# 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 unittest

import hypothesis.strategies as st


class TestTransposeFlattenConcatFusePass(PassAutoScanTest):
    """
        x_1_var              x_2_var
          |                     |
      transpose2            transpose2
          |                     |
       flatten2              flatten2
          \                     /
    flatten2_out_var    flatten2_out_var
              \              /
                   concat
    """

    def sample_predictor_configs(self, program_config):
        # TRT
        # after tensorrt_subgraph_pass ，The pass needs to be deleted on TRT

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

    def is_program_valid(self, prog_config):
        concat_axis = prog_config.ops[-1].attrs["axis"]
        ops_num = len(prog_config.ops) - 1
        if ops_num % 2 != 0:
            return False
        input_num = ops_num // 2
        flatten_shape = 0
        x_trans_axis = prog_config.ops[0].attrs["axis"]
        x_flatten_axis = prog_config.ops[1].attrs["axis"]
        for i in range(input_num):
            input_name = "transpose2_x" + str(i)
            input_shape = prog_config.inputs[input_name].shape
            trans_axis = prog_config.ops[i * 2].attrs["axis"]
            if x_trans_axis != trans_axis:
                return False
            #  calculate shape after transpose
            input_shape = [input_shape[j] for j in trans_axis]
            #  calculate shape after flateen
            flatten_axis = prog_config.ops[i * 2 + 1].attrs["axis"]
            if x_flatten_axis != flatten_axis:
                return False
            flatten_shape1 = flatten_shape2 = 1
            for j in range(len(input_shape)):
                if j < flatten_axis:
                    flatten_shape1 *= input_shape[j]
                else:
                    flatten_shape2 *= input_shape[j]
            if concat_axis == 0:
                if i == 0:
                    flatten_shape = flatten_shape2
                elif flatten_shape != flatten_shape2:
                    return False
            else:
                if i == 0:
                    flatten_shape = flatten_shape1
                elif flatten_shape != flatten_shape1:
                    return False
        return True

    def sample_program_config(self, draw):
        times = draw(st.integers(min_value=1, max_value=6))
        concat_axis = draw(st.integers(min_value=0, max_value=1))
        ops = []
        concat_input = []
        inputs = {}
        x_shape_rank = draw(st.integers(min_value=2, max_value=5))
        #  Generate axis of transpose
        trans_axis = [j for j in range(x_shape_rank)]
        for j in range(x_shape_rank - 1):
            if draw(st.booleans()):
                trans_axis[j], trans_axis[-1] = trans_axis[-1], trans_axis[j]
        #  Generate axis of flatten
        flatten_axis = draw(st.integers(min_value=0,
                                        max_value=x_shape_rank - 1))
        for i in range(times):
            #  Generate x_shape of transpose
            x_shape = draw(
                st.lists(st.integers(min_value=1, max_value=10),
                         min_size=x_shape_rank,
                         max_size=x_shape_rank))

            str_i = str(i)
            transpose_op = OpConfig(
                "transpose2",
                inputs={
                    "X": ["transpose2_x" + str_i],
                },
                axis=trans_axis,
                outputs={
                    "Out": ["trans_out" + str_i],
                    "XShape": ["trans_shape" + str_i]
                },
            )
            ops.append(transpose_op)
            flatten_op = OpConfig(
                "flatten2",
                inputs={
                    "X": ["trans_out" + str_i],
                },
                axis=flatten_axis,
                outputs={
                    "Out": ["flatten2_out" + str_i],
                    "XShape": ["xshape" + str_i]
                },
            )
            concat_input.append("flatten2_out" + str_i)
            ops.append(flatten_op)
            inputs["transpose2_x" + str_i] = TensorConfig(shape=x_shape)

        concat_op = OpConfig(
            "concat",
            inputs={
                "X": concat_input,
                "AxisTensor": [],
            },
            outputs={"Out": ["concat_out"]},
            axis=concat_axis,
        )

        ops.append(concat_op)

        program_config = ProgramConfig(
            ops=ops,
            weights={},
            inputs=inputs,
            outputs=ops[-1].outputs["Out"],
        )
        return program_config

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


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