# Copyright (c) 2025, 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.

# This file contains code artifacts adapted from the original implementation:
# https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/loss.py

import torch
import torch.nn.functional as F


def neighbour_exchange(from_rank, to_rank, tensor, group=None):
    tensor_recv = torch.zeros_like(tensor)
    send_op = torch.distributed.P2POp(
        torch.distributed.isend,
        tensor,
        to_rank,
        group=group,
    )
    recv_op = torch.distributed.P2POp(
        torch.distributed.irecv,
        tensor_recv,
        from_rank,
        group=group,
    )
    reqs = torch.distributed.batch_isend_irecv([send_op, recv_op])
    for req in reqs:
        req.wait()
    return tensor_recv


def neighbour_exchange_bidir(left_rank, right_rank, tensor_to_left, tensor_to_right, group=None):
    tensor_from_left = torch.zeros_like(tensor_to_right)
    tensor_from_right = torch.zeros_like(tensor_to_left)
    send_op_left = torch.distributed.P2POp(
        torch.distributed.isend,
        tensor_to_left,
        left_rank,
        group=group,
    )
    send_op_right = torch.distributed.P2POp(
        torch.distributed.isend,
        tensor_to_right,
        right_rank,
        group=group,
    )
    recv_op_left = torch.distributed.P2POp(
        torch.distributed.irecv,
        tensor_from_left,
        left_rank,
        group=group,
    )
    recv_op_right = torch.distributed.P2POp(
        torch.distributed.irecv,
        tensor_from_right,
        right_rank,
        group=group,
    )
    reqs = torch.distributed.batch_isend_irecv([send_op_right, send_op_left, recv_op_right, recv_op_left])
    for req in reqs:
        req.wait()
    return tensor_from_right, tensor_from_left


class NeighbourExchange(torch.autograd.Function):
    @staticmethod
    def forward(ctx, from_rank, to_rank, group, tensor):
        ctx.group = group
        ctx.from_rank = from_rank
        ctx.to_rank = to_rank
        return neighbour_exchange(from_rank, to_rank, tensor, group=group)

    @staticmethod
    def backward(ctx, grad_output):
        return (None, None, None) + (NeighbourExchange.apply(ctx.to_rank, ctx.from_rank, ctx.group, grad_output),)


def neighbour_exchange_with_grad(from_rank, to_rank, tensor, group=None):
    return NeighbourExchange.apply(from_rank, to_rank, group, tensor)


class NeighbourExchangeBidir(torch.autograd.Function):
    @staticmethod
    def forward(ctx, left_rank, right_rank, group, tensor_to_left, tensor_to_right):
        ctx.group = group
        ctx.left_rank = left_rank
        ctx.right_rank = right_rank
        return neighbour_exchange_bidir(left_rank, right_rank, tensor_to_left, tensor_to_right, group=group)

    @staticmethod
    def backward(ctx, *grad_outputs):
        return (None, None, None) + NeighbourExchangeBidir.apply(
            ctx.right_rank, ctx.left_rank, ctx.group, *grad_outputs
        )


def neighbour_exchange_bidir_with_grad(left_rank, right_rank, tensor_to_left, tensor_to_right, group=None):
    return NeighbourExchangeBidir.apply(left_rank, right_rank, group, tensor_to_left, tensor_to_right)


class SigLipLoss(torch.nn.Module):
    """Sigmoid Loss for Language Image Pre-Training (SigLIP) - https://arxiv.org/abs/2303.15343

    @article{zhai2023sigmoid,
      title={Sigmoid loss for language image pre-training},
      author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
      journal={arXiv preprint arXiv:2303.15343},
      year={2023}
    }
    """

    def __init__(
        self,
        cache_labels=False,
        rank=0,
        world_size=1,
        group=None,
        bidir=True,
    ):
        super().__init__()
        self.cache_labels = cache_labels
        self.rank = rank
        self.world_size = world_size
        self.group = group
        self.bidir = bidir

    def get_ground_truth(self, device, dtype, num_logits, negative_only=False) -> torch.Tensor:
        labels = -torch.ones((num_logits, num_logits), device=device, dtype=dtype)
        if not negative_only:
            labels = 2 * torch.eye(num_logits, device=device, dtype=dtype) + labels
        return labels

    def get_logits(self, image_features, text_features, logit_scale, logit_bias=None):
        logits = logit_scale * image_features @ text_features.T
        if logit_bias is not None:
            logits += logit_bias
        return logits

    def _loss(self, image_features, text_features, logit_scale, logit_bias=None, negative_only=False):
        logits = self.get_logits(image_features, text_features, logit_scale, logit_bias)
        labels = self.get_ground_truth(
            image_features.device,
            image_features.dtype,
            image_features.shape[0],
            negative_only=negative_only,
        )
        loss = -F.logsigmoid(labels * logits).sum() / image_features.shape[0]
        return loss

    def forward(
        self,
        output_tensor,
    ):
        image_features, text_features, logit_scale, logit_bias = output_tensor
        loss = self._loss(image_features, text_features, logit_scale, logit_bias)

        if self.world_size > 1:
            # exchange text features w/ neighbour world_size - 1 times
            right_rank = (self.rank + 1) % self.world_size
            left_rank = (self.rank - 1 + self.world_size) % self.world_size
            if self.bidir:
                text_features_to_right = text_features_to_left = text_features
                num_bidir, remainder = divmod(self.world_size - 1, 2)
                for i in range(num_bidir):
                    text_features_recv = neighbour_exchange_bidir_with_grad(
                        left_rank,
                        right_rank,
                        text_features_to_left,
                        text_features_to_right,
                        group=self.group,
                    )

                    for f in text_features_recv:
                        loss += self._loss(
                            image_features,
                            f,
                            logit_scale,
                            logit_bias,
                            negative_only=True,
                        )
                    text_features_to_left, text_features_to_right = text_features_recv

                if remainder:
                    text_features_recv = neighbour_exchange_with_grad(
                        left_rank, right_rank, text_features_to_right, group=self.group
                    )

                    loss += self._loss(
                        image_features,
                        text_features_recv,
                        logit_scale,
                        logit_bias,
                        negative_only=True,
                    )
            else:
                text_features_to_right = text_features
                for i in range(self.world_size - 1):
                    text_features_from_left = neighbour_exchange_with_grad(
                        left_rank, right_rank, text_features_to_right, group=self.group
                    )

                    loss += self._loss(
                        image_features,
                        text_features_from_left,
                        logit_scale,
                        logit_bias,
                        negative_only=True,
                    )
                    text_features_to_right = text_features_from_left
        return loss, {"loss": loss}