# Copyright The Lightning AI 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.
from collections import OrderedDict
from contextlib import suppress
from dataclasses import dataclass, field
from typing import Any, Dict

from torch import Tensor

import lightning.pytorch as pl
from lightning.pytorch.core.optimizer import do_nothing_closure
from lightning.pytorch.loops import _Loop
from lightning.pytorch.loops.optimization.closure import OutputResult
from lightning.pytorch.loops.progress import _Progress, _ReadyCompletedTracker
from lightning.pytorch.trainer import call
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from lightning.pytorch.utilities.types import STEP_OUTPUT


@dataclass
class ManualResult(OutputResult):
    """A container to hold the result returned by ``_ManualOptimization``.

    It is created from the output of :meth:`~lightning.pytorch.core.module.LightningModule.training_step`.

    Attributes:
        extra: Anything returned by the ``training_step``.
    """

    extra: Dict[str, Any] = field(default_factory=dict)

    @classmethod
    def from_training_step_output(cls, training_step_output: STEP_OUTPUT) -> "ManualResult":
        extra = {}
        if isinstance(training_step_output, dict):
            extra = training_step_output.copy()
        elif isinstance(training_step_output, Tensor):
            extra = {"loss": training_step_output}
        elif training_step_output is not None:
            raise MisconfigurationException(
                "In manual optimization, `training_step` must either return a Tensor or have no return."
            )

        if "loss" in extra:
            # we detach manually as it's expected that it will have a `grad_fn`
            extra["loss"] = extra["loss"].detach()

        return cls(extra=extra)

    def asdict(self) -> Dict[str, Any]:
        return self.extra


_OUTPUTS_TYPE = Dict[str, Any]


class _ManualOptimization(_Loop):
    """A special loop implementing what is known in Lightning as Manual Optimization where the optimization happens
    entirely in the :meth:`~lightning.pytorch.core.module.LightningModule.training_step` and therefore the user is
    responsible for back-propagating gradients and making calls to the optimizers.

    This loop is a trivial case because it performs only a single iteration (calling directly into the module's
    :meth:`~lightning.pytorch.core.module.LightningModule.training_step`) and passing through the output(s).
    """

    output_result_cls = ManualResult

    def __init__(self, trainer: "pl.Trainer") -> None:
        super().__init__(trainer)
        # since manual optimization does not track lr scheduler or optimizer frequencies, we use a simpler progress than
        # `_OptimizationProgress`
        self.optim_step_progress = _Progress.from_defaults(_ReadyCompletedTracker)

        self._output: _OUTPUTS_TYPE = {}

    def run(self, kwargs: OrderedDict) -> _OUTPUTS_TYPE:
        self.on_run_start()
        with suppress(StopIteration):  # no loop to break at this level
            self.advance(kwargs)
        self._restarting = False
        return self.on_run_end()

    def on_run_start(self) -> None:
        # inject logic around the optimizer step
        for lightning_optimizer in self.trainer.strategy._lightning_optimizers:
            lightning_optimizer._on_before_step = self._on_before_step
            lightning_optimizer._on_after_step = self._on_after_step

    def advance(self, kwargs: OrderedDict) -> None:
        """Performs the training step for manual optimization.

        Args:
            kwargs: The kwargs passed down to the hooks.
        """
        trainer = self.trainer

        # manually capture logged metrics
        training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
        del kwargs  # release the batch from memory
        self.trainer.strategy.post_training_step()  # unused hook - call anyway for backward compatibility
        result = self.output_result_cls.from_training_step_output(training_step_output)

        self._output = result.asdict()

    def on_run_end(self) -> _OUTPUTS_TYPE:
        """Returns the result of this loop, i.e., the post-processed outputs from the training step."""
        output, self._output = self._output, {}  # free memory
        # reset logic around the optimizer step
        for lightning_optimizer in self.trainer.strategy._lightning_optimizers:
            lightning_optimizer._on_before_step = do_nothing_closure
            lightning_optimizer._on_after_step = do_nothing_closure
        return output

    def _on_before_step(self) -> None:
        self.optim_step_progress.increment_ready()
        self.trainer.profiler.start("optimizer_step")

    def _on_after_step(self) -> None:
        self.trainer.profiler.stop("optimizer_step")
        self.optim_step_progress.increment_completed()
