from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import range
import sklearn
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegressionCV
from sklearn.model_selection import KFold

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models

try:
    from torchvision.models.utils import load_state_dict_from_url
except ImportError:
    from torch.utils.model_zoo import load_url as load_state_dict_from_url

# Inception weights ported to Pytorch from
# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'


def generate_batch_factor_code(ground_truth_data, representation_function,
                               num_points, random_state, batch_size):
  """Sample a single training sample based on a mini-batch of ground-truth data.
  Args:
    ground_truth_data: GroundTruthData to be sampled from.
    representation_function: Function that takes observation as input and
      outputs a representation.
    num_points: Number of points to sample.
    random_state: Numpy random state used for randomness.
    batch_size: Batchsize to sample points.
  Returns:
    representations: Codes (num_codes, num_points)-np array.
    factors: Factors generating the codes (num_factors, num_points)-np array.
  """
  representations = None
  factors = None
  i = 0
  while i < num_points:
    num_points_iter = min(num_points - i, batch_size)
    current_factors, current_observations = \
        ground_truth_data.sample(num_points_iter, random_state)
    if i == 0:
      factors = current_factors
      representations = representation_function(current_observations)
    else:
      factors = np.vstack((factors, current_factors))
      representations = np.vstack((representations,
                                   representation_function(
                                       current_observations)))
    i += num_points_iter
  return np.transpose(representations), np.transpose(factors)


def obtain_representation(observations, representation_function, batch_size):
  """"Obtain representations from observations.
  Args:
    observations: Observations for which we compute the representation.
    representation_function: Function that takes observation as input and
      outputs a representation.
    batch_size: Batch size to compute the representation.
  Returns:
    representations: Codes (num_codes, num_points)-Numpy array.
  """
  representations = None
  num_points = observations.shape[0]
  i = 0
  while i < num_points:
    num_points_iter = min(num_points - i, batch_size)
    current_observations = observations[i:i + num_points_iter]
    if i == 0:
      representations = representation_function(current_observations)
    else:
      representations = np.vstack((representations,
                                   representation_function(
                                       current_observations)))
    i += num_points_iter
  return np.transpose(representations)


def discrete_mutual_info(mus, ys):
  """Compute discrete mutual information."""
  num_codes = mus.shape[0]
  num_factors = ys.shape[0]
  m = np.zeros([num_codes, num_factors])
  for i in range(num_codes):
    for j in range(num_factors):
      m[i, j] = sklearn.metrics.mutual_info_score(ys[j, :], mus[i, :])
  return m


def discrete_entropy(ys):
  """Compute discrete mutual information."""
  num_factors = ys.shape[0]
  h = np.zeros(num_factors)
  for j in range(num_factors):
    h[j] = sklearn.metrics.mutual_info_score(ys[j, :], ys[j, :])
  return h

def _histogram_discretize(target, num_bins):
  """Discretization based on histograms."""
  discretized = np.zeros_like(target)
  for i in range(target.shape[0]):
    discretized[i, :] = np.digitize(target[i, :], np.histogram(
        target[i, :], num_bins)[1][:-1])
  return discretized

def make_discretizer(target, num_bins = 20,
                     discretizer_fn = _histogram_discretize):
  """Wrapper that creates discretizers."""
  return discretizer_fn(target, num_bins)





def normalize_data(data, mean=None, stddev=None):
  if mean is None:
    mean = np.mean(data, axis=1)
  if stddev is None:
    stddev = np.std(data, axis=1)
  return (data - mean[:, np.newaxis]) / stddev[:, np.newaxis], mean, stddev



def make_predictor_fn(predictor_fn):
  """Wrapper that creates classifiers."""
  return predictor_fn


def logistic_regression_cv():
  """Logistic regression with 5 folds cross validation."""
  return LogisticRegressionCV(Cs=10, cv=KFold(n_splits=5))


def gradient_boosting_classifier():
  """Default gradient boosting classifier."""
  return GradientBoostingClassifier()


## Modified Inception Network for FID score
class InceptionV3(nn.Module):
  """Pretrained InceptionV3 network returning feature maps"""

  # Index of default block of inception to return,
  # corresponds to output of final average pooling
  DEFAULT_BLOCK_INDEX = 3

