from abc import ABC, abstractmethod
from copy import deepcopy
from typing import Any, Callable, Dict, Optional, Union

import numpy as np
import torch
from torch import Tensor, eye, nn, ones, optim
from torch.nn.utils import clip_grad_norm_
from torch.utils import data
from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.tensorboard import SummaryWriter

from sbi import utils as utils
from sbi.inference.base import NeuralInference
from sbi.inference.posteriors.ratio_based_posterior import RatioBasedPosterior
from sbi.types import ScalarFloat
from sbi.utils import check_estimator_arg, clamp_and_warn, x_shape_from_simulation
from sbi.utils.torchutils import ensure_theta_batched, ensure_x_batched


class RatioEstimator(NeuralInference, ABC):
    def __init__(
        self,
        simulator: Callable,
        prior,
        num_workers: int = 1,
        simulation_batch_size: int = 1,
        classifier: Union[str, Callable] = "resnet",
        mcmc_method: str = "slice_np",
        mcmc_parameters: Optional[Dict[str, Any]] = None,
        device: str = "cpu",
        logging_level: Union[int, str] = "warning",
        summary_writer: Optional[SummaryWriter] = None,
        show_progress_bars: bool = True,
        show_round_summary: bool = False,
    ):
        r"""Sequential Neural Ratio Estimation.

        We implement two inference methods in the respective subclasses.

        - SNRE_A / AALR is limited to `num_atoms=2`, but allows for density evaluation
          when training for one round.
        - SNRE_B / SRE can use more than two atoms, potentially boosting performance,
          but allows for posterior evaluation **only up to a normalizing constant**,
          even when training only one round.

        Args:
            classifier: Classifier trained to approximate likelihood ratios. If it is
                a string, use a pre-configured network of the provided type (one of
                linear, mlp, resnet). Alternatively, a function that builds a custom
                neural network can be provided. The function will be called with the
                first batch of simulations (theta, x), which can thus be used for shape
                inference and potentially for z-scoring. It needs to return a PyTorch
                `nn.Module` implementing the classifier.
            mcmc_method: Method used for MCMC sampling, one of `slice_np`, `slice`, `hmc`, `nuts`.
                Currently defaults to `slice_np` for a custom numpy implementation of
                slice sampling; select `hmc`, `nuts` or `slice` for Pyro-based sampling.
            mcmc_parameters: Dictionary overriding the default parameters for MCMC.
                The following parameters are supported: `thin` to set the thinning
                factor for the chain, `warmup_steps` to set the initial number of
                samples to discard, `num_chains` for the number of chains, `init_strategy`
                for the initialisation strategy for chains; `prior` will draw init
                locations from prior, whereas `sir` will use Sequential-Importance-
                Resampling using `init_strategy_num_candidates` to find init
                locations.

        See docstring of `NeuralInference` class for all other arguments.
        """

        super().__init__(
            simulator=simulator,
            prior=prior,
            num_workers=num_workers,
            simulation_batch_size=simulation_batch_size,
            device=device,
            logging_level=logging_level,
            summary_writer=summary_writer,
            show_progress_bars=show_progress_bars,
            show_round_summary=show_round_summary,
        )

        # As detailed in the docstring, `density_estimator` is either a string or
        # a callable. The function creating the neural network is attached to
        # `_build_neural_net`. It will be called in the first round and receive
        # thetas and xs as inputs, so that they can be used for shape inference and
        # potentially for z-scoring.
        check_estimator_arg(classifier)
        if isinstance(classifier, str):
            self._build_neural_net = utils.classifier_nn(model=classifier)
        else:
            self._build_neural_net = classifier
        self._posterior = None
        self._mcmc_method = mcmc_method
        self._mcmc_parameters = mcmc_parameters

        # Ratio-based-specific summary_writer fields.
        self._summary.update({"mcmc_times": []})  # type: ignore

    def __call__(
        self,
        num_simulations: int,
        proposal: Optional[Any] = None,
        num_atoms: int = 10,
        training_batch_size: int = 50,
        learning_rate: float = 5e-4,
        validation_fraction: float = 0.1,
        stop_after_epochs: int = 20,
        max_num_epochs: Optional[int] = None,
        clip_max_norm: Optional[float] = 5.0,
        exclude_invalid_x: bool = True,
        discard_prior_samples: bool = False,
        retrain_from_scratch_each_round: bool = False,
    ) -> RatioBasedPosterior:
        r"""Run SNRE.

