# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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 os
import sys
import unittest

import torch

import tensorrt_llm
from tensorrt_llm import Tensor

sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from utils.util import create_session, run_session


class TestInterpolate(unittest.TestCase):

    def setUp(self):
        tensorrt_llm.logger.set_level('error')

    def test_interpolate_without_scales_nearest_5d(self):
        # test data
        dtype = 'float32'
        input_shape = (1, 1, 8, 12, 16)
        output_shape = (16, 24, 32)

        input_data = torch.rand(
            input_shape,
            dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
            device="cuda")
        mode = 'nearest'
        # construct trt network
        align_corners_flag = False

        builder = tensorrt_llm.Builder()
        network = builder.create_network()
        with tensorrt_llm.net_guard(network):

            input = Tensor(name='input',
                           shape=input_shape,
                           dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            output = tensorrt_llm.functional.interpolate(
                input=input,
                size=output_shape,
                mode=mode,
                align_corners=align_corners_flag,
            )
            output.mark_output('output')

        # trt run
        session = create_session(builder, network, precision=dtype)
        inputs = {
            'input': input_data,
        }
        outputs = run_session(session, inputs)

        # pytorch run
        ref = torch.nn.functional.interpolate(input_data,
                                              size=output_shape,
                                              mode=mode)

        # compare diff
        torch.testing.assert_close(ref, outputs['output'])

    def test_interpolate_without_scales_bilinear_4d_disable_align_corner(self):
        # test data
        dtype = 'float32'
        input_shape = (1, 1, 8, 12)
        output_shape = (16, 24)

        input_data = torch.rand(
            input_shape,
            dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
            device="cuda")
        mode = 'bilinear'
        # construct trt network
        align_corners_flag = False

        builder = tensorrt_llm.Builder()
        network = builder.create_network()
        with tensorrt_llm.net_guard(network):

            input = Tensor(name='input',
                           shape=input_shape,
                           dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            output = tensorrt_llm.functional.interpolate(
                input=input,
                size=output_shape,
                mode=mode,
                align_corners=align_corners_flag,
            )
            output.mark_output('output')

        # trt run
        session = create_session(builder, network, precision=dtype)
        inputs = {
            'input': input_data,
        }
        outputs = run_session(session, inputs)

        # pytorch run
        ref = torch.nn.functional.interpolate(input_data,
                                              size=output_shape,
                                              mode=mode)

        # compare diff
        torch.testing.assert_close(ref, outputs['output'])

    def test_interpolate_without_scales_bilinear_4d_enable_align_corner(self):
        # test data
        dtype = 'float32'
        input_shape = (1, 1, 8, 12)
        output_shape = (16, 24)

        input_data = torch.rand(
            input_shape,
            dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
            device="cuda")
        mode = 'bilinear'
        # construct trt network
        align_corners_flag = True

        builder = tensorrt_llm.Builder()
        network = builder.create_network()
        with tensorrt_llm.net_guard(network):

            input = Tensor(name='input',
                           shape=input_shape,
                           dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            output = tensorrt_llm.functional.interpolate(
                input=input,
                size=output_shape,
                mode=mode,
                align_corners=align_corners_flag,
            )
            output.mark_output('output')

        # trt run
        session = create_session(builder, network, precision=dtype)
        inputs = {
            'input': input_data,
        }

        outputs = run_session(session, inputs)

        # pytorch run
        ref = torch.nn.functional.interpolate(input_data,
                                              size=output_shape,
                                              mode=mode)
        # compare diff
        torch.testing.assert_close(ref, outputs['output'])

    def test_interpolate_without_scales_bicubic_4d_enable_align_corner(self):
        # test data
        dtype = 'float32'
        input_shape = (1, 4, 8, 12)
        output_shape = (16, 24)

        input_data = torch.rand(
            input_shape,
            dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
            device="cuda")
        mode = 'bicubic'
        # construct trt network
        align_corners_flag = True

        builder = tensorrt_llm.Builder()
        network = builder.create_network()
        with tensorrt_llm.net_guard(network):

            input = Tensor(name='input',
                           shape=input_shape,
                           dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            output = tensorrt_llm.functional.interpolate(
                input=input,
                size=output_shape,
                mode=mode,
                align_corners=align_corners_flag,
            )
            output.mark_output('output')

        # trt run
        session = create_session(builder, network, precision=dtype)
        inputs = {
            'input': input_data,
        }

        outputs = run_session(session, inputs)

