# Copyright 2022 The Plenoptix Team. 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.

"""
Semantic NeRF implementation.
"""
from __future__ import annotations

from typing import Dict, Tuple

import torch
from torch import nn

from nerfactory.cameras.rays import RayBundle
from nerfactory.configs import base as cfg
from nerfactory.datamanagers.structs import Semantics
from nerfactory.fields.modules.encoding import NeRFEncoding
from nerfactory.fields.modules.field_heads import FieldHeadNames
from nerfactory.fields.nerf_field import NeRFField
from nerfactory.fields.semantic_nerf_field import SemanticNerfField
from nerfactory.models.modules.scene_colliders import AABBBoxCollider
from nerfactory.models.vanilla_nerf import NeRFModel
from nerfactory.renderers.renderers import SemanticRenderer
from nerfactory.utils import misc


class SemanticNerfModel(NeRFModel):
    """Semantic-NeRF model

    Args:
        config: SemanticNeRF configuration to instantiate model
        semantics: additional semantics data info
    """

    def __init__(self, config: cfg.ModelConfig, semantics: Semantics, **kwargs) -> None:
        self.stuff_classes = semantics.stuff_classes
        self.stuff_colors = semantics.stuff_colors
        super().__init__(config=config, **kwargs)

    def populate_modules(self):
        """Set the fields and modules"""
        super().populate_modules()

        # setting up fields
        position_encoding = NeRFEncoding(
            in_dim=3, num_frequencies=10, min_freq_exp=0.0, max_freq_exp=8.0, include_input=True
        )
        direction_encoding = NeRFEncoding(
            in_dim=3, num_frequencies=4, min_freq_exp=0.0, max_freq_exp=4.0, include_input=True
        )
        self.field_coarse = NeRFField(position_encoding=position_encoding, direction_encoding=direction_encoding)

        num_semantic_classes = len(self.stuff_classes)
        self.field_fine = SemanticNerfField(
            num_semantic_classes, position_encoding=position_encoding, direction_encoding=direction_encoding
        )

        # renderers
        self.renderer_semantic = SemanticRenderer()

        # losses
        self.cross_entropy_loss = nn.CrossEntropyLoss(reduction="mean")

        # colliders
        if self.config.enable_collider:
            self.collider = AABBBoxCollider(scene_bounds=self.scene_bounds)

    def get_outputs(self, ray_bundle: RayBundle):
        # uniform sampling
        ray_samples_uniform = self.sampler_uniform(ray_bundle)

        # coarse field
        field_outputs_coarse = self.field_coarse.forward(ray_samples_uniform)
        weights_coarse = ray_samples_uniform.get_weights(field_outputs_coarse[FieldHeadNames.DENSITY])
        rgb_coarse = self.renderer_rgb(
            rgb=field_outputs_coarse[FieldHeadNames.RGB],
            weights=weights_coarse,
        )
        accumulation_coarse = self.renderer_accumulation(weights_coarse)
        depth_coarse = self.renderer_depth(weights_coarse, ray_samples_uniform)

        # pdf sampling
        ray_samples_pdf = self.sampler_pdf(ray_bundle, ray_samples_uniform, weights_coarse)

        # fine field
        field_outputs_fine = self.field_fine.forward(ray_samples_pdf)
        weights_fine = ray_samples_pdf.get_weights(field_outputs_fine[FieldHeadNames.DENSITY])
        rgb_fine = self.renderer_rgb(
            rgb=field_outputs_fine[FieldHeadNames.RGB],
            weights=weights_fine,
        )
        accumulation_fine = self.renderer_accumulation(weights_fine)
        depth_fine = self.renderer_depth(weights_fine, ray_samples_pdf)
        semantic_fine = self.renderer_semantic(field_outputs_fine[FieldHeadNames.SEMANTICS], weights=weights_fine)

        outputs = {
            "rgb_coarse": rgb_coarse,
            "rgb_fine": rgb_fine,
            "accumulation_coarse": accumulation_coarse,
            "accumulation_fine": accumulation_fine,
            "depth_coarse": depth_coarse,
            "depth_fine": depth_fine,
            "semantic_fine": semantic_fine,
        }
        return outputs

    def get_loss_dict(self, outputs, batch, metrics_dict=None):
        image = batch["image"].to(outputs["rgb_coarse"].device)
        rgb_loss_coarse = self.rgb_loss(image, outputs["rgb_coarse"])
        rgb_loss_fine = self.rgb_loss(image, outputs["rgb_fine"])
        semantic_logits = outputs["semantic_fine"]
        semantic_classes = batch["semantics"][..., 0].long()
        semantic_loss_fine = self.cross_entropy_loss(semantic_logits, semantic_classes)
        loss_dict = {
            "rgb_loss_coarse": rgb_loss_coarse,
            "rgb_loss_fine": rgb_loss_fine,
            "semantic_loss_fine": semantic_loss_fine,
        }
        loss_dict = misc.scale_dict(loss_dict, self.config.loss_coefficients)
        return loss_dict

    def get_image_metrics_and_images(
        self, outputs: Dict[str, torch.Tensor], batch: Dict[str, torch.Tensor]
    ) -> Tuple[Dict[str, float], Dict[str, torch.Tensor]]:
        metrics_dict, images_dict = super().get_image_metrics_and_images(outputs, batch)
        semantic_logits = outputs["semantic_fine"]
        semantic_labels = torch.argmax(torch.nn.functional.softmax(semantic_logits, dim=-1), dim=-1)  # type: ignore
        semantic_labels_image = self.stuff_colors[semantic_labels]

        mask = batch["mask"].repeat(1, 1, 3)

        images_dict["semantics"] = semantic_labels_image
        images_dict["mask"] = mask
        return metrics_dict, images_dict