# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# 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.

import logging
import sys
import warnings

import cudf
import numpy as np
import pandas as pd
import torch
from packaging import version
from torch import optim
from torch.nn import (
    BatchNorm1d,
    Dropout,
    LeakyReLU,
    Linear,
    Module,
    ReLU,
    Sequential,
    functional,
)

from syngen.generator.tabular.base_tabular_generator import BaseTabularGenerator
from syngen.generator.tabular.data_transformer.ctgan_data_transformer import (
    CTGANDataTransformer,
)


class CTGANGenerator(BaseTabularGenerator):
    """Conditional Table GAN Generator.
    For more details about the process, please check the
    [Modeling Tabular data using Conditional GAN](https://arxiv.org/abs/1907.00503) paper.
    Adopted from: https://github.com/sdv-dev/CTGAN
    Args:

        embedding_dim (int): Size of the random sample passed to the Generator. Defaults to 128.
        generator_dim (tuple or list of ints): Size of the output samples for each one of the Residuals. A Residual Layer
            will be created for each one of the values provided. Defaults to (256, 256).
        discriminator_dim (tuple or list of ints): Size of the output samples for each one of the Discriminator Layers. A Linear Layer
            will be created for each one of the values provided. Defaults to (256, 256).
        generator_lr (float):Learning rate for the generator. Defaults to 2e-4.
        generator_decay (float):Generator weight decay for the Adam Optimizer. Defaults to 1e-6.
        discriminator_lr (float):Learning rate for the discriminator. Defaults to 2e-4.
        discriminator_decay (float):Discriminator weight decay for the Adam Optimizer. Defaults to 1e-6.
        batch_size (int):Number of data samples to process in each step.
        discriminator_steps (int):Number of discriminator updates to do for each generator update.
            From the WGAN paper: https://arxiv.org/abs/1701.07875. WGAN paper
            default is 5. Default used is 1 to match original CTGAN implementation.
        log_frequency (boolean):Whether to use log frequency of categorical levels in conditional
            sampling. Defaults to ``True``.
        verbose (boolean):Whether to have print statements for progress results. Defaults to ``False``.
        epochs (int):Number of training epochs. Defaults to 300.
        pac (int):Number of samples to group together when applying the discriminator.
            Defaults to 10.
        cuda (bool):Whether to attempt to use cuda for GPU computation.
            If this is False or CUDA is not available, CPU will be used.
            Defaults to ``True``.
    """

    def __init__(
        self,
        embedding_dim=128,
        generator_dim=(256, 256),
        discriminator_dim=(256, 256),
        generator_lr=2e-4,
        generator_decay=1e-6,
        discriminator_lr=2e-4,
        discriminator_decay=1e-6,
        batch_size=500,
        discriminator_steps=1,
        log_frequency=True,
        verbose=False,
        epochs=300,
        pac=10,
        cuda=True,
        **kwargs,
    ):

        assert batch_size % 2 == 0

        logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
        logger = logging.getLogger(__name__)
        self.log = logger
        self._embedding_dim = embedding_dim
        self._generator_dim = generator_dim
        self._discriminator_dim = discriminator_dim

        self._generator_lr = generator_lr
        self._generator_decay = generator_decay
        self._discriminator_lr = discriminator_lr
        self._discriminator_decay = discriminator_decay

        self._batch_size = int(batch_size)
        self._discriminator_steps = discriminator_steps
        self._log_frequency = log_frequency
        self._verbose = verbose
        self._epochs = epochs
        self.pac = pac

        if not cuda or not torch.cuda.is_available():
            device = "cpu"
        elif isinstance(cuda, str):
            device = cuda
        else:
            device = "cuda"

        self._device = torch.device(device)

        self._transformer = None
        self._data_sampler = None
        self._generator = None

    @staticmethod
    def _gumbel_softmax(logits, tau=1, hard=False, eps=1e-10, dim=-1):
        """Deals with the instability of the gumbel_softmax for older versions of torch.

