# 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.
"""
Tests to ensure that the training loop works with a dict (1.0)
"""
from copy import deepcopy
from typing import Any, Callable

import pytest
import torch
from torch.utils.data import DataLoader

from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.core.step_result import Result
from pytorch_lightning.metrics import Accuracy
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.trainer.connectors.logger_connector.callback_hook_validator import CallbackHookNameValidator
from pytorch_lightning.trainer.connectors.logger_connector.metrics_holder import MetricsHolder
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from tests.helpers.boring_model import BoringModel, RandomDataset
from tests.helpers.skipif import RunIf


def decorator_with_arguments(fx_name: str = '', hook_fx_name: str = None) -> Callable:

    def decorator(func: Callable) -> Callable:

        def wrapper(self, *args, **kwargs) -> Any:
            # Set information
            self._current_fx_name = fx_name
            self._current_hook_fx_name = hook_fx_name
            self._results = Result()

            result = func(self, *args, **kwargs)

            # cache metrics
            self.trainer.logger_connector.cache_logged_metrics()
            return result

        return wrapper

    return decorator


def test__logger_connector__epoch_result_store__train(tmpdir, monkeypatch):
    """
    Tests that LoggerConnector will properly capture logged information
    and reduce them
    """
    monkeypatch.setenv("PL_DEV_DEBUG", "1")

    class TestModel(BoringModel):

        train_losses = []

        @decorator_with_arguments(fx_name="training_step")
        def training_step(self, batch, batch_idx):
            output = self.layer(batch)
            loss = self.loss(batch, output)

            self.train_losses.append(loss)

            self.log("train_loss", loss, on_step=True, on_epoch=True)

            return {"loss": loss}

        def training_step_end(self, *_):
            self.train_results = deepcopy(self.trainer.logger_connector.cached_results)

    model = TestModel()
    model.training_epoch_end = None
    model.val_dataloader = None

    trainer = Trainer(
        default_root_dir=tmpdir,
        limit_train_batches=2,
        limit_val_batches=4,
        max_epochs=1,
        log_every_n_steps=1,
        weights_summary=None,
    )
    trainer.fit(model)

    train_results = model.train_results

    assert len(train_results(fx_name="training_step", dl_idx=0, opt_idx=0)) == 2
    generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, batch_idx=0, split_idx=0)["train_loss"]
    assert generated == model.train_losses[0]
    generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, batch_idx=1, split_idx=0)["train_loss"]
    assert generated == model.train_losses[1]

    assert train_results.has_reduced is not True

    train_results.has_batch_loop_finished = True

    assert train_results.has_reduced is True

    generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, reduced=True)['train_loss_epoch'].item()
    excepted = torch.stack(model.train_losses).mean().item()
    assert generated == excepted


def test__logger_connector__epoch_result_store__train__ttbt(tmpdir):
    """
    Tests that LoggerConnector will properly capture logged information with ttbt
    and reduce them
    """
    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 TestModel(BoringModel):

        train_losses = []

        def __init__(self):
            super().__init__()
            self.test_hidden = None
            self.layer = torch.nn.Linear(2, 2)

        @decorator_with_arguments(fx_name="training_step")
        def training_step(self, batch, batch_idx, hiddens):
            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 = torch.nn.functional.mse_loss(pred, y_tensor.view(batch_size, truncated_bptt_steps))

            self.train_losses.append(loss)

            self.log('a', loss, on_epoch=True)

            return {'loss': loss, 'hiddens': self.test_hidden}

        def on_train_epoch_start(self) -> None:
            self.test_hidden = None

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

        def training_step_end(self, *_):
            self.train_results = deepcopy(self.trainer.logger_connector.cached_results)

    model = TestModel()
    model.training_epoch_end = None
    model.example_input_array = torch.randn(5, truncated_bptt_steps)

    trainer = Trainer(
        default_root_dir=tmpdir,
        limit_train_batches=10,
        limit_val_batches=0,
        truncated_bptt_steps=truncated_bptt_steps,
        max_epochs=1,
        log_every_n_steps=1,
        weights_summary=None,
    )
    trainer.fit(model)

    train_results = model.train_results

    generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, batch_idx=0)
    assert len(generated) == len(model.train_losses)

    # assert reduction didn't happen yet
    assert train_results.has_reduced is False

    # Launch reduction
    train_results.has_batch_loop_finished = True

    # assert reduction did happen
    assert train_results.has_reduced is True

    generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, reduced=True)['a_epoch'].item()
    assert generated == torch.stack(model.train_losses).mean().item()


