import math
import warnings

import pytest
import torch

import tests.base.utils as tutils
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import (
    EarlyStopping,
)
from tests.base import (
    TestModelBase,
    LightTrainDataloader,
    LightningTestModel,
    LightTestMixin,
    LightValidationMixin
)


def test_early_stopping_cpu_model(tmpdir):
    """Test each of the trainer options."""
    tutils.reset_seed()

    stopping = EarlyStopping(monitor='val_loss', min_delta=0.1)
    trainer_options = dict(
        default_save_path=tmpdir,
        early_stop_callback=stopping,
        gradient_clip_val=1.0,
        overfit_pct=0.20,
        track_grad_norm=2,
        show_progress_bar=True,
        logger=tutils.get_default_testtube_logger(tmpdir),
        train_percent_check=0.1,
        val_percent_check=0.1,
    )

    model, hparams = tutils.get_default_model()
    tutils.run_model_test(trainer_options, model, on_gpu=False)

    # test freeze on cpu
    model.freeze()
    model.unfreeze()


def test_lbfgs_cpu_model(tmpdir):
    """Test each of the trainer options."""
    tutils.reset_seed()

    trainer_options = dict(
        default_save_path=tmpdir,
        max_epochs=2,
        show_progress_bar=False,
        weights_summary='top',
        train_percent_check=1.0,
        val_percent_check=0.2,
    )

    model, hparams = tutils.get_default_model(lbfgs=True)
    tutils.run_model_test_no_loggers(trainer_options, model, min_acc=0.30)


def test_default_logger_callbacks_cpu_model(tmpdir):
    """Test each of the trainer options."""
    tutils.reset_seed()

    trainer_options = dict(
        default_save_path=tmpdir,
        max_epochs=1,
        gradient_clip_val=1.0,
        overfit_pct=0.20,
        show_progress_bar=False,
        train_percent_check=0.01,
        val_percent_check=0.01,
    )

    model, hparams = tutils.get_default_model()
    tutils.run_model_test_no_loggers(trainer_options, model)

    # test freeze on cpu
    model.freeze()
    model.unfreeze()


def test_running_test_after_fitting(tmpdir):
    """Verify test() on fitted model."""
    tutils.reset_seed()

    hparams = tutils.get_default_hparams()
    model = LightningTestModel(hparams)

    # logger file to get meta
    logger = tutils.get_default_testtube_logger(tmpdir, False)

    # logger file to get weights
    checkpoint = tutils.init_checkpoint_callback(logger)

    trainer_options = dict(
        default_save_path=tmpdir,
        show_progress_bar=False,
        max_epochs=8,
        train_percent_check=0.4,
        val_percent_check=0.2,
        test_percent_check=0.2,
        checkpoint_callback=checkpoint,
        logger=logger
    )

    # fit model
    trainer = Trainer(**trainer_options)
    result = trainer.fit(model)

    assert result == 1, 'training failed to complete'

    trainer.test()

    # test we have good test accuracy
    tutils.assert_ok_model_acc(trainer, thr=0.35)


def test_running_test_without_val(tmpdir):
    """Verify `test()` works on a model with no `val_loader`."""
    tutils.reset_seed()

    class CurrentTestModel(LightTrainDataloader, LightTestMixin, TestModelBase):
        pass

    hparams = tutils.get_default_hparams()
    model = CurrentTestModel(hparams)

    # logger file to get meta
    logger = tutils.get_default_testtube_logger(tmpdir, False)

    # logger file to get weights
    checkpoint = tutils.init_checkpoint_callback(logger)

    trainer_options = dict(
        show_progress_bar=False,
        max_epochs=1,
        train_percent_check=0.4,
        val_percent_check=0.2,
        test_percent_check=0.2,
        checkpoint_callback=checkpoint,
        logger=logger,
        early_stop_callback=False
    )

    # fit model
    trainer = Trainer(**trainer_options)
    result = trainer.fit(model)

    assert result == 1, 'training failed to complete'

    trainer.test()

    # test we have good test accuracy
    tutils.assert_ok_model_acc(trainer)


def test_disabled_validation():
    """Verify that `val_percent_check=0` disables the validation loop unless `fast_dev_run=True`."""
    tutils.reset_seed()

    class CurrentModel(LightTrainDataloader, LightValidationMixin, TestModelBase):

        validation_step_invoked = False
        validation_end_invoked = False

        def validation_step(self, *args, **kwargs):
            self.validation_step_invoked = True
            return super().validation_step(*args, **kwargs)

        def validation_end(self, *args, **kwargs):
            self.validation_end_invoked = True
            return super().validation_end(*args, **kwargs)

    hparams = tutils.get_default_hparams()
    model = CurrentModel(hparams)

    trainer_options = dict(
        show_progress_bar=False,
        max_epochs=2,
        train_percent_check=0.4,
        val_percent_check=0.0,
        fast_dev_run=False,
    )

    trainer = Trainer(**trainer_options)
    result = trainer.fit(model)