        For more details about the issue:
        https://drive.google.com/file/d/1AA5wPfZ1kquaRtVruCd6BiYZGcDeNxyP/view?usp=sharing
        Parameters
        **********
            logits:
                […, num_features] unnormalized log probabilities
            tau:
                non-negative scalar temperature
            hard:
                if True, the returned samples will be discretized as one-hot vectors,
                but will be differentiated as if it is the soft sample in autograd
            dim (int):
                a dimension along which softmax will be computed. Default: -1.
        Returns
        *******
            Sampled tensor of same shape as logits from the Gumbel-Softmax distribution.
        """
        if version.parse(torch.__version__) < version.parse("1.2.0"):
            for i in range(10):
                transformed = functional.gumbel_softmax(
                    logits, tau=tau, hard=hard, eps=eps, dim=dim
                )
                if not torch.isnan(transformed).any():
                    return transformed
                raise ValueError("gumbel_softmax returning NaN.")

        return functional.gumbel_softmax(
            logits, tau=tau, hard=hard, eps=eps, dim=dim
        )

    def _apply_activate(self, data):
        """Apply proper activation function to the output of the generator."""
        data_t = []
        st = 0
        for column_info in self._transformer.output_info_list:
            for span_info in column_info:
                if span_info.activation_fn == "tanh":
                    ed = st + span_info.dim
                    data_t.append(torch.tanh(data[:, st:ed]))
                    st = ed
                elif span_info.activation_fn == "softmax":
                    ed = st + span_info.dim
                    transformed = self._gumbel_softmax(data[:, st:ed], tau=0.2)
                    data_t.append(transformed)
                    st = ed
                else:
                    assert 0

        return torch.cat(data_t, dim=1)

    def _cond_loss(self, data, c, m):
        """Compute the cross entropy loss on the fixed discrete column."""
        loss = []
        st = 0
        st_c = 0
        for column_info in self._transformer.output_info_list:
            for span_info in column_info:
                if (
                    len(column_info) != 1
                    or span_info.activation_fn != "softmax"
                ):
                    # not discrete column
                    st += span_info.dim
                else:
                    ed = st + span_info.dim
                    ed_c = st_c + span_info.dim
                    tmp = functional.cross_entropy(
                        data[:, st:ed],
                        torch.argmax(c[:, st_c:ed_c], dim=1),
                        reduction="none",
                    )
                    loss.append(tmp)
                    st = ed
                    st_c = ed_c

        loss = torch.stack(loss, dim=1)

        return (loss * m).sum() / data.size()[0]

    def _validate_discrete_columns(self, train_data, categorical_columns):
        """Check whether ``categorical_columns`` exists in ``train_data``.

        Args:

            train_data (numpy.ndarray or pandas.DataFrame):
                Training Data. It must be a 2-dimensional numpy array or a pandas.DataFrame.
            categorical_columns (list-like):
                List of discrete columns to be used to generate the Conditional
                Vector. If ``train_data`` is a Numpy array, this list should
                contain the integer indices of the columns. Otherwise, if it is
                a ``pandas.DataFrame``, this list should contain the column names.
        """
        if isinstance(train_data, (pd.DataFrame, cudf.DataFrame)):
            invalid_columns = set(categorical_columns) - set(
                train_data.columns
            )
        elif isinstance(train_data, np.ndarray):
            invalid_columns = []
            for column in categorical_columns:
                if column < 0 or column >= train_data.shape[1]:
                    invalid_columns.append(column)
        else:
            raise TypeError(
                "``train_data`` should be either pd.DataFrame or np.array."
            )

        if invalid_columns:
            raise ValueError(
                "Invalid columns found: {}".format(invalid_columns)
            )

    def fit(self, train_data, categorical_columns=tuple(), epochs=None):
        """Fit the CTGAN Synthesizer models to the training data.

        Args:

            train_data (numpy.ndarray or pandas.DataFrame):
                Training Data. It must be a 2-dimensional numpy array or a pandas.DataFrame.
            categorical_columns (list-like):
                List of discrete columns to be used to generate the Conditional
                Vector. If ``train_data`` is a Numpy array, this list should
                contain the integer indices of the columns. Otherwise, if it is
                a ``pandas.DataFrame``, this list should contain the column names.
        """
        self._validate_discrete_columns(train_data, categorical_columns)

        if epochs is None:
            epochs = self._epochs
        else:
            warnings.warn(
                (
                    "`epochs` argument in `fit` method has been deprecated and will be removed "
                    "in a future version. Please pass `epochs` to the constructor instead"
                ),
                DeprecationWarning,
            )

        self._transformer = CTGANDataTransformer()
        self._transformer.fit(train_data, categorical_columns)
        train_data = self._transformer.transform(train_data)

        self._data_sampler = DataSampler(
            train_data, self._transformer.output_info_list, self._log_frequency
        )

        data_dim = self._transformer.output_dimensions

        self._generator = Generator(
            self._embedding_dim + self._data_sampler.dim_cond_vec(),
            self._generator_dim,
            data_dim,
        ).to(self._device)

        discriminator = Discriminator(
            data_dim + self._data_sampler.dim_cond_vec(),
            self._discriminator_dim,
            pac=self.pac,
        ).to(self._device)

