# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

from auto_scan_test import PassAutoScanTest
from program_config import TensorConfig, ProgramConfig, OpConfig
import numpy as np
import unittest
import os

import hypothesis.strategies as st

os.environ['NVIDIA_TF32_OVERRIDE'] = '0'


class TestConvEltwiseaddBnFusePass(PassAutoScanTest):
    r"""
         x_var   f_var(persistable)
           \       /
               conv2d
               |
               conv2d_var    bias_var(persistable)
                   \          /
               elementwise_add
                       |
               elementwise_add_var Scale(persistable) Bias(persistable) Mean(persistable) Variance(persistable)
                       |
                   batch_norm
                       |
    Y  MeanOut VarianceOut  SavedMeanSavedVariance
    """

    def sample_predictor_configs(self, program_config):
        # cpu
        config = self.create_inference_config(use_gpu=False)
        yield config, ["conv2d", "elementwise_add"], (1e-4, 1e-5)

        # MKLDNN
        config = self.create_inference_config(use_gpu=False)
        config.enable_mkldnn()
        yield config, ["conv2d", "elementwise_add"], (1e-4, 1e-5)

        # for gpu
        config = self.create_inference_config(use_gpu=True)
        yield config, ["conv2d", "elementwise_add"], (1e-4, 1e-5)

    def is_program_valid(self, prog_config):
        paddings = prog_config.ops[0].attrs["paddings"]
        strides = prog_config.ops[0].attrs["strides"]
        groups = prog_config.ops[0].attrs["groups"]
        padding_algorithm = prog_config.ops[0].attrs["padding_algorithm"]
        dilations = prog_config.ops[0].attrs["dilations"]
        data_format = prog_config.ops[0].attrs["data_format"]
        filter_shape = prog_config.weights["filter"].shape
        input_shape = prog_config.inputs["input_x"].shape
        if data_format != "NCHW":
            return False
        if padding_algorithm == "VALID":
            if (
                (input_shape[2] - (dilations[0] * (filter_shape[2] - 1) + 1))
                / strides[0]
                + 1
            ) <= 1 or (
                (input_shape[3] - (dilations[1] * (filter_shape[3] - 1) + 1))
                / strides[1]
                + 1
            ) <= 1:
                return False
        if padding_algorithm == "EXPLICIT":
            if (
                (
                    input_shape[2]
                    + paddings[0]
                    + paddings[1]
                    - (dilations[0] * (filter_shape[2] - 1) + 1)
                )
                / strides[0]
                + 1
            ) <= 1 or (
                (
                    input_shape[3]
                    + paddings[2]
                    + paddings[3]
                    - (dilations[1] * (filter_shape[3] - 1) + 1)
                )
                / strides[1]
                + 1
            ) <= 1:
                return False

        if data_format == "NCHW":
            if input_shape[1] != filter_shape[1] * groups:
                return False
            if filter_shape[0] % groups != 0:
                return False
        else:
            if input_shape[3] != filter_shape[1] * groups:
                return False
            if filter_shape[0] % groups != 0:
                return False

        bn_scale = np.array(prog_config.weights["scale_in"].data)
        bn_bias = np.array(prog_config.weights["bias_in"].data)
        bn_mean = np.array(prog_config.weights["mean_in"].data)
        bn_variance = np.array(prog_config.weights["variance_in"].data)
        epsilon = np.array(prog_config.ops[-1].attrs["epsilon"])
        bn_variance = bn_variance + epsilon

        if np.isnan(bn_variance).any():
            return False
        bn_variance = np.sqrt(bn_variance)
        if np.sum(bn_variance == 0.0) > 0:
            return False
        bn_variance = bn_scale / bn_variance
        if np.isnan(bn_variance).any():
            return False
        return True

    def sample_program_config(self, draw):
        # 1. Generate shape of input:X of conv2d
        x_shape = draw(
            st.lists(
                st.integers(min_value=10, max_value=100), min_size=4, max_size=4
            )
        )
        x_shape[1] = draw(st.integers(min_value=1, max_value=10))

        # 2. Generate legal attr:data_format of conv2d
        data_format = draw(st.sampled_from(["NCHW", "NHWC"]))

        # 2. Generate legal shape of input:Y of conv2d
        f_shape = draw(
            st.lists(
                st.integers(min_value=1, max_value=7), min_size=4, max_size=4
            )
        )
        if data_format == "NCHW":
            f_shape[1] = x_shape[1]
        else:
            f_shape[1] = x_shape[3]

