import random
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import cast

import pytest
import torch
from torch import Tensor, nn
from torch.optim import SGD

from refiners.fluxion import layers as fl
from refiners.fluxion.utils import norm
from refiners.training_utils.callback import Callback, CallbackConfig
from refiners.training_utils.common import (
    Epoch,
    Iteration,
    Step,
    count_learnable_parameters,
    human_readable_number,
)
from refiners.training_utils.config import BaseConfig, ModelConfig
from refiners.training_utils.trainer import (
    Trainer,
    TrainingClock,
    WarmupScheduler,
    count_learnable_parameters,
    human_readable_number,
    register_callback,
    register_model,
)


@dataclass
class MockBatch:
    inputs: torch.Tensor
    targets: torch.Tensor


class MockModelConfig(ModelConfig):
    use_activation: bool


class MockConfig(BaseConfig):
    mock_model: MockModelConfig
    mock_callback: CallbackConfig


class MockModel(fl.Chain):
    def __init__(self):
        super().__init__(
            fl.Linear(10, 10),
            fl.Linear(10, 10),
            fl.Linear(10, 10),
        )

    def add_activation(self) -> None:
        self.insert(1, fl.SiLU())
        self.insert(3, fl.SiLU())


class MockCallback(Callback["MockTrainer"]):
    def __init__(self) -> None:
        self.optimizer_step_count = 0
        self.batch_end_count = 0
        self.optimizer_step_random_int: int | None = None
        self.batch_end_random_int: int | None = None

    def on_init_begin(self, trainer: "MockTrainer") -> None:
        pass

    def on_optimizer_step_begin(self, trainer: "MockTrainer") -> None:
        self.optimizer_step_count += 1
        self.optimizer_step_random_int = random.randint(0, 100)

    def on_batch_end(self, trainer: "MockTrainer") -> None:
        self.batch_end_count += 1
        self.batch_end_random_int = random.randint(0, 100)


class MockTrainer(Trainer[MockConfig, MockBatch]):
    step_counter: int = 0
    model_registration_counter: int = 0

    @property
    def dataset_length(self) -> int:
        return 20

    def get_item(self, index: int) -> MockBatch:
        return MockBatch(inputs=torch.randn(1, 10), targets=torch.randn(1, 10))

    def collate_fn(self, batch: list[MockBatch]) -> MockBatch:
        return MockBatch(
            inputs=torch.cat([b.inputs for b in batch]),
            targets=torch.cat([b.targets for b in batch]),
        )

    @register_callback()
    def mock_callback(self, config: CallbackConfig) -> MockCallback:
        return MockCallback()

    @register_model()
    def mock_model(self, config: MockModelConfig) -> MockModel:
        model = MockModel()
        if config.use_activation:
            model.add_activation()

        self.model_registration_counter += 1
        return model

    def compute_loss(self, batch: MockBatch) -> Tensor:
        self.step_counter += 1
        inputs, targets = batch.inputs.to(self.device), batch.targets.to(self.device)
        outputs = self.mock_model(inputs)
        return norm(outputs - targets)


@pytest.fixture
def mock_config() -> MockConfig:
    config = MockConfig.load_from_toml(Path(__file__).parent / "mock_config.toml")
    return config


@pytest.fixture
def mock_trainer(mock_config: MockConfig) -> MockTrainer:
    return MockTrainer(config=mock_config)


@pytest.fixture
def mock_trainer_short(mock_config: MockConfig) -> MockTrainer:
    mock_config_short = mock_config.model_copy(deep=True)
    mock_config_short.training.duration = Step(3)
    return MockTrainer(config=mock_config_short)


@pytest.fixture
def mock_model() -> fl.Chain:
    return MockModel()


def test_count_learnable_parameters_with_params() -> None:
    params = [
        nn.Parameter(torch.randn(2, 2), requires_grad=True),
        nn.Parameter(torch.randn(5), requires_grad=False),
        nn.Parameter(torch.randn(3, 3), requires_grad=True),
    ]
    # cast because of PyTorch 2.2, see https://github.com/pytorch/pytorch/issues/118736
    assert count_learnable_parameters(cast(list[nn.Parameter], params)) == 13


def test_count_learnable_parameters_with_model(mock_model: fl.Chain) -> None:
    assert count_learnable_parameters(mock_model.parameters()) == 330


def test_human_readable_number() -> None:
    assert human_readable_number(123) == "123.0"
    assert human_readable_number(1234) == "1.2K"
    assert human_readable_number(1234567) == "1.2M"


@pytest.fixture
def training_clock() -> TrainingClock:
    return TrainingClock(
        dataset_length=100,
        batch_size=10,
        training_duration=Epoch(5),
        gradient_accumulation=Epoch(1),
        evaluation_interval=Epoch(1),
        lr_scheduler_interval=Epoch(1),
    )


def test_small_dataset_error():
    with pytest.raises(AssertionError):
        TrainingClock(
            dataset_length=3,
            batch_size=10,
            training_duration=Epoch(5),
            gradient_accumulation=Epoch(1),
            evaluation_interval=Epoch(1),
            lr_scheduler_interval=Epoch(1),
        )


def test_zero_batch_size_error():
    with pytest.raises(AssertionError):
        TrainingClock(
            dataset_length=3,
            batch_size=0,
            training_duration=Epoch(5),
            gradient_accumulation=Epoch(1),
            evaluation_interval=Epoch(1),
            lr_scheduler_interval=Epoch(1),
        )