        optimizerG = optim.Adam(
            self._generator.parameters(),
            lr=self._generator_lr,
            betas=(0.5, 0.9),
            weight_decay=self._generator_decay,
        )

        optimizerD = optim.Adam(
            discriminator.parameters(),
            lr=self._discriminator_lr,
            betas=(0.5, 0.9),
            weight_decay=self._discriminator_decay,
        )
        mean = torch.zeros(
            self._batch_size, self._embedding_dim, device=self._device
        )
        std = mean + 1

        steps_per_epoch = max(len(train_data) // self._batch_size, 1)
        for i in range(epochs):
            for id_ in range(steps_per_epoch):

                for n in range(self._discriminator_steps):
                    fakez = torch.normal(mean=mean, std=std)

                    condvec = self._data_sampler.sample_condvec(
                        self._batch_size
                    )
                    if condvec is None:
                        c1, m1, col, opt = None, None, None, None
                        real = self._data_sampler.sample_data(
                            self._batch_size, col, opt
                        )
                    else:
                        c1, m1, col, opt = condvec
                        c1 = torch.from_numpy(c1).to(self._device)
                        m1 = torch.from_numpy(m1).to(self._device)
                        fakez = torch.cat([fakez, c1], dim=1)

                        perm = np.arange(self._batch_size)
                        np.random.shuffle(perm)
                        real = self._data_sampler.sample_data(
                            self._batch_size, col[perm], opt[perm]
                        )
                        c2 = c1[perm]

                    fake = self._generator(fakez)
                    fakeact = self._apply_activate(fake)

                    real = torch.from_numpy(real.astype("float32")).to(
                        self._device
                    )

                    if c1 is not None:
                        fake_cat = torch.cat([fakeact, c1], dim=1)
                        real_cat = torch.cat([real, c2], dim=1)
                    else:
                        real_cat = real
                        fake_cat = fakeact

                    y_fake = discriminator(fake_cat)
                    y_real = discriminator(real_cat)

                    pen = discriminator.calc_gradient_penalty(
                        real_cat, fake_cat, self._device, self.pac
                    )
                    loss_d = -(torch.mean(y_real) - torch.mean(y_fake))

                    optimizerD.zero_grad()
                    pen.backward(retain_graph=True)
                    loss_d.backward()
                    optimizerD.step()

                fakez = torch.normal(mean=mean, std=std)
                condvec = self._data_sampler.sample_condvec(self._batch_size)

                if condvec is None:
                    c1, m1, col, opt = None, None, None, None
                else:
                    c1, m1, col, opt = condvec
                    c1 = torch.from_numpy(c1).to(self._device)
                    m1 = torch.from_numpy(m1).to(self._device)
                    fakez = torch.cat([fakez, c1], dim=1)

                fake = self._generator(fakez)
                fakeact = self._apply_activate(fake)

                if c1 is not None:
                    y_fake = discriminator(torch.cat([fakeact, c1], dim=1))
                else:
                    y_fake = discriminator(fakeact)

                if condvec is None:
                    cross_entropy = 0
                else:
                    cross_entropy = self._cond_loss(fake, c1, m1)

                loss_g = -torch.mean(y_fake) + cross_entropy

                optimizerG.zero_grad()
                loss_g.backward()
                optimizerG.step()

            if self._verbose:
                self.log.info(
                    f"Epoch {i + 1}, Loss G: {loss_g.detach().cpu(): .4f}, "
                    f"Loss D: {loss_d.detach().cpu(): .4f}"
                )

    def sample(self, n, condition_column=None, condition_value=None):
        """Sample data similar to the training data.

        Choosing a condition_column and condition_value will increase the probability of the
        discrete condition_value happening in the condition_column.
        Args:
            n (int):
                Number of rows to sample.
            condition_column (string):
                Name of a discrete column.
            condition_value (string):
                Name of the category in the condition_column which we wish to increase the
                probability of happening.
        Returns:
            numpy.ndarray or pandas.DataFrame
        """
        if condition_column is not None and condition_value is not None:
            condition_info = self._transformer.convert_column_name_value_to_id(
                condition_column, condition_value
            )
            global_condition_vec = self._data_sampler.generate_cond_from_condition_column_info(
                condition_info, self._batch_size
            )
        else:
            global_condition_vec = None

        steps = n // self._batch_size + 1
        data = []
        for i in range(steps):
            mean = torch.zeros(self._batch_size, self._embedding_dim)
            std = mean + 1
            fakez = torch.normal(mean=mean, std=std).to(self._device)

