content: # Copyright (c) Meta Platforms, Inc. and affiliates.
  #
  # This source code is licensed under the Apache License, Version 2.0
  # found in the LICENSE file in the root directory of this source tree.
  
  from enum import Enum
  from functools import partial
  from typing import Optional, Tuple, Union
  
  import torch
  
  from .backbones import _make_dinov2_model
  from .depth import BNHead, DepthEncoderDecoder, DPTHead
  from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name, CenterPadding
  
  
  class Weights(Enum):
      NYU = "NYU"
      KITTI = "KITTI"
  
  
  def _get_depth_range(pretrained: bool, weights: Weights = Weights.NYU) -> Tuple[float, float]:
      if not pretrained:  # Default
          return (0.001, 10.0)
  
      # Pretrained, set according to the training dataset for the provided weights
      if weights == Weights.KITTI:
          return (0.001, 80.0)
  
      if weights == Weights.NYU:
          return (0.001, 10.0)
  
      return (0.001, 10.0)
  
  
  def _make_dinov2_linear_depth_head(
      *,
      embed_dim: int,
      layers: int,
      min_depth: float,
      max_depth: float,
      **kwargs,
  ):
      if layers not in (1, 4):
          raise AssertionError(f"Unsupported number of layers: {layers}")
  
      if layers == 1:
          in_index = [0]
      else:
          assert layers == 4
          in_index = [0, 1, 2, 3]
  
      return BNHead(
          classify=True,
          n_bins=256,
          bins_strategy="UD",
          norm_strategy="linear",
          upsample=4,
          in_channels=[embed_dim] * len(in_index),
          in_index=in_index,
          input_transform="resize_concat",
          channels=embed_dim * len(in_index) * 2,
          align_corners=False,
          min_depth=0.001,
          max_depth=80,
          loss_decode=(),
      )
  
  
  def _make_dinov2_linear_depther(
      *,
      arch_name: str = "vit_large",
      layers: int = 4,
      pretrained: bool = True,
      weights: Union[Weights, str] = Weights.NYU,
      depth_range: Optional[Tuple[float, float]] = None,
      **kwargs,
  ):
      if layers not in (1, 4):
          raise AssertionError(f"Unsupported number of layers: {layers}")
      if isinstance(weights, str):
          try:
              weights = Weights[weights]
          except KeyError:
              raise AssertionError(f"Unsupported weights: {weights}")
  
      if depth_range is None:
          depth_range = _get_depth_range(pretrained, weights)
      min_depth, max_depth = depth_range
  
      backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs)
  
      embed_dim = backbone.embed_dim
      patch_size = backbone.patch_size
      model_name = _make_dinov2_model_name(arch_name, patch_size)
      linear_depth_head = _make_dinov2_linear_depth_head(
          embed_dim=embed_dim,
          layers=layers,
          min_depth=min_depth,
          max_depth=max_depth,
      )
  
      layer_count = {
          "vit_small": 12,
          "vit_base": 12,
          "vit_large": 24,
          "vit_giant2": 40,
      }[arch_name]
  
      if layers == 4:
          out_index = {
              "vit_small": [2, 5, 8, 11],
              "vit_base": [2, 5, 8, 11],
              "vit_large": [4, 11, 17, 23],
              "vit_giant2": [9, 19, 29, 39],
          }[arch_name]
      else:
          assert layers == 1
          out_index = [layer_count - 1]
  
      model = DepthEncoderDecoder(backbone=backbone, decode_head=linear_depth_head)
      model.backbone.forward = partial(
          backbone.get_intermediate_layers,
          n=out_index,
          reshape=True,
          return_class_token=True,
          norm=False,
      )
      model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(patch_size)(x[0]))
  
      if pretrained:
          layers_str = str(layers) if layers == 4 else ""
          weights_str = weights.value.lower()
          url = _DINOV2_BASE_URL + f"/{model_name}/{model_name}_{weights_str}_linear{layers_str}_head.pth"
          checkpoint = torch.hub.load_state_dict_from_url(url, map_location="cpu")
          if "state_dict" in checkpoint:
              state_dict = checkpoint["state_dict"]
          model.load_state_dict(state_dict, strict=False)
  
