# Copyright The PyTorch Lightning team.
#
# 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 torch
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.accelerators.base_backend import Accelerator
from pytorch_lightning.utilities import AMPType, rank_zero_warn


class CPUBackend(Accelerator):

    def __init__(self, trainer):
        super().__init__(trainer)

    def setup(self, model):
        # run through amp wrapper
        if self.trainer.amp_backend:
            raise MisconfigurationException('amp + cpu is not supported.  Please use a GPU option')

        # call setup after the ddp process has connected
        self.trainer.call_setup_hook(model)

        # CHOOSE OPTIMIZER
        # allow for lr schedulers as well
        optimizers, lr_schedulers, optimizer_frequencies = self.trainer.init_optimizers(model)
        self.trainer.optimizers = optimizers
        self.trainer.lr_schedulers = lr_schedulers
        self.trainer.optimizer_frequencies = optimizer_frequencies
        self.trainer.model = model

    def train(self):
        model = self.trainer.model

        # set up training routine
        self.trainer.train_loop.setup_training(model)

        # train or test
        results = self.train_or_test()
        return results

    def training_step(self, args):
        if self.trainer.amp_backend == AMPType.NATIVE:
            with torch.cuda.amp.autocast():
                output = self.trainer.model.training_step(*args)
        else:
            output = self.trainer.model.training_step(*args)
        return output

    def validation_step(self, args):
        if self.trainer.amp_backend == AMPType.NATIVE:
            with torch.cuda.amp.autocast():
                output = self.trainer.model.validation_step(*args)
        else:
            output = self.trainer.model.validation_step(*args)
        return output

    def test_step(self, args):
        if self.trainer.amp_backend == AMPType.NATIVE:
            with torch.cuda.amp.autocast():
                output = self.trainer.model.test_step(*args)
        else:
            output = self.trainer.model.test_step(*args)
        return output