# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, 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.
"""
 Utilities for PyTorch Transformer XL model. Directly adapted from https://github.com/kimiyoung/transformer-xl.
"""


import torch
import torch.nn as nn
import torch.nn.functional as F


# CUDA_MAJOR = int(torch.version.cuda.split('.')[0])
# CUDA_MINOR = int(torch.version.cuda.split('.')[1])


class ProjectedAdaptiveLogSoftmax(nn.Module):
    def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, keep_order=False):
        super().__init__()

        self.n_token = n_token
        self.d_embed = d_embed
        self.d_proj = d_proj

        self.cutoffs = cutoffs + [n_token]
        self.cutoff_ends = [0] + self.cutoffs
        self.div_val = div_val

        self.shortlist_size = self.cutoffs[0]
        self.n_clusters = len(self.cutoffs) - 1
        self.head_size = self.shortlist_size + self.n_clusters

        if self.n_clusters > 0:
            self.cluster_weight = nn.Parameter(torch.zeros(self.n_clusters, self.d_embed))
            self.cluster_bias = nn.Parameter(torch.zeros(self.n_clusters))

        self.out_layers = nn.ModuleList()
        self.out_projs = nn.ParameterList()

        if div_val == 1:
            for i in range(len(self.cutoffs)):
                if d_proj != d_embed:
                    self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_embed)))
                else:
                    self.out_projs.append(None)

            self.out_layers.append(nn.Linear(d_embed, n_token))
        else:
            for i in range(len(self.cutoffs)):
                l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
                d_emb_i = d_embed // (div_val ** i)

                self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_emb_i)))

                self.out_layers.append(nn.Linear(d_emb_i, r_idx - l_idx))

        self.keep_order = keep_order

    def _compute_logit(self, hidden, weight, bias, proj):
        if proj is None:
            logit = F.linear(hidden, weight, bias=bias)
        else:
            # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
            proj_hid = F.linear(hidden, proj.t().contiguous())
            logit = F.linear(proj_hid, weight, bias=bias)
            # else:
            #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
            #     if bias is not None:
            #         logit = logit + bias

        return logit

    def forward(self, hidden, labels=None, keep_order=False):
        """
        Params:
            hidden :: [len*bsz x d_proj]
            labels :: [len*bsz

        Return:
            if labels is None: out :: [len*bsz x n_tokens] log probabilities of tokens over the vocabulary else: out ::
            [(len-1)*bsz] Negative log likelihood. We could replace this implementation by the native PyTorch one if
            theirs had an option to set bias on all clusters in the native one. here:
            https://github.com/pytorch/pytorch/blob/dbe6a7a9ff1a364a8706bf5df58a1ca96d2fd9da/torch/nn/modules/adaptive.py#L138
        """

        if labels is not None:
            # Shift so that tokens < n predict n
            hidden = hidden[..., :-1, :].contiguous()
            labels = labels[..., 1:].contiguous()
            hidden = hidden.view(-1, hidden.size(-1))
            labels = labels.view(-1)
            if hidden.size(0) != labels.size(0):
                raise RuntimeError("Input and labels should have the same size " "in the batch dimension.")
        else:
            hidden = hidden.view(-1, hidden.size(-1))

        if self.n_clusters == 0:
            logit = self._compute_logit(hidden, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0])
            if labels is not None:
                out = -F.log_softmax(logit, dim=-1).gather(1, labels.unsqueeze(1)).squeeze(1)
            else:
                out = F.log_softmax(logit, dim=-1)
        else:
            # construct weights and biases
            weights, biases = [], []
            for i in range(len(self.cutoffs)):
                if self.div_val == 1:
                    l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
                    weight_i = self.out_layers[0].weight[l_idx:r_idx]
                    bias_i = self.out_layers[0].bias[l_idx:r_idx]
                else:
                    weight_i = self.out_layers[i].weight
                    bias_i = self.out_layers[i].bias

                if i == 0:
                    weight_i = torch.cat([weight_i, self.cluster_weight], dim=0)
                    bias_i = torch.cat([bias_i, self.cluster_bias], dim=0)

                weights.append(weight_i)
                biases.append(bias_i)

            head_weight, head_bias, head_proj = weights[0], biases[0], self.out_projs[0]

