#
# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import numpy as np
import pytest
import torch

from polygraphy.backend.onnxrt import OnnxrtRunner, SessionFromOnnx
from polygraphy.exception import PolygraphyException
from polygraphy.logger import G_LOGGER
from tests.models.meta import ONNX_MODELS


class TestLoggerCallbacks:
    @pytest.mark.parametrize("sev", G_LOGGER.SEVERITY_LETTER_MAPPING.keys())
    def test_set_severity(self, sev):
        G_LOGGER.module_severity = sev


class TestOnnxrtRunner:
    def test_can_name_runner(self):
        NAME = "runner"
        runner = OnnxrtRunner(None, name=NAME)
        assert runner.name == NAME

    def test_basic(self):
        model = ONNX_MODELS["identity"]
        with OnnxrtRunner(SessionFromOnnx(model.loader)) as runner:
            assert runner.is_active
            model.check_runner(runner)
            assert runner.last_inference_time() is not None
        assert not runner.is_active

    def test_torch_tensors(self):
        model = ONNX_MODELS["identity"]
        with OnnxrtRunner(SessionFromOnnx(model.loader)) as runner:
            arr = torch.ones((1, 1, 2, 2), dtype=torch.float32)
            outputs = runner.infer({"x": arr})

            assert isinstance(outputs["y"], torch.Tensor)
            assert torch.equal(outputs["y"], arr)

    @pytest.mark.serial
    def test_warn_if_impl_methods_called(self, check_warnings_on_runner_impl_methods):
        model = ONNX_MODELS["identity"]
        runner = OnnxrtRunner(SessionFromOnnx(model.loader))
        check_warnings_on_runner_impl_methods(runner)

    def test_shape_output(self):
        model = ONNX_MODELS["reshape"]
        with OnnxrtRunner(SessionFromOnnx(model.loader)) as runner:
            model.check_runner(runner)

    def test_dim_param_preserved(self):
        model = ONNX_MODELS["dim_param"]
        with OnnxrtRunner(SessionFromOnnx(model.loader)) as runner:
            input_meta = runner.get_input_metadata(use_numpy_dtypes=False)
            # In Polygraphy, we only use None to indicate a dynamic input dimension - not strings.
            assert len(input_meta) == 1
            for _, (_, shape) in input_meta.items():
                assert shape == ["dim0", 16, 128]

    @pytest.mark.parametrize(
        "names, err",
        [
            (["fake-input", "x"], "Extra inputs in"),
            (["fake-input"], "The following inputs were not found"),
            ([], "The following inputs were not found"),
        ],
    )
    def test_error_on_wrong_name_feed_dict(self, names, err):
        model = ONNX_MODELS["identity"]
        with OnnxrtRunner(SessionFromOnnx(model.loader)) as runner:
            with pytest.raises(PolygraphyException, match=err):
                runner.infer(
                    {
                        name: np.ones(shape=(1, 1, 2, 2), dtype=np.float32)
                        for name in names
                    }
                )

    def test_error_on_wrong_dtype_feed_dict(self):
        model = ONNX_MODELS["identity"]
        with OnnxrtRunner(SessionFromOnnx(model.loader)) as runner:
            with pytest.raises(PolygraphyException, match="unexpected dtype."):
                runner.infer({"x": np.ones(shape=(1, 1, 2, 2), dtype=np.int32)})

    def test_error_on_wrong_shape_feed_dict(self):
        model = ONNX_MODELS["identity"]
        with OnnxrtRunner(SessionFromOnnx(model.loader)) as runner:
            with pytest.raises(PolygraphyException, match="incompatible shape."):
                runner.infer({"x": np.ones(shape=(1, 1, 3, 2), dtype=np.float32)})