      return model
  
  
  def dinov2_vits14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
      return _make_dinov2_linear_depther(
          arch_name="vit_small", layers=layers, pretrained=pretrained, weights=weights, **kwargs
      )
  
  
  def dinov2_vitb14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
      return _make_dinov2_linear_depther(
          arch_name="vit_base", layers=layers, pretrained=pretrained, weights=weights, **kwargs
      )
  
  
  def dinov2_vitl14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
      return _make_dinov2_linear_depther(
          arch_name="vit_large", layers=layers, pretrained=pretrained, weights=weights, **kwargs
      )
  
  
  def dinov2_vitg14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
      return _make_dinov2_linear_depther(
          arch_name="vit_giant2", layers=layers, ffn_layer="swiglufused", pretrained=pretrained, weights=weights, **kwargs
      )
  
  
  def _make_dinov2_dpt_depth_head(*, embed_dim: int, min_depth: float, max_depth: float):
      return DPTHead(
          in_channels=[embed_dim] * 4,
          channels=256,
          embed_dims=embed_dim,
          post_process_channels=[embed_dim // 2 ** (3 - i) for i in range(4)],
          readout_type="project",
          min_depth=min_depth,
          max_depth=max_depth,
          loss_decode=(),
      )
  
  
  def _make_dinov2_dpt_depther(
      *,
      arch_name: str = "vit_large",
      pretrained: bool = True,
      weights: Union[Weights, str] = Weights.NYU,
      depth_range: Optional[Tuple[float, float]] = None,
      **kwargs,
  ):
      if isinstance(weights, str):
          try:
              weights = Weights[weights]
          except KeyError:
              raise AssertionError(f"Unsupported weights: {weights}")
  
      if depth_range is None:
          depth_range = _get_depth_range(pretrained, weights)
      min_depth, max_depth = depth_range
  
      backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs)
  
      model_name = _make_dinov2_model_name(arch_name, backbone.patch_size)
      dpt_depth_head = _make_dinov2_dpt_depth_head(embed_dim=backbone.embed_dim, min_depth=min_depth, max_depth=max_depth)
  
      out_index = {
          "vit_small": [2, 5, 8, 11],
          "vit_base": [2, 5, 8, 11],
          "vit_large": [4, 11, 17, 23],
          "vit_giant2": [9, 19, 29, 39],
      }[arch_name]
  
      model = DepthEncoderDecoder(backbone=backbone, decode_head=dpt_depth_head)
      model.backbone.forward = partial(
          backbone.get_intermediate_layers,
          n=out_index,
          reshape=True,
          return_class_token=True,
          norm=False,
      )
      model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(backbone.patch_size)(x[0]))
  
      if pretrained:
          weights_str = weights.value.lower()
          url = _DINOV2_BASE_URL + f"/{model_name}/{model_name}_{weights_str}_dpt_head.pth"
          checkpoint = torch.hub.load_state_dict_from_url(url, map_location="cpu")
          if "state_dict" in checkpoint:
              state_dict = checkpoint["state_dict"]
          model.load_state_dict(state_dict, strict=False)
  
      return model
  
  
  def dinov2_vits14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
      return _make_dinov2_dpt_depther(arch_name="vit_small", pretrained=pretrained, weights=weights, **kwargs)
  
  
  def dinov2_vitb14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
      return _make_dinov2_dpt_depther(arch_name="vit_base", pretrained=pretrained, weights=weights, **kwargs)
  
  
  def dinov2_vitl14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
      return _make_dinov2_dpt_depther(arch_name="vit_large", pretrained=pretrained, weights=weights, **kwargs)
  
  
  def dinov2_vitg14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
      return _make_dinov2_dpt_depther(
          arch_name="vit_giant2", ffn_layer="swiglufused", pretrained=pretrained, weights=weights, **kwargs
      )
  