        Return posterior $p(\theta|x)$ after inference.

        Args:
            num_atoms: Number of atoms to use for classification.
            exclude_invalid_x: Whether to exclude simulation outputs `x=NaN` or `x=±∞`
                during training. Expect errors, silent or explicit, when `False`.
            discard_prior_samples: Whether to discard samples simulated in round 1, i.e.
                from the prior. Training may be sped up by ignoring such less targeted
                samples.
            retrain_from_scratch_each_round: Whether to retrain the conditional density
                estimator for the posterior from scratch each round.

        Returns:
            Posterior $p(\theta|x)$ that can be sampled and evaluated.
        """

        max_num_epochs = 2 ** 31 - 1 if max_num_epochs is None else max_num_epochs

        self._check_proposal(proposal)
        self._round = self._round + 1 if (proposal is not None) else 0

        # If presimulated data was provided from a later round, set the self._round to
        # this value. Otherwise, we would rely on the user to _additionally_ provide the
        # proposal that the presimulated data was sampled from in order for self._round
        # to become larger than 0.
        if self._data_round_index:
            self._round = max(self._round, max(self._data_round_index))

        # Run simulations for the round.
        theta, x = self._run_simulations(proposal, num_simulations)
        self._append_to_data_bank(theta, x, self._round)

        # Load data from most recent round.
        theta, x, _ = self._get_from_data_bank(self._round, exclude_invalid_x, False)

        # First round or if retraining from scratch:
        # Call the `self._build_neural_net` with the rounds' thetas and xs as
        # arguments, which will build the neural network
        # This is passed into NeuralPosterior, to create a neural posterior which
        # can `sample()` and `log_prob()`. The network is accessible via `.net`.
        if self._posterior is None or retrain_from_scratch_each_round:
            x_shape = x_shape_from_simulation(x)
            self._posterior = RatioBasedPosterior(
                method_family=self.__class__.__name__.lower(),
                neural_net=self._build_neural_net(theta, x),
                prior=self._prior,
                x_shape=x_shape,
                mcmc_method=self._mcmc_method,
                mcmc_parameters=self._mcmc_parameters,
            )

        # Copy MCMC init parameters for latest sample init
        if hasattr(proposal, "_mcmc_init_params"):
            self._posterior._mcmc_init_params = proposal._mcmc_init_params

        # Fit posterior using newly aggregated data set.
        self._train(
            num_atoms=num_atoms,
            training_batch_size=training_batch_size,
            learning_rate=learning_rate,
            validation_fraction=validation_fraction,
            stop_after_epochs=stop_after_epochs,
            max_num_epochs=max_num_epochs,
            clip_max_norm=clip_max_norm,
            exclude_invalid_x=exclude_invalid_x,
            discard_prior_samples=discard_prior_samples,
        )

        # Update description for progress bar.
        if self._show_round_summary:
            print(self._describe_round(self._round, self._summary))

        # Update tensorboard and summary dict.
        self._summarize(
            round_=self._round,
            x_o=self._posterior.default_x,
            theta_bank=theta,
            x_bank=x,
        )

        self._posterior._num_trained_rounds = self._round + 1

        return deepcopy(self._posterior)

    def _train(
        self,
        num_atoms: int,
        training_batch_size: int,
        learning_rate: float,
        validation_fraction: float,
        stop_after_epochs: int,
        max_num_epochs: int,
        clip_max_norm: Optional[float],
        exclude_invalid_x: bool,
        discard_prior_samples: bool,
    ) -> None:
        r"""
        Trains the neural classifier.

        Update the classifier weights by maximizing a Bernoulli likelihood which
        distinguishes between jointly distributed $(\theta, x)$ pairs and randomly
        chosen $(\theta, x)$ pairs.