  # Maps feature dimensionality to their output blocks indices
  BLOCK_INDEX_BY_DIM = {
      64: 0,   # First max pooling features
      192: 1,  # Second max pooling featurs
      768: 2,  # Pre-aux classifier features
      2048: 3  # Final average pooling features
  }

  def __init__(self,
                 output_blocks=[DEFAULT_BLOCK_INDEX],
                 resize_input=True,
                 normalize_input=True,
                 requires_grad=False,
                 use_fid_inception=True):
    """Build pretrained InceptionV3
    Parameters
    ----------
    output_blocks : list of int
        Indices of blocks to return features of. Possible values are:
            - 0: corresponds to output of first max pooling
            - 1: corresponds to output of second max pooling
            - 2: corresponds to output which is fed to aux classifier
            - 3: corresponds to output of final average pooling
    resize_input : bool
        If true, bilinearly resizes input to width and height 299 before
        feeding input to model. As the network without fully connected
        layers is fully convolutional, it should be able to handle inputs
        of arbitrary size, so resizing might not be strictly needed
    normalize_input : bool
        If true, scales the input from range (0, 1) to the range the
        pretrained Inception network expects, namely (-1, 1)
    requires_grad : bool
        If true, parameters of the model require gradients. Possibly useful
        for finetuning the network
    use_fid_inception : bool
        If true, uses the pretrained Inception model used in Tensorflow's
        FID implementation. If false, uses the pretrained Inception model
        available in torchvision. The FID Inception model has different
        weights and a slightly different structure from torchvision's
        Inception model. If you want to compute FID scores, you are
        strongly advised to set this parameter to true to get comparable
        results.
    """
    super(InceptionV3, self).__init__()

    self.resize_input = resize_input
    self.normalize_input = normalize_input
    self.output_blocks = sorted(output_blocks)
    self.last_needed_block = max(output_blocks)

    assert self.last_needed_block <= 3, \
        'Last possible output block index is 3'

    self.blocks = nn.ModuleList()

    if use_fid_inception:
        inception = fid_inception_v3()
    else:
        inception = models.inception_v3(pretrained=True)

    # Block 0: input to maxpool1
    block0 = [
        inception.Conv2d_1a_3x3,
        inception.Conv2d_2a_3x3,
        inception.Conv2d_2b_3x3,
        nn.MaxPool2d(kernel_size=3, stride=2)
    ]
    self.blocks.append(nn.Sequential(*block0))

    # Block 1: maxpool1 to maxpool2
    if self.last_needed_block >= 1:
        block1 = [
            inception.Conv2d_3b_1x1,
            inception.Conv2d_4a_3x3,
            nn.MaxPool2d(kernel_size=3, stride=2)
        ]
        self.blocks.append(nn.Sequential(*block1))

    # Block 2: maxpool2 to aux classifier
    if self.last_needed_block >= 2:
        block2 = [
            inception.Mixed_5b,
            inception.Mixed_5c,
            inception.Mixed_5d,
            inception.Mixed_6a,
            inception.Mixed_6b,
            inception.Mixed_6c,
            inception.Mixed_6d,
            inception.Mixed_6e,
        ]
        self.blocks.append(nn.Sequential(*block2))

    # Block 3: aux classifier to final avgpool
    if self.last_needed_block >= 3:
        block3 = [
            inception.Mixed_7a,
            inception.Mixed_7b,
            inception.Mixed_7c,
            nn.AdaptiveAvgPool2d(output_size=(1, 1))
        ]
        self.blocks.append(nn.Sequential(*block3))

    for param in self.parameters():
        param.requires_grad = requires_grad

  def forward(self, inp):
    """Get Inception feature maps
    Parameters
    ----------
    inp : torch.autograd.Variable
        Input tensor of shape Bx3xHxW. Values are expected to be in
        range (0, 1)
    Returns
    -------
    List of torch.autograd.Variable, corresponding to the selected output
    block, sorted ascending by index
    """
    outp = []
    x = inp

    if self.resize_input:
        x = F.interpolate(x,
                          size=(299, 299),
                          mode='bilinear',
                          align_corners=False)

    if self.normalize_input:
        x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)

    for idx, block in enumerate(self.blocks):
        x = block(x)
        if idx in self.output_blocks:
            outp.append(x)

        if idx == self.last_needed_block:
            break

    return outp

class FIDInceptionA(models.inception.InceptionA):
  """InceptionA block patched for FID computation"""
  def __init__(self, in_channels, pool_features):
    super(FIDInceptionA, self).__init__(in_channels, pool_features)

  def forward(self, x):
    branch1x1 = self.branch1x1(x)

    branch5x5 = self.branch5x5_1(x)
    branch5x5 = self.branch5x5_2(branch5x5)

    branch3x3dbl = self.branch3x3dbl_1(x)
    branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
    branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)