            head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj)
            head_logprob = F.log_softmax(head_logit, dim=1)

            if labels is None:
                out = hidden.new_empty((head_logit.size(0), self.n_token))
            else:
                out = torch.zeros_like(labels, dtype=hidden.dtype, device=hidden.device)

            offset = 0
            cutoff_values = [0] + self.cutoffs
            for i in range(len(cutoff_values) - 1):
                l_idx, r_idx = cutoff_values[i], cutoff_values[i + 1]

                if labels is not None:
                    mask_i = (labels >= l_idx) & (labels < r_idx)
                    indices_i = mask_i.nonzero().squeeze()

                    if indices_i.numel() == 0:
                        continue

                    target_i = labels.index_select(0, indices_i) - l_idx
                    head_logprob_i = head_logprob.index_select(0, indices_i)
                    hidden_i = hidden.index_select(0, indices_i)
                else:
                    hidden_i = hidden

                if i == 0:
                    if labels is not None:
                        logprob_i = head_logprob_i.gather(1, target_i[:, None]).squeeze(1)
                    else:
                        out[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]]
                else:
                    weight_i, bias_i, proj_i = weights[i], biases[i], self.out_projs[i]

                    tail_logit_i = self._compute_logit(hidden_i, weight_i, bias_i, proj_i)
                    tail_logprob_i = F.log_softmax(tail_logit_i, dim=1)
                    cluster_prob_idx = self.cutoffs[0] + i - 1  # No probability for the head cluster
                    if labels is not None:
                        logprob_i = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                            1, target_i[:, None]
                        ).squeeze(1)
                    else:
                        logprob_i = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
                        out[:, l_idx:r_idx] = logprob_i

                if labels is not None:
                    if (hasattr(self, "keep_order") and self.keep_order) or keep_order:
                        out.index_copy_(0, indices_i, -logprob_i)
                    else:
                        out[offset : offset + logprob_i.size(0)].copy_(-logprob_i)
                    offset += logprob_i.size(0)

        return out

    def log_prob(self, hidden):
        r"""
        Computes log probabilities for all :math:`n\_classes` From:
        https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/adaptive.p

        Args:
            hidden (Tensor): a minibatch of example

        Returns:
            log-probabilities of for each class :math:`c` in range :math:`0 <= c <= n\_classes`, where
            :math:`n\_classes` is a parameter passed to ``AdaptiveLogSoftmaxWithLoss`` constructor. Shape:

            - Input: :math:`(N, in\_features)`
            - Output: :math:`(N, n\_classes)`
        """
        if self.n_clusters == 0:
            logit = self._compute_logit(hidden, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0])
            return F.log_softmax(logit, dim=-1)
        else:
            # construct weights and biases
            weights, biases = [], []
            for i in range(len(self.cutoffs)):
                if self.div_val == 1:
                    l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
                    weight_i = self.out_layers[0].weight[l_idx:r_idx]
                    bias_i = self.out_layers[0].bias[l_idx:r_idx]
                else:
                    weight_i = self.out_layers[i].weight
                    bias_i = self.out_layers[i].bias

                if i == 0:
                    weight_i = torch.cat([weight_i, self.cluster_weight], dim=0)
                    bias_i = torch.cat([bias_i, self.cluster_bias], dim=0)

                weights.append(weight_i)
                biases.append(bias_i)

            head_weight, head_bias, head_proj = weights[0], biases[0], self.out_projs[0]
            head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj)

            out = hidden.new_empty((head_logit.size(0), self.n_token))
            head_logprob = F.log_softmax(head_logit, dim=1)

            cutoff_values = [0] + self.cutoffs
            for i in range(len(cutoff_values) - 1):
                start_idx, stop_idx = cutoff_values[i], cutoff_values[i + 1]

                if i == 0:
                    out[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]]
                else:
                    weight_i, bias_i, proj_i = weights[i], biases[i], self.out_projs[i]

                    tail_logit_i = self._compute_logit(hidden, weight_i, bias_i, proj_i)
                    tail_logprob_i = F.log_softmax(tail_logit_i, dim=1)

                    logprob_i = head_logprob[:, -i] + tail_logprob_i
                    out[:, start_idx, stop_idx] = logprob_i

            return out
