# Copyright 2023 The FastEstimator 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 tempfile
import unittest
from typing import List, Tuple

import fastestimator as fe
from fastestimator.op.tensorop.model import ModelOp
from fastestimator.slicer import Slicer
from fastestimator.test.unittest_util import sample_system_object
from fastestimator.types import Array


class FakeSlicer(Slicer):
    def __init__(self, slice, unslice, mode, ds_id, var):
        super().__init__(slice=slice, unslice=unslice, mode=mode, ds_id=ds_id)
        self.var = var

    def _slice_batch(self, batch: Array) -> List[Array]:
        return [batch]

    def _unslice_batch(self, slices: Tuple[Array, ...], key: str) -> Array:
        return slices[0]


class TestSlicer(unittest.TestCase):
    def test_save_and_load_state_tf(self):
        def instantiate_system():
            system = sample_system_object()
            model = fe.build(model_fn=fe.architecture.tensorflow.LeNet, optimizer_fn='adam', model_name='tf')
            system.network = fe.Network(
                ops=[ModelOp(model=model, inputs="x_out", outputs="y_pred")],
                slicers=FakeSlicer(slice="x", unslice=("x", "y_pred"), mode=None, ds_id=None, var=2.0))
            return system

        system = instantiate_system()

        # make some changes
        new_var = 4.0
        system.network.slicers[0].var = new_var

        # save the state
        save_path = tempfile.mkdtemp()
        system.save_state(save_path)

        # reinstantiate system and load the state
        system = instantiate_system()
        system.load_state(save_path)
        loaded_var = system.network.slicers[0].var

        self.assertEqual(loaded_var, new_var)

    def test_save_and_load_state_torch(self):
        def instantiate_system():
            system = sample_system_object()
            model = fe.build(model_fn=fe.architecture.pytorch.LeNet, optimizer_fn='adam', model_name='torch')
            system.network = fe.Network(
                ops=[ModelOp(model=model, inputs="x_out", outputs="y_pred")],
                slicers=FakeSlicer(slice="x", unslice=("x", "y_pred"), mode=None, ds_id=None, var=2.0))
            return system

        system = instantiate_system()

        # make some changes
        new_var = 4.0
        system.network.slicers[0].var = new_var

        # save the state
        save_path = tempfile.mkdtemp()
        system.save_state(save_path)

        # reinstantiate system and load the state
        system = instantiate_system()
        system.load_state(save_path)
        loaded_var = system.network.slicers[0].var

        self.assertEqual(loaded_var, new_var)