    # check that val_percent_check=0 turns off validation
    assert result == 1, 'training failed to complete'
    assert trainer.current_epoch == 1
    assert not model.validation_step_invoked, '`validation_step` should not run when `val_percent_check=0`'
    assert not model.validation_end_invoked, '`validation_end` should not run when `val_percent_check=0`'

    # check that val_percent_check has no influence when fast_dev_run is turned on
    model = CurrentModel(hparams)
    trainer_options.update(fast_dev_run=True)
    trainer = Trainer(**trainer_options)
    result = trainer.fit(model)

    assert result == 1, 'training failed to complete'
    assert trainer.current_epoch == 0
    assert model.validation_step_invoked, 'did not run `validation_step` with `fast_dev_run=True`'
    assert model.validation_end_invoked, 'did not run `validation_end` with `fast_dev_run=True`'


def test_single_gpu_batch_parse():
    tutils.reset_seed()

    if not tutils.can_run_gpu_test():
        return

    trainer = Trainer()

    # batch is just a tensor
    batch = torch.rand(2, 3)
    batch = trainer.transfer_batch_to_gpu(batch, 0)
    assert batch.device.index == 0 and batch.type() == 'torch.cuda.FloatTensor'

    # tensor list
    batch = [torch.rand(2, 3), torch.rand(2, 3)]
    batch = trainer.transfer_batch_to_gpu(batch, 0)
    assert batch[0].device.index == 0 and batch[0].type() == 'torch.cuda.FloatTensor'
    assert batch[1].device.index == 0 and batch[1].type() == 'torch.cuda.FloatTensor'

    # tensor list of lists
    batch = [[torch.rand(2, 3), torch.rand(2, 3)]]
    batch = trainer.transfer_batch_to_gpu(batch, 0)
    assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'
    assert batch[0][1].device.index == 0 and batch[0][1].type() == 'torch.cuda.FloatTensor'

    # tensor dict
    batch = [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)}]
    batch = trainer.transfer_batch_to_gpu(batch, 0)
    assert batch[0]['a'].device.index == 0 and batch[0]['a'].type() == 'torch.cuda.FloatTensor'
    assert batch[0]['b'].device.index == 0 and batch[0]['b'].type() == 'torch.cuda.FloatTensor'

    # tuple of tensor list and list of tensor dict
    batch = ([torch.rand(2, 3) for _ in range(2)],
             [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)} for _ in range(2)])
    batch = trainer.transfer_batch_to_gpu(batch, 0)
    assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'

    assert batch[1][0]['a'].device.index == 0
    assert batch[1][0]['a'].type() == 'torch.cuda.FloatTensor'

    assert batch[1][0]['b'].device.index == 0
    assert batch[1][0]['b'].type() == 'torch.cuda.FloatTensor'


def test_simple_cpu(tmpdir):
    """Verify continue training session on CPU."""
    tutils.reset_seed()

    hparams = tutils.get_default_hparams()
    model = LightningTestModel(hparams)

    # logger file to get meta
    trainer_options = dict(
        default_save_path=tmpdir,
        max_epochs=1,
        val_percent_check=0.1,
        train_percent_check=0.1,
    )

    # fit model
    trainer = Trainer(**trainer_options)
    result = trainer.fit(model)

    # traning complete
    assert result == 1, 'amp + ddp model failed to complete'


def test_cpu_model(tmpdir):
    """Make sure model trains on CPU."""
    tutils.reset_seed()

    trainer_options = dict(
        default_save_path=tmpdir,
        show_progress_bar=False,
        logger=tutils.get_default_testtube_logger(tmpdir),
        max_epochs=1,
        train_percent_check=0.4,
        val_percent_check=0.4
    )

    model, hparams = tutils.get_default_model()

    tutils.run_model_test(trainer_options, model, on_gpu=False)


def test_all_features_cpu_model(tmpdir):
    """Test each of the trainer options."""
    tutils.reset_seed()

    trainer_options = dict(
        default_save_path=tmpdir,
        gradient_clip_val=1.0,
        overfit_pct=0.20,
        track_grad_norm=2,
        show_progress_bar=False,
        logger=tutils.get_default_testtube_logger(tmpdir),
        accumulate_grad_batches=2,
        max_epochs=1,
        train_percent_check=0.4,
        val_percent_check=0.4
    )

    model, hparams = tutils.get_default_model()
    tutils.run_model_test(trainer_options, model, on_gpu=False)


