#  Copyright (c) 2023 zfit

from __future__ import annotations

from typing import TYPE_CHECKING

if TYPE_CHECKING:
    import zfit

from collections.abc import Iterable

import tensorflow as tf

from ..core.interfaces import ZfitIndependentParameter, ZfitLoss
from ..util.exception import OperationNotAllowedError
from .baseminimizer import BaseStepMinimizer, minimize_supports


class WrapOptimizer(BaseStepMinimizer):
    def __init__(
        self,
        optimizer,
        tol=None,
        criterion=None,
        strategy=None,
        verbosity=None,
        name=None,
        **kwargs,
    ):
        """Wrap TensorFlow optimizers to have the zfit interface and behavior.

        .. note:: Different behavior of minimize

          While the ``minimize`` method in TensorFlow optimizers executes a single step of the minimization,
          the ``minimize`` method of a :class:`ZfitMinimizer` fully minimizes a function until convergence
          is reached. In order to execute a single step, use the :meth:`~WrapOptimizer.step` method (however,
          this is in general not necessary to do and rather inefficient).

        Args:
            optimizer: Instance of a :class:`tf.keras.optimizers.Optimizer`.
            All other arguments: these are passed through and have the same functionality as described
            in :py:class:`~BaseStepMinimizer`
        """
        if not isinstance(optimizer, tf.keras.optimizers.Optimizer):
            raise TypeError(
                f"optimizer {str(optimizer)} has to be from class Optimizer"
            )
        super().__init__(
            tol=tol,
            criterion=criterion,
            strategy=strategy,
            verbosity=verbosity,
            name=name,
            minimizer_options=None,
            **kwargs,
        )
        self._optimizer_tf = optimizer

    @minimize_supports(init=True)
    def _minimize(self, loss, params, init):
        try:
            return super()._minimize(loss, params, init)
        except ValueError as error:
            if "No gradients provided for any variable" in error.args[0]:
                raise OperationNotAllowedError(
                    "Cannot use TF optimizer with"
                    " a numerical gradient (non-TF function)"
                ) from None
            else:
                raise

    def _step(
        self,
        loss: ZfitLoss,
        params: Iterable[ZfitIndependentParameter],
        init: zfit.result.FitResult | None,
    ) -> tf.Tensor:
        # TODO(WrappedVariable): this is needed if we want to use wrapped Variables
        # import zfit
        # params = zfit.z.math._extract_tfparams(params)
        self._optimizer_tf.minimize(loss=loss.value, var_list=params)
        return loss.value()
