import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Variable

import pdb

class Distribution(nn.Module):
    """Base class for all distribution objects."""

    def forward(self, *args):
        raise RuntimeError('Forward method cannot be called for a Distribution object.')

    def log_prob(self, inputs, context=None):
        """Calculate log probability under the distribution.

        Args:
            inputs: Tensor, input variables.
            context: Tensor or None, conditioning variables. If a Tensor, it must have the same
                number or rows as the inputs. If None, the context is ignored.

        Returns:
            A Tensor of shape [input_size], the log probability of the inputs given the context.
        """
        inputs = torch.as_tensor(inputs)
        if context is not None:
            context = torch.as_tensor(context)
            if inputs.shape[0] != context.shape[0]:
                raise ValueError('Number of input items must be equal to number of context items.')
        return self._log_prob(inputs, context)

    def _log_prob(self, inputs, context):
        raise NotImplementedError()

    def sample(self, num_samples, context=None, batch_size=None):
        """Generates samples from the distribution. Samples can be generated in batches.

        Args:
            num_samples: int, number of samples to generate.
            context: Tensor or None, conditioning variables. If None, the context is ignored.
            batch_size: int or None, number of samples per batch. If None, all samples are generated
                in one batch.

        Returns:
            A Tensor containing the samples, with shape [num_samples, ...] if context is None, or
            [context_size, num_samples, ...] if context is given.
        """
        if not utils.is_positive_int(num_samples):
            raise TypeError('Number of samples must be a positive integer.')

        if context is not None:
            context = torch.as_tensor(context)

        if batch_size is None:
            return self._sample(num_samples, context)

        else:
            if not utils.is_positive_int(batch_size):
                raise TypeError('Batch size must be a positive integer.')

            num_batches = num_samples // batch_size
            num_leftover = num_samples % batch_size
            samples = [self._sample(batch_size, context) for _ in range(num_batches)]
            if num_leftover > 0:
                samples.append(self._sample(num_leftover, context))
            return torch.cat(samples, dim=0)

    def _sample(self, num_samples, context):
        raise NotImplementedError()

    def sample_and_log_prob(self, num_samples, context=None):
        """Generates samples from the distribution together with their log probability.

        Args:
            num_samples: int, number of samples to generate.
            context: Tensor or None, conditioning variables. If None, the context is ignored.

        Returns:
            A tuple of:
                * A Tensor containing the samples, with shape [num_samples, ...] if context is None,
                  or [context_size, num_samples, ...] if context is given.
                * A Tensor containing the log probabilities of the samples, with shape
                  [num_samples, ...] if context is None, or [context_size, num_samples, ...] if
                  context is given.
        """
        samples = self.sample(num_samples, context=context)

        if context is not None:
            # Merge the context dimension with sample dimension in order to call log_prob.
            samples = utils.merge_leading_dims(samples, num_dims=2)
            context = utils.repeat_rows(context, num_reps=num_samples)
            assert samples.shape[0] == context.shape[0]

        log_prob = self.log_prob(samples, context=context)

        if context is not None:
            # Split the context dimension from sample dimension.
            samples = utils.split_leading_dim(samples, shape=[-1, num_samples])
            log_prob = utils.split_leading_dim(log_prob, shape=[-1, num_samples])

        return samples, log_prob

    def mean(self, context=None):
        if context is not None:
            context = torch.as_tensor(context)
        return self._mean(context)

    def _mean(self, context):
        raise NoMeanException()


class SplineFlow(Distribution):
    """Base class for all flow objects."""

    def __init__(self, transform):
        """Constructor.

        Args:
            transform: A `Transform` object, it transforms data into noise.
        """
        super().__init__()
        self._transform = transform

    def forward(self, inputs, context=None):
        noise, logabsdet = self._transform(inputs, context=context)
        return noise, logabsdet


if __name__ == "__main__":
    print("DONE.")
