# 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 paddle.inference as paddle_infer
import unittest

import hypothesis.strategies as st


class TestSimplifyWithBasicOpsPassUpscale(PassAutoScanTest):

    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_config(self, draw):
        #scale = draw(st.floats(min_value=0.01, max_value=1.0))
        #bias = draw(st.floats(min_value=0.01, max_value=2.0))
        #bias_after_scale = draw(st.booleans())
        fix_seed = draw(st.booleans())
        dropout_implementation = "upscale_in_train"
        dropout_prob = draw(st.floats(min_value=0.0, max_value=1.0))
        seed = draw(st.integers(min_value=0, max_value=512))
        x_shape = draw(
            st.lists(st.integers(min_value=1, max_value=4),
                     min_size=2,
                     max_size=4))
        is_test = True

        dropout_op = OpConfig("dropout",
                              inputs={"X": ["input_data"]},
                              outputs={
                                  "Out": ["dropout_output"],
                                  "Mask": ["mask"]
                              },
                              fix_seed=fix_seed,
                              dropout_implementation=dropout_implementation,
                              dropout_prob=dropout_prob,
                              seed=seed,
                              is_test=is_test)
        relu_op = OpConfig("relu",
                           inputs={"X": ["dropout_output"]},
                           outputs={"Out": ["relu_out"]})
        ops = [dropout_op, relu_op]

        program_config = ProgramConfig(ops=ops,
                                       weights={},
                                       inputs={
                                           "input_data":
                                           TensorConfig(shape=x_shape),
                                       },
                                       outputs=["relu_out"])

        return program_config

    def sample_predictor_configs(self, program_config):
        config = self.create_inference_config(use_gpu=True)
        yield config, ['relu'], (1e-5, 1e-5)
        config = self.create_inference_config(use_gpu=False)
        yield config, ['relu'], (1e-5, 1e-5)
        config = self.create_trt_inference_config()
        config.enable_tensorrt_engine(
            max_batch_size=4,
            workspace_size=102400,
            min_subgraph_size=0,
            precision_mode=paddle_infer.PrecisionType.Float32,
            use_static=False,
            use_calib_mode=False)
        yield config, ['relu'], (1e-5, 1e-5)

    def test(self):
        self.run_and_statis(quant=False,
                            max_examples=30,
                            passes=["simplify_with_basic_ops_pass"],
                            min_success_num=30)


class TestSimplifyWithBasicOpsPassDowngrade(PassAutoScanTest):

    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_config(self, draw):
        fix_seed = draw(st.booleans())
        dropout_implementation = "downgrade_in_infer"
        dropout_prob = draw(st.floats(min_value=0.0, max_value=1.0))
        seed = draw(st.integers(min_value=0, max_value=512))
        x_shape = draw(
            st.lists(st.integers(min_value=1, max_value=4),
                     min_size=2,
                     max_size=4))
        is_test = True

        dropout_op = OpConfig("dropout",
                              inputs={"X": ["input_data"]},
                              outputs={
                                  "Out": ["dropout_output"],
                                  "Mask": ["mask"]
                              },
                              fix_seed=fix_seed,
                              dropout_implementation=dropout_implementation,
                              dropout_prob=dropout_prob,
                              seed=seed,
                              is_test=is_test)
        relu_op = OpConfig("relu",
                           inputs={"X": ["dropout_output"]},
                           outputs={"Out": ["relu_out"]})
        ops = [dropout_op, relu_op]

        program_config = ProgramConfig(ops=ops,
                                       weights={},
                                       inputs={
                                           "input_data":
                                           TensorConfig(shape=x_shape),
                                       },
                                       outputs=["relu_out"])

        return program_config

    def sample_predictor_configs(self, program_config):
        config = self.create_inference_config(use_gpu=True)
        yield config, ['scale', 'relu'], (1e-5, 1e-5)
        config = self.create_inference_config(use_gpu=False)
        yield config, ['scale', 'relu'], (1e-5, 1e-5)
        config = self.create_trt_inference_config()
        config.enable_tensorrt_engine(
            max_batch_size=4,
            workspace_size=102400,
            min_subgraph_size=0,
            precision_mode=paddle_infer.PrecisionType.Float32,
            use_static=False,
            use_calib_mode=False)
        yield config, ['scale', 'relu'], (1e-5, 1e-5)

    def test(self):
        self.run_and_statis(quant=False,
                            max_examples=30,
                            passes=["simplify_with_basic_ops_pass"],
                            min_success_num=30)


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