        Uses performance on a held-out validation set as a terminating condition (early
        stopping).
        """

        # Starting index for the training set (1 = discard round-0 samples).
        start_idx = int(discard_prior_samples and self._round > 0)
        theta, x, _ = self._get_from_data_bank(start_idx, exclude_invalid_x)

        # Get total number of training examples.
        num_examples = len(theta)

        # Select random train and validation splits from (theta, x) pairs.
        permuted_indices = torch.randperm(num_examples)
        num_training_examples = int((1 - validation_fraction) * num_examples)
        num_validation_examples = num_examples - num_training_examples
        train_indices, val_indices = (
            permuted_indices[:num_training_examples],
            permuted_indices[num_training_examples:],
        )

        clipped_batch_size = min(training_batch_size, num_validation_examples)

        num_atoms = clamp_and_warn(
            "num_atoms", num_atoms, min_val=2, max_val=clipped_batch_size
        )

        # Dataset is shared for training and validation loaders.
        dataset = data.TensorDataset(theta, x)

        # Create neural net and validation loaders using a subset sampler.
        train_loader = data.DataLoader(
            dataset,
            batch_size=clipped_batch_size,
            drop_last=True,
            sampler=SubsetRandomSampler(train_indices),
        )
        val_loader = data.DataLoader(
            dataset,
            batch_size=clipped_batch_size,
            shuffle=False,
            drop_last=False,
            sampler=SubsetRandomSampler(val_indices),
        )

        optimizer = optim.Adam(
            list(self._posterior.net.parameters()), lr=learning_rate,
        )

        epoch, self._val_log_prob = 0, float("-Inf")

        while epoch <= max_num_epochs and not self._converged(epoch, stop_after_epochs):

            # Train for a single epoch.
            self._posterior.net.train()
            for batch in train_loader:
                optimizer.zero_grad()
                theta_batch, x_batch = (
                    batch[0].to(self._device),
                    batch[1].to(self._device),
                )
                loss = self._loss(theta_batch, x_batch, num_atoms)
                loss.backward()
                if clip_max_norm is not None:
                    clip_grad_norm_(
                        self._posterior.net.parameters(), max_norm=clip_max_norm,
                    )
                optimizer.step()

            epoch += 1

            # Calculate validation performance.
            self._posterior.net.eval()
            log_prob_sum = 0
            with torch.no_grad():
                for batch in val_loader:
                    theta_batch, x_batch = (
                        batch[0].to(self._device),
                        batch[1].to(self._device),
                    )
                    log_prob = self._loss(theta_batch, x_batch, num_atoms)
                    log_prob_sum -= log_prob.sum().item()
                self._val_log_prob = log_prob_sum / num_validation_examples

            self._maybe_show_progress(self._show_progress_bars, epoch)

        self._report_convergence_at_end(epoch, stop_after_epochs, max_num_epochs)

        # Update summary.
        self._summary["epochs"].append(epoch)
        self._summary["best_validation_log_probs"].append(self._best_val_log_prob)

    def _classifier_logits(self, theta: Tensor, x: Tensor, num_atoms: int) -> Tensor:
        """Return logits obtained through classifier forward pass.

        The logits are obtained from atomic sets of (theta,x) pairs.
        """
        batch_size = theta.shape[0]
        repeated_x = utils.repeat_rows(x, num_atoms)

        # Choose `1` or `num_atoms - 1` thetas from the rest of the batch for each x.
        probs = ones(batch_size, batch_size) * (1 - eye(batch_size)) / (batch_size - 1)

        choices = torch.multinomial(probs, num_samples=num_atoms - 1, replacement=False)

        contrasting_theta = theta[choices]

        atomic_theta = torch.cat((theta[:, None, :], contrasting_theta), dim=1).reshape(
            batch_size * num_atoms, -1
        )

        theta_and_x = torch.cat((atomic_theta, repeated_x), dim=1)

        return self._posterior.net(theta_and_x)

    @abstractmethod
    def _loss(self, theta: Tensor, x: Tensor, num_atoms: int) -> Tensor:
        raise NotImplementedError
