensor = torch.from_dlpack(cupy_array)

        self.assertEqual(torch_tensor.data_ptr(), tensor.data_ptr)
        self.assertEqual(torch_tensor.nbytes, tensor.size)
        self.assertEqual(torch_tensor.__dlpack_device__(), tensor.__dlpack_device__())

    @pytest.mark.skipif(torch is None, reason="Skipping test, torch not installed")
    def test_tensor_from_numpy(self):
        cpu_array = numpy.random.rand(1, 3, 100, 100).astype(numpy.float32)
        tensor = tritonserver.Tensor.from_dlpack(cpu_array)
        torch_tensor = torch.from_dlpack(tensor)
        numpy.testing.assert_array_equal(torch_tensor.numpy(), cpu_array)
        self.assertEqual(torch_tensor.data_ptr(), cpu_array.ctypes.data)


class ServerTests(unittest.TestCase):
    def setup_method(self, method):
        self._server_options = copy.copy(server_options)
        self._server_options.log_file = os.path.join(
            test_logs_directory, method.__name__ + ".server.log"
        )

    def test_not_started(self):
        server = tritonserver.Server()
        with self.assertRaises(tritonserver.InvalidArgumentError):
            server.ready()

    def test_invalid_option_type(self):
        server = tritonserver.Server(server_id=1)
        with self.assertRaises(TypeError):
            server.start()

        server = tritonserver.Server(model_repository=1)
        with self.assertRaises(TypeError):
            server.start()

    def test_invalid_repo(self):
        with self.assertRaises(tritonserver.InternalError):
            tritonserver.Server(model_repository="foo").start()

    def test_ready(self):
        server = tritonserver.Server(self._server_options).start()
        self.assertTrue(server.ready())

    def test_stop(self):
        server = tritonserver.Server(self._server_options).start(wait_until_ready=True)

        self.assertTrue(server.ready())

        server.load(
            "test",
            {
                "config": json.dumps(
                    {
                        "backend": "python",
                        "parameters": {"decoupled": {"string_value": "False"}},
                        # Keep instance count low for fast startup/cleanup.
                        # Alternatively can use KIND_CPU here, but keeping gpus/count explicit.
                        "instance_group": [
                            {"kind": "KIND_GPU", "gpus": [0], "count": 1}
                        ],
                    }
                )
            },
        )

        fp16_input = numpy.random.rand(1, 100).astype(dtype=numpy.float16)

        for response in server.model("test").infer(
            inputs={"fp16_input": fp16_input},
            output_memory_type="cpu",
            raise_on_error=True,
        ):
            fp16_output = numpy.from_dlpack(response.outputs["fp16_output"])
            numpy.testing.assert_array_equal(fp16_input, fp16_output)

        server.stop()

    def test_model_repository_not_specified(self):
        with self.assertRaises(tritonserver.InvalidArgumentError):
            tritonserver.Server(model_repository=None).start()


class InferenceTests(unittest.TestCase):
    def setup_method(self, method):
        self._server_options = copy.copy(server_options)
        self._server_options.log_file = os.path.join(
            test_logs_directory, method.__name__ + ".server.log"
        )

    @pytest.mark.skipif(cupy is None, reason="Skipping gpu memory, cupy not installed")
    def test_gpu_output(self):
        server = tritonserver.Server(self._server_options).start(wait_until_ready=True)

        self.assertTrue(server.ready())

        server.load(
            "test",
            {
                "config": json.dumps(
                    {
                        "backend": "python",
                        "parameters": {"decoupled": {"string_value": "False"}},
                    }
                )
            },
        )

        fp16_input = numpy.random.rand(1, 100).astype(dtype=numpy.float16)

        for response in server.model("test").infer(
            inputs={"fp16_input": fp16_input},
            output_memory_type="gpu",
        ):
            fp16_output = cupy.from_dlpack(response.outputs["fp16_output"])
            self.assertEqual(fp16_input[0][0], fp16_output[0][0])

        for response in server.model("test").infer(
            inputs={"string_input": [["hello"]]},
            output_memory_type="gpu",
        ):
            text_output = response.outputs["string_output"].to_string_array()
            self.assertEqual(text_output[0][0], "hello")

        for response in server.model("test").infer(
            inputs={"string_input": tritonserver.Tensor.from_string_array([["hello"]])},
            output_memory_type="gpu",
        ):
            text_output = response.outputs["string_output"].to_string_array()
            text_output = response.outputs["string_output"].to_string_array()
            self.assertEqual(text_output[0][0], "hello")

    def test_basic_inference(self):
        server = tritonserver.Server(self._server_options).start(wait_until_ready=True)

        self.assertTrue(server.ready())

        server.load(
            "test",
            {
                "config": json.dumps(
                    {
                        "backend": "python",
                        "parameters": {"decoupled": {"string_value": "False"}},
                    }
                )
            },
        )

        inputs = {
            "fp16_input": numpy.random.rand(1, 100).astype(dtype=numpy.float16),
            "bool_input": numpy.random.rand(1, 100).astype(dtype=numpy.bool_),
        }

        for response in server.model("test").infer(
            inputs=inputs,
            output_memory_type="cpu",
            raise_on_error=True,
        ):
            for input_name, input_value in inputs.items():
                output_value = response.outputs[input_name.replace("input", "output")]
                output_value = numpy.from_dlpack(output_value)
                numpy.testing.assert_array_equal(input_value, output_value)

        # test normal bool
        inputs = {"bool_input": [[True, False, False, True]]}

        for response in server.model("test").infer(
            inputs=inputs,
            output_memory_type="cpu",
            raise_on_error=True,
        ):
            for input_name, input_value in inputs.items():
                output_value = numpy.from_dlpack(
                    response.outputs[input_name.replace("input", "output")]
                )
                numpy.testing.assert_array_equal(input_value, output_value)

    def test_parameters(self):
        server = tritonserver.Server(self._server_options).start(wait_until_ready=True)

        self.assertTrue(server.ready())

        server.load(
            "test",
            {
                "config": json.dumps(
                    {
                        "backend": "python",
                        "parameters": {"decoupled": {"string_value": "False"}},
                    }
                )
            },
        )

        fp16_input = numpy.random.rand(1, 100).astype(dtype=numpy.float16)

        input_parameters = {
            "int_parameter": 0,
            "float_parameter": 0.5,
            "bool_parameter": False,
            "string_parameter": "test",
        }
        for response in server.model("test").infer(
            inputs={"fp16_input": fp16_input},
            parameters=input_parameters,
            output_memory_type="cpu",
            raise_on_error=True,
        ):
            fp16_output = numpy.from_dlpack(response.outputs["fp16_output"])
            numpy.testing.assert_array_equal(fp16_input, fp16_output)
            output_parameters = json.loads(
                response.outputs["output_parameters"].to_string_array()[0]
            )
            assert input_parameters == output_parameters

        with self.assertRaises(tritonserver.InvalidArgumentError):
            input_parameters = {
                "invalid": {"test": 1},
            }

            server.model("test").infer(
                inputs={"fp16_input": fp16_input},
                parameters=input_parameters,
                output_memory_type="cpu",
                raise_on_error=True,
            )

        with self.assertRaises(tritonserver.InvalidArgumentError):
            input_parameters = {
                "invalid": None,
            }

            server.model("test").infer(
                inputs={"fp16_input": fp16_input},
                parameters=input_parameters,
                output_memory_type="cpu",
                raise_on_error=True,
            )