        # pytorch run
        ref = torch.nn.functional.interpolate(input_data,
                                              size=output_shape,
                                              mode=mode)
        # compare diff
        torch.testing.assert_close(ref, outputs['output'])

    def test_interpolate_with_scale_3d_nearest_exact(self):
        # test data
        dtype = 'float32'
        input_shape = (1, 4, 8, 16)
        scales_factor = (2, 4)
        input_data = torch.rand(
            input_shape,
            dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
            device="cuda")
        mode = 'nearest-exact'
        # construct trt network
        align_corners_flag = False

        builder = tensorrt_llm.Builder()
        network = builder.create_network()
        with tensorrt_llm.net_guard(network):

            input = Tensor(name='input',
                           shape=input_shape,
                           dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            output = tensorrt_llm.functional.interpolate(
                input=input,
                scale_factor=scales_factor,
                mode=mode,
                align_corners=align_corners_flag,
            )
            output.mark_output('output')

        # trt run
        session = create_session(builder, network, precision=dtype)
        inputs = {
            'input': input_data,
        }

        outputs = run_session(session, inputs)

        # pytorch run
        ref = torch.nn.functional.interpolate(input_data,
                                              scale_factor=scales_factor,
                                              mode=mode)
        # compare diff
        torch.testing.assert_close(ref, outputs['output'])

    def test_interpolate_with_scale_4d_bicubic(self):
        # test data
        dtype = 'float32'
        input_shape = (1, 4, 8, 12)
        scales_factor = (2.5, 2)
        input_data = torch.rand(
            input_shape,
            dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
            device="cuda")
        mode = 'bicubic'
        # construct trt network
        align_corners_flag = False

        builder = tensorrt_llm.Builder()
        network = builder.create_network()
        with tensorrt_llm.net_guard(network):

            input = Tensor(name='input',
                           shape=input_shape,
                           dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            output = tensorrt_llm.functional.interpolate(
                input=input,
                scale_factor=scales_factor,
                mode=mode,
                align_corners=align_corners_flag,
            )
            output.mark_output('output')

        # trt run
        session = create_session(builder, network, precision=dtype)
        inputs = {
            'input': input_data,
        }

        outputs = run_session(session, inputs)

        # pytorch run
        ref = torch.nn.functional.interpolate(input_data,
                                              scale_factor=scales_factor,
                                              mode=mode)

        # compare diff
        torch.testing.assert_close(ref, outputs['output'])

    def test_interpolate_with_scale_4d_bilinear(self):
        # test data
        dtype = 'float32'
        input_shape = (1, 1, 8, 32)
        scales_factor = (2.5, 4)
        input_data = torch.rand(
            input_shape,
            dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
            device="cuda")
        mode = 'bilinear'
        # construct trt network
        align_corners_flag = False

        builder = tensorrt_llm.Builder()
        network = builder.create_network()
        with tensorrt_llm.net_guard(network):

            input = Tensor(name='input',
                           shape=input_shape,
                           dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            output = tensorrt_llm.functional.interpolate(
                input=input,
                scale_factor=scales_factor,
                mode=mode,
                align_corners=align_corners_flag,
            )
            output.mark_output('output')

        # trt run
        session = create_session(builder, network, precision=dtype)
        inputs = {
            'input': input_data,
        }

        outputs = run_session(session, inputs)

        # pytorch run
        ref = torch.nn.functional.interpolate(input_data,
                                              scale_factor=scales_factor,
                                              align_corners=align_corners_flag,
                                              mode=mode)

        # compare diff
        torch.testing.assert_close(ref, outputs['output'])

    def test_interpolate_with_scale_5d_trilinear_enable_align_corner(self):
        # test data
        dtype = 'float32'
        input_shape = (1, 1, 8, 16, 32)
        scales_factor = (2.5, 2, 4)
        input_data = torch.rand(
            input_shape,
            dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
            device="cuda")
        mode = 'trilinear'
        # construct trt network
        align_corners_flag = True

        builder = tensorrt_llm.Builder()
        network = builder.create_network()
        with tensorrt_llm.net_guard(network):

            input = Tensor(name='input',
                           shape=input_shape,
                           dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            output = tensorrt_llm.functional.interpolate(
                input=input,
                scale_factor=scales_factor,
                mode=mode,
                align_corners=align_corners_flag,
            )
            output.mark_output('output')

        # trt run
        session = create_session(builder, network, precision=dtype)
        inputs = {
            'input': input_data,
        }
        outputs = run_session(session, inputs)

        # pytorch run
        ref = torch.nn.functional.interpolate(input_data,
                                              scale_factor=scales_factor,
                                              align_corners=align_corners_flag,
                                              mode=mode)

        # compare diff
        torch.testing.assert_close(ref, outputs['output'])