def test_tbptt_cpu_model(tmpdir):
    """Test truncated back propagation through time works."""
    tutils.reset_seed()

    truncated_bptt_steps = 2
    sequence_size = 30
    batch_size = 30

    x_seq = torch.rand(batch_size, sequence_size, 1)
    y_seq_list = torch.rand(batch_size, sequence_size, 1).tolist()

    class MockSeq2SeqDataset(torch.utils.data.Dataset):
        def __getitem__(self, i):
            return x_seq, y_seq_list

        def __len__(self):
            return 1

    class BpttTestModel(LightTrainDataloader, TestModelBase):
        def __init__(self, hparams):
            super().__init__(hparams)
            self.test_hidden = None

        def training_step(self, batch, batch_idx, hiddens):
            assert hiddens == self.test_hidden, "Hidden state not persistent between tbptt steps"
            self.test_hidden = torch.rand(1)

            x_tensor, y_list = batch
            assert x_tensor.shape[1] == truncated_bptt_steps, "tbptt split Tensor failed"

            y_tensor = torch.tensor(y_list, dtype=x_tensor.dtype)
            assert y_tensor.shape[1] == truncated_bptt_steps, "tbptt split list failed"

            pred = self(x_tensor.view(batch_size, truncated_bptt_steps))
            loss_val = torch.nn.functional.mse_loss(
                pred, y_tensor.view(batch_size, truncated_bptt_steps))
            return {
                'loss': loss_val,
                'hiddens': self.test_hidden,
            }

        def train_dataloader(self):
            return torch.utils.data.DataLoader(
                dataset=MockSeq2SeqDataset(),
                batch_size=batch_size,
                shuffle=False,
                sampler=None,
            )

    trainer_options = dict(
        default_save_path=tmpdir,
        max_epochs=1,
        truncated_bptt_steps=truncated_bptt_steps,
        val_percent_check=0,
        weights_summary=None,
        early_stop_callback=False
    )

    hparams = tutils.get_default_hparams()
    hparams.batch_size = batch_size
    hparams.in_features = truncated_bptt_steps
    hparams.hidden_dim = truncated_bptt_steps
    hparams.out_features = truncated_bptt_steps

    model = BpttTestModel(hparams)

    # fit model
    trainer = Trainer(**trainer_options)
    result = trainer.fit(model)

    assert result == 1, 'training failed to complete'


def test_single_gpu_model(tmpdir):
    """Make sure single GPU works (DP mode)."""
    tutils.reset_seed()

    if not torch.cuda.is_available():
        warnings.warn('test_single_gpu_model cannot run.'
                      ' Rerun on a GPU node to run this test')
        return
    model, hparams = tutils.get_default_model()

    trainer_options = dict(
        default_save_path=tmpdir,
        show_progress_bar=False,
        max_epochs=1,
        train_percent_check=0.1,
        val_percent_check=0.1,
        gpus=1
    )

    tutils.run_model_test(trainer_options, model)


def test_nan_loss_detection(tmpdir):
    test_step = 8

    class InfLossModel(LightTrainDataloader, TestModelBase):

        def training_step(self, batch, batch_idx):
            output = super().training_step(batch, batch_idx)
            if batch_idx == test_step:
                if isinstance(output, dict):
                    output['loss'] *= torch.tensor(math.inf)  # make loss infinite
                else:
                    output /= 0
            return output

    hparams = tutils.get_default_hparams()
    model = InfLossModel(hparams)

    # fit model
    trainer = Trainer(
        default_save_path=tmpdir,
        max_steps=(test_step + 1),
    )

    with pytest.raises(ValueError, match=r'.*The loss returned in `training_step` is nan or inf.*'):
        trainer.fit(model)
        assert trainer.global_step == test_step

    for param in model.parameters():
        assert torch.isfinite(param).all()


def test_nan_params_detection(tmpdir):
    test_step = 8

    class NanParamModel(LightTrainDataloader, TestModelBase):

        def on_after_backward(self):
            if self.global_step == test_step:
                # simulate parameter that became nan
                torch.nn.init.constant_(self.c_d1.bias, math.nan)

    hparams = tutils.get_default_hparams()

    model = NanParamModel(hparams)
    trainer = Trainer(
        default_save_path=tmpdir,
        max_steps=(test_step + 1),
    )

    with pytest.raises(ValueError, match=r'.*Detected nan and/or inf values in `c_d1.bias`.*'):
        trainer.fit(model)
        assert trainer.global_step == test_step

    # after aborting the training loop, model still has nan-valued params
    params = torch.cat([param.view(-1) for param in model.parameters()])
    assert not torch.isfinite(params).all()


# if __name__ == '__main__':
#     pytest.main([__file__])