            if global_condition_vec is not None:
                condvec = global_condition_vec.copy()
            else:
                condvec = self._data_sampler.sample_original_condvec(
                    self._batch_size
                )

            if condvec is not None:
                c1 = condvec
                c1 = torch.from_numpy(c1).to(self._device)
                fakez = torch.cat([fakez, c1], dim=1)

            fake = self._generator(fakez)
            fakeact = self._apply_activate(fake)
            data.append(fakeact.detach().cpu().numpy())

        data = np.concatenate(data, axis=0)
        data = data[:n]

        return self._transformer.inverse_transform(data)

    def set_device(self, device):
        self._device = device
        if self._generator is not None:
            self._generator.to(self._device)

    @staticmethod
    def add_args(parser):
        parser.add_argument("--generator-lr", type=float, default=2e-4)
        parser.add_argument("--generator-decay", type=float, default=1e-6)
        parser.add_argument("--discriminator-lr", type=float, default=2e-4)
        parser.add_argument("--discriminator-decay", type=float, default=1e-6)
        parser.add_argument("--discriminator-steps", type=int, default=1)
        parser.add_argument("--pac", type=int, default=10)
        return parser


class Discriminator(Module):
    def __init__(self, input_dim, discriminator_dim, pac=10):
        super(Discriminator, self).__init__()
        dim = input_dim * pac
        self.pac = pac
        self.pacdim = dim
        seq = []
        for item in list(discriminator_dim):
            seq += [Linear(dim, item), LeakyReLU(0.2), Dropout(0.5)]
            dim = item

        seq += [Linear(dim, 1)]
        self.seq = Sequential(*seq)

    def calc_gradient_penalty(
        self, real_data, fake_data, device="cpu", pac=10, lambda_=10
    ):
        alpha = torch.rand(real_data.size(0) // pac, 1, 1, device=device)
        alpha = alpha.repeat(1, pac, real_data.size(1))
        alpha = alpha.view(-1, real_data.size(1))

        interpolates = alpha * real_data + ((1 - alpha) * fake_data)

        disc_interpolates = self(interpolates)

        gradients = torch.autograd.grad(
            outputs=disc_interpolates,
            inputs=interpolates,
            grad_outputs=torch.ones(disc_interpolates.size(), device=device),
            create_graph=True,
            retain_graph=True,
            only_inputs=True,
        )[0]

        gradient_penalty = (
            (gradients.view(-1, pac * real_data.size(1)).norm(2, dim=1) - 1)
            ** 2
        ).mean() * lambda_

        return gradient_penalty

    def forward(self, input):
        assert input.size()[0] % self.pac == 0, f'generator batch size ({input.size()[0]}) ' \
                                                f'should be divisible by pac ({self.pac})'
        return self.seq(input.view(-1, self.pacdim))


class Residual(Module):
    def __init__(self, i, o):
        super(Residual, self).__init__()
        self.fc = Linear(i, o)
        self.bn = BatchNorm1d(o)
        self.relu = ReLU()

    def forward(self, input):
        out = self.fc(input)
        out = self.bn(out)
        out = self.relu(out)
        return torch.cat([out, input], dim=1)


class Generator(Module):
    def __init__(self, embedding_dim, generator_dim, data_dim):
        super(Generator, self).__init__()
        dim = embedding_dim
        seq = []
        for item in list(generator_dim):
            seq += [Residual(dim, item)]
            dim += item
        seq.append(Linear(dim, data_dim))
        self.seq = Sequential(*seq)

    def forward(self, input):
        data = self.seq(input)
        return data


class DataSampler(object):
    """DataSampler samples the conditional vector and corresponding data for CTGAN."""

    def __init__(self, data, output_info, log_frequency):
        self._data = data

        def is_discrete_column(column_info):
            return (
                len(column_info) == 1
                and column_info[0].activation_fn == "softmax"
            )

        n_discrete_columns = sum(
            [
                1
                for column_info in output_info
                if is_discrete_column(column_info)
            ]
        )

        self._discrete_column_matrix_st = np.zeros(
            n_discrete_columns, dtype="int32"
        )

        # Store the row id for each category in each discrete column.
        # For example _rid_by_cat_cols[a][b] is a list of all rows with the
        # a-th discrete column equal value b.
        self._rid_by_cat_cols = []

