#!/usr/bin/env python3

# Copyright (c) 2022 CINN 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.

import argparse
import logging
import os
import sys
import unittest

import numpy as np
from cinn.common import DefaultHostTarget, DefaultNVGPUTarget
from cinn.frontend import PaddleModelConvertor
from cinn.runtime import seed as cinn_seed
from op_mappers.op_mapper_test import OpMapperTest

import paddle

logging.basicConfig(level=os.environ.get('LOG_LEVEL', 'INFO').upper())
logger = logging.getLogger(name="paddle_model_convertor")

parser = argparse.ArgumentParser(
    description='Load Paddle Model File and Running at CINN'
)
parser.add_argument(
    "--path", help="The path to load the paddle model", type=str, required=True
)
parser.add_argument(
    "-m",
    "--model_filename",
    help="The filename of model file, default \"__model__\"",
    type=str,
    default="__model__",
)
parser.add_argument(
    "-p",
    "--params_filename",
    help="The filename of model parameter file, default None, in which each parameter will saved in each file",
    type=str,
    default=None,
)
parser.add_argument(
    "-cuda",
    "--enable_cuda",
    help="Whether enable CUDA, default True",
    type=bool,
    default=True,
)
args = parser.parse_args()

np.random.seed(1234)
paddle.seed(1234)
cinn_seed(1234)

paddle.enable_static()

# first save paddle model like:
# ```
# import paddle
# paddle.enable_static()

# x = paddle.static.data(name='x', shape=[10, 12, 128, 128], dtype='float32')
# y = paddle.static.data(name='y', shape=[10, 12, 128, 128], dtype='float32')
# prediction = paddle.stack([x, y], 1)

# place = paddle.CUDAPlace(0)

# exe = paddle.static.Executor(place)
# exe.run(paddle.static.default_startup_program())
# prog = paddle.static.default_main_program()

# paddle.static.io.save_inference_model("./stack", [x.name, y.name], [prediction], exe, prog)
# ```
# Second load and run model like:
# ```
# python test_paddle_model_convertor.py --path build/thirds/resnet_model -m "__model__" -p "params"
# ```


class TestPaddleModel(OpMapperTest):
    def setUp(self):
        if args.enable_cuda:
            self.target = DefaultNVGPUTarget()
            self.place = paddle.CUDAPlace(0)
        else:
            self.target = DefaultHostTarget()
            self.place = paddle.CPUPlace()

        self.model_dir = args.path
        self.model_filename = args.model_filename
        self.params_filename = args.params_filename

        logger.info(
            "Run Model From \"{}\", which model filename is \"{}\", and parameter filename is \"{}\"".format(
                self.model_dir, self.model_filename, self.params_filename
            )
        )

        self.load_paddle_program()
        self.init_case()

    @staticmethod
    def eliminate_unkown_shape(shape):
        return [1 if dim == -1 else dim for dim in shape]

    def get_paddle_op_attrs(self, op):
        attr_map = {}
        for n in op.attr_names:
            attr_map[n] = op.attr(n)

        return attr_map

    def init_case(self):
        self.feed_data = {}
        for i in range(len(self.feed_names)):
            # check no repeat variable
            self.assertNotIn(
                self.feed_names[i],
                self.feed_data,
                msg="Repeat feed name: " + self.feed_names[i],
            )

            dtype = self.paddleddtype2nptype(self.feed_dtypes[i])
            # random int type data should not limited to [0, 1]
            high = 1 if ("int" not in dtype) else self.feed_shapes[i][0]

            # the paddle's feed list need dict not list
            self.feed_data[self.feed_names[i]] = self.random(
                self.eliminate_unkown_shape(self.feed_shapes[i]),
                dtype,
                high=high,
            )

    def load_paddle_program(self):
        self.exe = paddle.static.Executor(self.place)