def test_time_unit_to_steps_conversion(training_clock: TrainingClock) -> None:
    assert training_clock.convert_time_value_to_steps(Epoch(1)) == 10
    assert training_clock.convert_time_value_to_steps(Epoch(2)) == 20
    assert training_clock.convert_time_value_to_steps(Step(1)) == 1
    assert training_clock.convert_time_value_to_steps(Iteration(1)) == 10


def test_steps_to_time_unit_conversion(training_clock: TrainingClock) -> None:
    assert training_clock.convert_steps_to_time_unit(10, Epoch) == 1
    assert training_clock.convert_steps_to_time_unit(20, Epoch) == 2
    assert training_clock.convert_steps_to_time_unit(1, Step) == 1
    assert training_clock.convert_steps_to_time_unit(10, Iteration) == 1


def test_clock_properties(training_clock: TrainingClock) -> None:
    assert training_clock.num_batches_per_epoch == 10
    assert training_clock.num_epochs == 5
    assert training_clock.num_iterations == 5
    assert training_clock.num_steps == 50


def test_timer_functionality(training_clock: TrainingClock) -> None:
    training_clock.start_timer()
    assert training_clock.start_time is not None
    training_clock.stop_timer()
    assert training_clock.end_time is not None
    assert training_clock.time_elapsed >= 0


def test_state_based_properties(training_clock: TrainingClock) -> None:
    training_clock.step = 5  # Halfway through the first epoch
    assert not training_clock.is_due(training_clock.evaluation_interval)  # Assuming evaluation every epoch
    training_clock.step = 10  # End of the first epoch
    assert training_clock.is_due(training_clock.evaluation_interval)


def test_mock_trainer_initialization(mock_config: MockConfig, mock_trainer: MockTrainer) -> None:
    assert mock_trainer.config == mock_config
    assert isinstance(mock_trainer, MockTrainer)
    assert mock_trainer.optimizer is not None
    assert mock_trainer.lr_scheduler is not None
    assert mock_trainer.model_registration_counter == 1


def test_training_cycle(mock_trainer: MockTrainer) -> None:
    clock = mock_trainer.clock
    config = mock_trainer.config

    assert clock.num_step_per_iteration == config.training.gradient_accumulation.number
    assert clock.num_batches_per_epoch == mock_trainer.dataset_length // config.training.batch_size

    assert mock_trainer.step_counter == 0
    assert clock.epoch == 0

    mock_trainer.train()

    assert clock.epoch == config.training.duration.number
    assert clock.step == config.training.duration.number * clock.num_batches_per_epoch

    assert mock_trainer.step_counter == mock_trainer.clock.step


def test_callback_registration(mock_trainer: MockTrainer) -> None:
    mock_trainer.train()

    # Check that the callback skips every other iteration
    assert mock_trainer.mock_callback.optimizer_step_count == mock_trainer.clock.iteration // 2
    assert mock_trainer.mock_callback.batch_end_count == mock_trainer.clock.step // 3

    # Check that the random seed was set
    assert mock_trainer.mock_callback.optimizer_step_random_int == 81
    assert mock_trainer.mock_callback.batch_end_random_int == 72


def test_training_short_cycle(mock_trainer_short: MockTrainer) -> None:
    clock = mock_trainer_short.clock
    config = mock_trainer_short.config

    assert mock_trainer_short.step_counter == 0
    assert mock_trainer_short.clock.epoch == 0

    mock_trainer_short.train()

    assert clock.step == config.training.duration.number


@pytest.fixture
def warmup_scheduler():
    optimizer = SGD([nn.Parameter(torch.randn(2, 2), requires_grad=True)], lr=0.1)
    scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer, 1)
    return WarmupScheduler(optimizer, scheduler, warmup_scheduler_steps=100)


def test_initial_lr(warmup_scheduler: WarmupScheduler) -> None:
    optimizer = warmup_scheduler.optimizer
    for group in optimizer.param_groups:
        assert group["lr"] == 1e-3


def test_warmup_lr(warmup_scheduler: WarmupScheduler) -> None:
    with warnings.catch_warnings():
        warnings.filterwarnings(
            "ignore",
            category=UserWarning,
            message=r"Detected call of `lr_scheduler.step\(\)` before `optimizer.step\(\)`",
        )
        for _ in range(102):
            warmup_scheduler.step()
    optimizer = warmup_scheduler.optimizer
    for group in optimizer.param_groups:
        assert group["lr"] == 0.1


class MockTrainerWith2Models(MockTrainer):
    @register_model()
    def mock_model1(self, config: ModelConfig) -> MockModel:
        return MockModel()

    @register_model()
    def mock_model2(self, config: ModelConfig) -> MockModel:
        return MockModel()

    def compute_loss(self, batch: MockBatch) -> Tensor:
        self.step_counter += 1
        inputs, targets = batch.inputs.to(self.device), batch.targets.to(self.device)
        outputs = self.mock_model2(self.mock_model1(inputs))
        return norm(outputs - targets)


class MockConfig_2_Models(BaseConfig):
    mock_model1: ModelConfig
    mock_model2: ModelConfig


@pytest.fixture
def mock_config_2_models() -> MockConfig_2_Models:
    return MockConfig_2_Models.load_from_toml(Path(__file__).parent / "mock_config_2_models.toml")


@pytest.fixture
def mock_trainer_2_models(mock_config_2_models: MockConfig) -> MockTrainerWith2Models:
    return MockTrainerWith2Models(config=mock_config_2_models)


def test_optimizer_parameters(mock_trainer_2_models: MockTrainerWith2Models) -> None:
    assert len(mock_trainer_2_models.optimizer.param_groups) == 2
    assert mock_trainer_2_models.optimizer.param_groups[0]["lr"] == 1e-5