        # Compute _rid_by_cat_cols
        st = 0
        for column_info in output_info:
            if is_discrete_column(column_info):
                span_info = column_info[0]
                ed = st + span_info.dim

                rid_by_cat = []
                for j in range(span_info.dim):
                    rid_by_cat.append(np.nonzero(data[:, st + j])[0])
                self._rid_by_cat_cols.append(rid_by_cat)
                st = ed
            else:
                st += sum([span_info.dim for span_info in column_info])
        assert st == data.shape[1]

        # Prepare an interval matrix for efficiently sample conditional vector
        max_category = max(
            [
                column_info[0].dim
                for column_info in output_info
                if is_discrete_column(column_info)
            ],
            default=0,
        )

        self._discrete_column_cond_st = np.zeros(
            n_discrete_columns, dtype="int32"
        )
        self._discrete_column_n_category = np.zeros(
            n_discrete_columns, dtype="int32"
        )
        self._discrete_column_category_prob = np.zeros(
            (n_discrete_columns, max_category)
        )
        self._n_discrete_columns = n_discrete_columns
        self._n_categories = sum(
            [
                column_info[0].dim
                for column_info in output_info
                if is_discrete_column(column_info)
            ]
        )

        st = 0
        current_id = 0
        current_cond_st = 0
        for column_info in output_info:
            if is_discrete_column(column_info):
                span_info = column_info[0]
                ed = st + span_info.dim
                category_freq = np.sum(data[:, st:ed], axis=0)
                if log_frequency:
                    category_freq = np.log(category_freq + 1)
                category_prob = category_freq / np.sum(category_freq)
                self._discrete_column_category_prob[
                    current_id, : span_info.dim
                ] = category_prob
                self._discrete_column_cond_st[current_id] = current_cond_st
                self._discrete_column_n_category[current_id] = span_info.dim
                current_cond_st += span_info.dim
                current_id += 1
                st = ed
            else:
                st += sum([span_info.dim for span_info in column_info])

    def _random_choice_prob_index(self, discrete_column_id):
        probs = self._discrete_column_category_prob[discrete_column_id]
        r = np.expand_dims(np.random.rand(probs.shape[0]), axis=1)
        return (probs.cumsum(axis=1) > r).argmax(axis=1)

    def sample_condvec(self, batch):
        """Generate the conditional vector for training.

        Returns:
            cond (batch x #categories):
                The conditional vector.
            mask (batch x #discrete columns):
                A one-hot vector indicating the selected discrete column.
            discrete column id (batch):
                Integer representation of mask.
            category_id_in_col (batch):
                Selected category in the selected discrete column.
        """
        if self._n_discrete_columns == 0:
            return None

        discrete_column_id = np.random.choice(
            np.arange(self._n_discrete_columns), batch
        )

        cond = np.zeros((batch, self._n_categories), dtype="float32")
        mask = np.zeros((batch, self._n_discrete_columns), dtype="float32")
        mask[np.arange(batch), discrete_column_id] = 1
        category_id_in_col = self._random_choice_prob_index(discrete_column_id)
        category_id = (
            self._discrete_column_cond_st[discrete_column_id]
            + category_id_in_col
        )
        cond[np.arange(batch), category_id] = 1

        return cond, mask, discrete_column_id, category_id_in_col

    def sample_original_condvec(self, batch):
        """Generate the conditional vector for generation use original frequency."""
        if self._n_discrete_columns == 0:
            return None

        cond = np.zeros((batch, self._n_categories), dtype="float32")

        for i in range(batch):
            row_idx = np.random.randint(0, len(self._data))
            col_idx = np.random.randint(0, self._n_discrete_columns)
            matrix_st = self._discrete_column_matrix_st[col_idx]
            matrix_ed = matrix_st + self._discrete_column_n_category[col_idx]
            pick = np.argmax(self._data[row_idx, matrix_st:matrix_ed])
            cond[i, pick + self._discrete_column_cond_st[col_idx]] = 1

        return cond

    def sample_data(self, n, col, opt):
        """Sample data from original training data satisfying the sampled conditional vector.

        Returns:
            n rows of matrix data.
        """
        if col is None:
            idx = np.random.randint(len(self._data), size=n)
            return self._data[idx]

        idx = []
        for c, o in zip(col, opt):
            idx.append(np.random.choice(self._rid_by_cat_cols[c][o]))

        return self._data[idx]

    def dim_cond_vec(self):
        return self._n_categories

    def generate_cond_from_condition_column_info(self, condition_info, batch):
        vec = np.zeros((batch, self._n_categories), dtype="float32")
        vec_id = (
            self._discrete_column_matrix_st[
                condition_info["discrete_column_id"]
            ]
            + condition_info["value_id"]
        )
        vec[:, vec_id] = 1
        return vec