@pytest.mark.parametrize('num_dataloaders', [1, 2])
def test__logger_connector__epoch_result_store__test_multi_dataloaders(tmpdir, monkeypatch, num_dataloaders):
    """
    Tests that LoggerConnector will properly capture logged information in multi dataloaders scenario
    """
    monkeypatch.setenv("PL_DEV_DEBUG", "1")

    class TestModel(BoringModel):
        test_losses = {dl_idx: [] for dl_idx in range(num_dataloaders)}

        @decorator_with_arguments(fx_name="test_step")
        def test_step(self, batch, batch_idx, dl_idx=0):
            output = self.layer(batch)
            loss = self.loss(batch, output)
            self.test_losses[dl_idx].append(loss)
            self.log("test_loss", loss, on_step=True, on_epoch=True)
            return {"test_loss": loss}

        def on_test_batch_end(self, *args, **kwargs):
            # save objects as it will be reset at the end of epoch.
            self.batch_results = deepcopy(self.trainer.logger_connector.cached_results)

        def on_test_epoch_end(self):
            # save objects as it will be reset at the end of epoch.
            self.reduce_results = deepcopy(self.trainer.logger_connector.cached_results)

        def test_dataloader(self):
            return [super().test_dataloader()] * num_dataloaders

    model = TestModel()
    model.test_epoch_end = None
    limit_test_batches = 4

    trainer = Trainer(
        default_root_dir=tmpdir,
        limit_train_batches=0,
        limit_val_batches=0,
        limit_test_batches=limit_test_batches,
        max_epochs=1,
        log_every_n_steps=1,
        weights_summary=None,
    )
    trainer.test(model)

    test_results = model.batch_results

    generated = test_results(fx_name="test_step")
    assert len(generated) == num_dataloaders

    for dl_idx in range(num_dataloaders):
        generated = test_results(fx_name="test_step", dl_idx=dl_idx)
        assert len(generated) == limit_test_batches

    test_results = model.reduce_results

    for dl_idx in range(num_dataloaders):
        expected = torch.stack(model.test_losses[dl_idx]).mean()
        generated = test_results(fx_name="test_step", dl_idx=dl_idx, reduced=True)["test_loss_epoch"]
        torch.testing.assert_allclose(generated, expected)


def test_call_back_validator(tmpdir):

    funcs_name = sorted([f for f in dir(Callback) if not f.startswith('_')])

    callbacks_func = [
        'on_after_backward',
        'on_batch_end',
        'on_batch_start',
        'on_before_accelerator_backend_setup',
        'on_before_zero_grad',
        'on_epoch_end',
        'on_epoch_start',
        'on_fit_end',
        'on_fit_start',
        'on_init_end',
        'on_init_start',
        'on_keyboard_interrupt',
        'on_load_checkpoint',
        'on_pretrain_routine_end',
        'on_pretrain_routine_start',
        'on_sanity_check_end',
        'on_sanity_check_start',
        'on_save_checkpoint',
        'on_test_batch_end',
        'on_test_batch_start',
        'on_test_end',
        'on_test_epoch_end',
        'on_test_epoch_start',
        'on_test_start',
        'on_train_batch_end',
        'on_train_batch_start',
        'on_train_end',
        'on_train_epoch_end',
        'on_train_epoch_start',
        'on_train_start',
        'on_validation_batch_end',
        'on_validation_batch_start',
        'on_validation_end',
        'on_validation_epoch_end',
        'on_validation_epoch_start',
        'on_validation_start',
        'setup',
        'teardown',
    ]

    not_supported = [
        "on_before_accelerator_backend_setup",
        "on_fit_end",
        "on_fit_start",
        "on_init_end",
        "on_init_start",
        "on_keyboard_interrupt",
        "on_load_checkpoint",
        "on_pretrain_routine_end",
        "on_pretrain_routine_start",
        "on_sanity_check_end",
        "on_sanity_check_start",
        "on_save_checkpoint",
        "on_test_end",
        "on_train_end",
        "on_validation_end",
        "setup",
        "teardown",
    ]

    assert (
        funcs_name == sorted(callbacks_func)
    ), """Detected new callback function.
        Need to add its logging permission to CallbackHookNameValidator and update this test"""

    validator = CallbackHookNameValidator()

