# 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.

"""Classic NeRF field"""


from typing import Dict, Optional, Tuple

import torch
from torch import nn
from torchtyping import TensorType

from nerfactory.cameras.rays import RaySamples
from nerfactory.fields.base import Field
from nerfactory.fields.modules.encoding import Encoding, Identity
from nerfactory.fields.modules.field_heads import (
    DensityFieldHead,
    FieldHead,
    FieldHeadNames,
    RGBFieldHead,
)
from nerfactory.fields.modules.mlp import MLP
from nerfactory.fields.modules.spatial_distortions import SpatialDistortion


class NeRFField(Field):
    """NeRF Field

    Args:
        position_encoding: Position encoder.
        direction_encoding: Direction encoder.
        base_mlp_num_layers: Number of layers for base MLP.
        base_mlp_layer_width: Width of base MLP layers.
        head_mlp_num_layers: Number of layer for ourput head MLP.
        head_mlp_layer_width: Width of output head MLP layers.
        skip_connections: Where to add skip connection in base MLP.
        use_integrated_encoding: Used integrated samples as encoding input.
        spatial_distortion: Spatial distortion.
    """

    def __init__(
        self,
        position_encoding: Encoding = Identity(in_dim=3),
        direction_encoding: Encoding = Identity(in_dim=3),
        base_mlp_num_layers: int = 8,
        base_mlp_layer_width: int = 256,
        head_mlp_num_layers: int = 2,
        head_mlp_layer_width: int = 128,
        skip_connections: Tuple[int] = (4,),
        field_heads: Tuple[FieldHead] = (RGBFieldHead(),),
        use_integrated_encoding: bool = False,
        spatial_distortion: Optional[SpatialDistortion] = None,
    ) -> None:
        super().__init__()
        self.position_encoding = position_encoding
        self.direction_encoding = direction_encoding
        self.use_integrated_encoding = use_integrated_encoding
        self.spatial_distortion = spatial_distortion

        self.mlp_base = MLP(
            in_dim=self.position_encoding.get_out_dim(),
            num_layers=base_mlp_num_layers,
            layer_width=base_mlp_layer_width,
            skip_connections=skip_connections,
        )

        self.mlp_head = MLP(
            in_dim=self.mlp_base.get_out_dim() + self.direction_encoding.get_out_dim(),
            num_layers=head_mlp_num_layers,
            layer_width=head_mlp_layer_width,
        )

        self.field_output_density = DensityFieldHead(in_dim=self.mlp_base.get_out_dim())
        self.field_heads = nn.ModuleList(field_heads)
        for field_head in self.field_heads:
            field_head.set_in_dim(self.mlp_head.get_out_dim())  # type: ignore

    def get_density(self, ray_samples: RaySamples):
        if self.use_integrated_encoding:
            gaussian_samples = ray_samples.frustums.get_gaussian_blob()
            if self.spatial_distortion is not None:
                gaussian_samples = self.spatial_distortion(gaussian_samples)
            encoded_xyz = self.position_encoding(gaussian_samples.mean, covs=gaussian_samples.cov)
        else:
            positions = ray_samples.frustums.get_positions()
            if self.spatial_distortion is not None:
                positions = self.spatial_distortion(positions)
            encoded_xyz = self.position_encoding(positions)
        base_mlp_out = self.mlp_base(encoded_xyz)
        density = self.field_output_density(base_mlp_out)
        return density, base_mlp_out

    def get_outputs(
        self, ray_samples: RaySamples, density_embedding: Optional[TensorType] = None
    ) -> Dict[FieldHeadNames, TensorType]:
        outputs = {}
        for field_head in self.field_heads:
            encoded_dir = self.direction_encoding(ray_samples.frustums.directions)
            mlp_out = self.mlp_head(torch.cat([encoded_dir, density_embedding], dim=-1))  # type: ignore
            outputs[field_head.field_head_name] = field_head(mlp_out)
        return outputs