        [
            self.inference_program,
            self.feed_names,
            self.fetch_targets,
        ] = paddle.static.io.load_inference_model(
            path_prefix=self.model_dir,
            executor=self.exe,
        )

        self.param_vars = paddle.load(
            self.model_dir,
            model_filename=self.model_filename,
            params_filename=self.params_filename,
            return_numpy=True,
        )

        logger.debug(msg=f"Program:\n{self.inference_program}")
        logger.debug(msg=f"Param List: {self.param_vars.keys()}")
        logger.debug(msg=f"Feed List: {self.feed_names}")
        logger.debug(
            msg="Fetch List: {}".format(
                [var.name for var in self.fetch_targets]
            )
        )

        self.feed_shapes = []
        self.feed_dtypes = []

        for var in self.inference_program.list_vars():
            if var.name in self.feed_names:
                self.feed_shapes.append(var.shape)
                self.feed_dtypes.append(var.dtype)

        self.assertEqual(
            len(self.feed_names),
            len(self.feed_shapes),
            msg="Cannot found some feed var in program!",
        )

    def build_paddle_program(self, target):
        self.paddle_outputs = self.exe.run(
            self.inference_program,
            feed=self.feed_data,
            fetch_list=self.fetch_targets,
            return_numpy=True,
        )
        logger.debug(f"Paddle Result:\n{self.paddle_outputs}")

    def build_cinn_program(self, target):
        self.assertEqual(
            1,
            self.inference_program.num_blocks,
            msg="CINN only support single block now",
        )

        feed_with_param = []

        convertor = PaddleModelConvertor(target)
        for i in range(len(self.feed_names)):
            convertor.create_input(
                dtype=self.paddleddtype2nptype(self.feed_dtypes[i]),
                shape=self.feed_data[self.feed_names[i]].shape,
                name=self.feed_names[i],
            )
            feed_with_param.append(self.feed_names[i])

        for param_name, param_value in self.param_vars.items():
            convertor.create_input(
                dtype=str(param_value.dtype),
                shape=param_value.shape,
                name=param_name,
            )
            feed_with_param.append(param_name)

        for op in self.inference_program.global_block().ops:
            if op.desc.type() == "feed" or op.desc.type() == "fetch":
                continue
            convertor.append_op(
                op.desc.type(),
                op.desc.inputs(),
                op.desc.outputs(),
                self.get_paddle_op_attrs(op),
            )

        prog = convertor()

        # get cinn input list
        inputs = prog.get_inputs()
        logger.debug(f"CINN Input List: {[var.name() for var in inputs]}")
        self.assertEqual(
            len(feed_with_param),
            len(inputs),
            msg="The paddle's input list not equal to cinn's input list!",
        )

        # map the name the variable
        input_dict = {var.name(): var for var in inputs}

        cinn_inputs = []
        cinn_feed_datas = []
        for name in feed_with_param:
            cinn_name = convertor.get_cinn_name(name)

            self.assertIn(
                cinn_name,
                input_dict,
                msg="Cannot find variable "
                + cinn_name
                + " in cinn program's input, which are "
                + str(input_dict.items()),
            )
            cinn_inputs.append(input_dict[cinn_name])

            if name in self.feed_data:
                cinn_feed_datas.append(self.feed_data[name])
            else:
                self.assertIn(
                    name,
                    self.param_vars,
                    msg="The input variable should in feed list or parameter list",
                )
                cinn_feed_datas.append(self.param_vars[name])

        # get cinn output list
        fetch_names = {var.name for var in self.fetch_targets}
        output_dict = convertor.get_fetch_list(fetch_names)
        cinn_output = [output_dict[var.name] for var in self.fetch_targets]

        # run and get result
        self.cinn_outputs = self.get_cinn_output(
            prog, target, cinn_inputs, cinn_feed_datas, cinn_output, passes=[]
        )

        logger.debug(f"CINN Result:\n{self.cinn_outputs}")

    def test_check_results(self):
        # TODO(6clc): There is a random accuracy problem,
        #             temporarily adjust max_absolute_error from 1e-6 to 1e-3
        self.check_outputs_and_grads(
            max_relative_error=1e-2, max_absolute_error=1e-3
        )


if __name__ == "__main__":
    tester = unittest.defaultTestLoader.loadTestsFromTestCase(TestPaddleModel)
    test_runer = unittest.TextTestRunner()
    res = test_runer.run(tester)
    sys.exit(not res.wasSuccessful())