    # Patch: Tensorflow's average pool does not use the padded zero's in
    # its average calculation
    branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
                               count_include_pad=False)
    branch_pool = self.branch_pool(branch_pool)

    outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
    return torch.cat(outputs, 1)


class FIDInceptionC(models.inception.InceptionC):
  """InceptionC block patched for FID computation"""
  def __init__(self, in_channels, channels_7x7):
    super(FIDInceptionC, self).__init__(in_channels, channels_7x7)

  def forward(self, x):
    branch1x1 = self.branch1x1(x)

    branch7x7 = self.branch7x7_1(x)
    branch7x7 = self.branch7x7_2(branch7x7)
    branch7x7 = self.branch7x7_3(branch7x7)

    branch7x7dbl = self.branch7x7dbl_1(x)
    branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
    branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
    branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
    branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)

    # Patch: Tensorflow's average pool does not use the padded zero's in
    # its average calculation
    branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
                               count_include_pad=False)
    branch_pool = self.branch_pool(branch_pool)

    outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
    return torch.cat(outputs, 1)


class FIDInceptionE_1(models.inception.InceptionE):
  """First InceptionE block patched for FID computation"""
  def __init__(self, in_channels):
    super(FIDInceptionE_1, self).__init__(in_channels)

  def forward(self, x):
    branch1x1 = self.branch1x1(x)

    branch3x3 = self.branch3x3_1(x)
    branch3x3 = [
        self.branch3x3_2a(branch3x3),
        self.branch3x3_2b(branch3x3),
    ]
    branch3x3 = torch.cat(branch3x3, 1)

    branch3x3dbl = self.branch3x3dbl_1(x)
    branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
    branch3x3dbl = [
        self.branch3x3dbl_3a(branch3x3dbl),
        self.branch3x3dbl_3b(branch3x3dbl),
    ]
    branch3x3dbl = torch.cat(branch3x3dbl, 1)

    # Patch: Tensorflow's average pool does not use the padded zero's in
    # its average calculation
    branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
                               count_include_pad=False)
    branch_pool = self.branch_pool(branch_pool)

    outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
    return torch.cat(outputs, 1)


class FIDInceptionE_2(models.inception.InceptionE):
  """Second InceptionE block patched for FID computation"""
  def __init__(self, in_channels):
    super(FIDInceptionE_2, self).__init__(in_channels)

  def forward(self, x):
    branch1x1 = self.branch1x1(x)

    branch3x3 = self.branch3x3_1(x)
    branch3x3 = [
        self.branch3x3_2a(branch3x3),
        self.branch3x3_2b(branch3x3),
    ]
    branch3x3 = torch.cat(branch3x3, 1)

    branch3x3dbl = self.branch3x3dbl_1(x)
    branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
    branch3x3dbl = [
        self.branch3x3dbl_3a(branch3x3dbl),
        self.branch3x3dbl_3b(branch3x3dbl),
    ]
    branch3x3dbl = torch.cat(branch3x3dbl, 1)

    # Patch: The FID Inception model uses max pooling instead of average
    # pooling. This is likely an error in this specific Inception
    # implementation, as other Inception models use average pooling here
    # (which matches the description in the paper).
    branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)
    branch_pool = self.branch_pool(branch_pool)

    outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
    return torch.cat(outputs, 1)

def fid_inception_v3():
  """Build pretrained Inception model for FID computation
  The Inception model for FID computation uses a different set of weights
  and has a slightly different structure than torchvision's Inception.
  This method first constructs torchvision's Inception and then patches the
  necessary parts that are different in the FID Inception model.
  """
  inception = models.inception_v3(num_classes=1008,
                                  aux_logits=False,
                                  pretrained=False)
  inception.Mixed_5b = FIDInceptionA(192, pool_features=32)
  inception.Mixed_5c = FIDInceptionA(256, pool_features=64)
  inception.Mixed_5d = FIDInceptionA(288, pool_features=64)
  inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128)
  inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160)
  inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160)
  inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192)
  inception.Mixed_7b = FIDInceptionE_1(1280)
  inception.Mixed_7c = FIDInceptionE_2(2048)

  state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True)
  inception.load_state_dict(state_dict)
  return inception