    for func_name in funcs_name:
        # This summarizes where and what is currently possible to log using `self.log`
        is_stage = "train" in func_name or "test" in func_name or "validation" in func_name
        is_start = "start" in func_name or "batch" in func_name
        on_step = is_stage and is_start
        on_epoch = True
        # creating allowed condition
        allowed = (
            is_stage or "batch" in func_name or "epoch" in func_name or "grad" in func_name or "backward" in func_name
        )
        allowed = (
            allowed and "pretrain" not in func_name
            and func_name not in ["on_train_end", "on_test_end", "on_validation_end"]
        )
        if allowed:
            validator.check_logging_in_callbacks(current_hook_fx_name=func_name, on_step=on_step, on_epoch=on_epoch)
            if not is_start and is_stage:
                with pytest.raises(MisconfigurationException, match="function supports only"):
                    validator.check_logging_in_callbacks(
                        current_hook_fx_name=func_name, on_step=True, on_epoch=on_epoch
                    )
        else:
            assert func_name in not_supported
            with pytest.raises(MisconfigurationException, match="function doesn't support"):
                validator.check_logging_in_callbacks(current_hook_fx_name=func_name, on_step=on_step, on_epoch=on_epoch)

        # should not fail
        validator.check_logging_in_callbacks(current_hook_fx_name=None, on_step=None, on_epoch=None)


@RunIf(min_gpus=2)
def test_epoch_results_cache_dp(tmpdir):

    root_device = torch.device("cuda", 0)

    class TestModel(BoringModel):

        def training_step(self, *args, **kwargs):
            result = super().training_step(*args, **kwargs)
            self.log("train_loss_epoch", result["loss"], on_step=False, on_epoch=True)
            return result

        def training_step_end(self, training_step_outputs):  # required for dp
            loss = training_step_outputs["loss"].mean()
            return loss

        def training_epoch_end(self, outputs):
            assert all(out["loss"].device == root_device for out in outputs)
            assert self.trainer.callback_metrics["train_loss_epoch"].device == root_device

        def validation_step(self, *args, **kwargs):
            val_loss = torch.rand(1, device=torch.device("cuda", 1))
            self.log("val_loss_epoch", val_loss, on_step=False, on_epoch=True)
            return val_loss

        def validation_epoch_end(self, outputs):
            assert all(loss.device == root_device for loss in outputs)
            assert self.trainer.callback_metrics["val_loss_epoch"].device == root_device

        def test_step(self, *args, **kwargs):
            test_loss = torch.rand(1, device=torch.device("cuda", 1))
            self.log("test_loss_epoch", test_loss, on_step=False, on_epoch=True)
            return test_loss

        def test_epoch_end(self, outputs):
            assert all(loss.device == root_device for loss in outputs)
            assert self.trainer.callback_metrics["test_loss_epoch"].device == root_device

        def train_dataloader(self):
            return DataLoader(RandomDataset(32, 64), batch_size=4)

        def val_dataloader(self):
            return DataLoader(RandomDataset(32, 64), batch_size=4)

        def test_dataloader(self):
            return DataLoader(RandomDataset(32, 64), batch_size=4)

    model = TestModel()
    trainer = Trainer(
        default_root_dir=tmpdir,
        accelerator="dp",
        gpus=2,
        limit_train_batches=2,
        limit_val_batches=2,
        max_epochs=1,
    )
    trainer.fit(model)
    trainer.test(model, ckpt_path=None)


@pytest.mark.parametrize('to_float', [False, True])
def test_metrics_holder(to_float, tmpdir):

    device = "cuda" if torch.cuda.is_available() else "cpu"
    preds = torch.tensor([[0.9, 0.1]], device=device)

    def is_float(value: Any) -> bool:
        return isinstance(value, float)

    excepted_function = is_float if to_float else torch.is_tensor
    targets = torch.tensor([1], device=device)
    acc = Accuracy().to(device)
    metric_holder = MetricsHolder(to_float=to_float)
    metric_holder.update({
        "x": 1,
        "y": torch.tensor(2),
        "z": acc(preds, targets),
    })
    metric_holder.convert(False, device)
    metrics = metric_holder.metrics
    assert excepted_function(metrics["x"])
    assert excepted_function(metrics["y"])
    assert excepted_function(metrics["z"])


def test_logging_to_progress_bar_with_reserved_key(tmpdir):
    """ Test that logging a metric with a reserved name to the progress bar raises a warning. """

    class TestModel(BoringModel):

        def training_step(self, *args, **kwargs):
            output = super().training_step(*args, **kwargs)
            self.log("loss", output["loss"], prog_bar=True)
            return output

    model = TestModel()
    trainer = Trainer(
        default_root_dir=tmpdir,
        max_steps=2,
    )
    with pytest.warns(UserWarning, match="The progress bar already tracks a metric with the .* 'loss'"):
        trainer.fit(model)