        # 3. Generate legal attr:strides of conv2d
        strides = draw(
            st.lists(
                st.integers(min_value=1, max_value=5), min_size=2, max_size=2
            )
        )

        # 4. Generate legal attr:padding_algorithm of conv2d
        padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VALID"]))

        # 5. Generate legal attr:padding of conv2d
        padding = draw(
            st.lists(
                st.integers(min_value=1, max_value=5), min_size=4, max_size=4
            )
        )

        # 6. Generate legal attr:groups of conv2d
        groups = draw(st.integers(min_value=1, max_value=3))

        # 7. Generate legal attr:dilations of conv2d
        dilations = draw(
            st.lists(
                st.integers(min_value=1, max_value=5), min_size=2, max_size=2
            )
        )

        # 9. Generate legal input:ResidualData of conv2d
        res_shape = []
        if draw(st.booleans()):
            res_shape = draw(
                st.lists(
                    st.integers(min_value=1, max_value=100),
                    min_size=4,
                    max_size=4,
                )
            )

        # 10. Generate legal shape of input:bias of elementwise_add
        bias_shape = [f_shape[0]]

        # 11. Generate legal attr:axis of elementwise_add
        axis = 1

        # 12. Generate legal input:Scale of batch_norm
        bn_scale_shape = [f_shape[0]]

        # 13. Generate legal input:Bias of batch_norm
        bn_bias_shape = [f_shape[0]]

        # 14. Generate legal input:Mean of batch_norm
        bn_mean_shape = [f_shape[0]]

        # 15. Generate legal input:Variance of batch_norm
        bn_variance_shape = [f_shape[0]]

        # 16. Generate legal attr:epsilon of batch_norm
        epsilon = draw(st.floats(min_value=0.00001, max_value=0.001))

        def generate_batch_variance():
            return (
                0.1 + (1.0 - 0.1) * np.random.random(bn_variance_shape)
            ).astype(np.float32)

        conv2d_op = OpConfig(
            "conv2d",
            inputs={
                "Input": ["input_x"],
                "Filter": ["filter"],
                "ResidualData": ["residualdata"],
            },
            outputs={"Output": ["conv2d_out"]},
            strides=strides,
            padding_algorithm=padding_algorithm,
            paddings=padding,
            groups=groups,
            dilations=dilations,
            data_format=data_format,
        )
        add_op = OpConfig(
            "elementwise_add",
            inputs={"X": ["conv2d_out"], "Y": ["bias"]},
            outputs={"Out": ["add_out"]},
            axis=axis,
        )

        bn_op = OpConfig(
            "batch_norm",
            inputs={
                "X": ["add_out"],
                "Scale": ["scale_in"],
                "Bias": ["bias_in"],
                "Mean": ["mean_in"],
                "Variance": ["variance_in"],
            },
            outputs={
                "Y": ["y_out"],
                "MeanOut": ["mean_in"],
                "VarianceOut": ["variance_in"],
                "SavedMean": ["SavedMean_out"],
                "SavedVariance": ["SavedVariance_out"],
                "ReserveSpace": ["ReserveSpace_out"],
            },
            epsilon=epsilon,
            is_test=True,
            trainable_statistics=False,
            data_layout=data_format,
        )

        ops = [conv2d_op, add_op, bn_op]

        # 17. if the output of bias is more than one
        if draw(st.booleans()):
            outputs = ops[-1].outputs["Y"]
        else:
            outputs = ops[-1].outputs["Y"] + ["bias"]

        program_config = ProgramConfig(
            ops=ops,
            weights={
                "filter": TensorConfig(shape=f_shape),
                "bias": TensorConfig(shape=bias_shape),
                "scale_in": TensorConfig(shape=bn_scale_shape),
                "bias_in": TensorConfig(shape=bn_bias_shape),
                "mean_in": TensorConfig(shape=bn_mean_shape),
                "variance_in": TensorConfig(data_gen=generate_batch_variance),
            },
            inputs={
                "input_x": TensorConfig(shape=x_shape),
                "residualdata": TensorConfig(shape=res_shape),
            },
            outputs=outputs,
        )
        return program_config

    def test(self):
        self.run_and_statis(
            quant=False,
            max_examples=300,
            passes=["conv_eltwiseadd_bn_fuse_pass"],
        )


if __name__ == "__main__":
    unittest.main()
