"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import argparse
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from sapiens_transformers import DepthAnythingConfig, DepthAnythingForDepthEstimation, Dinov2Config, DPTImageProcessor
from sapiens_transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def get_dpt_config(model_name):
    if "small" in model_name:
        out_indices = [3, 6, 9, 12] if "v2" in model_name else [9, 10, 11, 12]
        backbone_config = Dinov2Config.from_pretrained("facebook/dinov2-small", out_indices=out_indices, apply_layernorm=True, reshape_hidden_states=False)
        fusion_hidden_size = 64
        neck_hidden_sizes = [48, 96, 192, 384]
    elif "base" in model_name:
        out_indices = [3, 6, 9, 12] if "v2" in model_name else [9, 10, 11, 12]
        backbone_config = Dinov2Config.from_pretrained("facebook/dinov2-base", out_indices=out_indices, apply_layernorm=True, reshape_hidden_states=False)
        fusion_hidden_size = 128
        neck_hidden_sizes = [96, 192, 384, 768]
    elif "large" in model_name:
        out_indices = [5, 12, 18, 24] if "v2" in model_name else [21, 22, 23, 24]
        backbone_config = Dinov2Config.from_pretrained("facebook/dinov2-large", out_indices=out_indices, apply_layernorm=True, reshape_hidden_states=False)
        fusion_hidden_size = 256
        neck_hidden_sizes = [256, 512, 1024, 1024]
    else: raise NotImplementedError(f"Model not supported: {model_name}")
    if "metric" in model_name:
        depth_estimation_type = "metric"
        max_depth = 20 if "indoor" in model_name else 80
    else:
        depth_estimation_type = "relative"
        max_depth = None
    config = DepthAnythingConfig(reassemble_hidden_size=backbone_config.hidden_size, patch_size=backbone_config.patch_size, backbone_config=backbone_config, fusion_hidden_size=fusion_hidden_size,
    neck_hidden_sizes=neck_hidden_sizes, depth_estimation_type=depth_estimation_type, max_depth=max_depth)
    return config
def create_rename_keys(config):
    rename_keys = []
    rename_keys.append(("pretrained.cls_token", "backbone.embeddings.cls_token"))
    rename_keys.append(("pretrained.mask_token", "backbone.embeddings.mask_token"))
    rename_keys.append(("pretrained.pos_embed", "backbone.embeddings.position_embeddings"))
    rename_keys.append(("pretrained.patch_embed.proj.weight", "backbone.embeddings.patch_embeddings.projection.weight"))
    rename_keys.append(("pretrained.patch_embed.proj.bias", "backbone.embeddings.patch_embeddings.projection.bias"))
    for i in range(config.backbone_config.num_hidden_layers):
        rename_keys.append((f"pretrained.blocks.{i}.ls1.gamma", f"backbone.encoder.layer.{i}.layer_scale1.lambda1"))
        rename_keys.append((f"pretrained.blocks.{i}.ls2.gamma", f"backbone.encoder.layer.{i}.layer_scale2.lambda1"))
        rename_keys.append((f"pretrained.blocks.{i}.norm1.weight", f"backbone.encoder.layer.{i}.norm1.weight"))
        rename_keys.append((f"pretrained.blocks.{i}.norm1.bias", f"backbone.encoder.layer.{i}.norm1.bias"))
        rename_keys.append((f"pretrained.blocks.{i}.norm2.weight", f"backbone.encoder.layer.{i}.norm2.weight"))
        rename_keys.append((f"pretrained.blocks.{i}.norm2.bias", f"backbone.encoder.layer.{i}.norm2.bias"))
        rename_keys.append((f"pretrained.blocks.{i}.mlp.fc1.weight", f"backbone.encoder.layer.{i}.mlp.fc1.weight"))
        rename_keys.append((f"pretrained.blocks.{i}.mlp.fc1.bias", f"backbone.encoder.layer.{i}.mlp.fc1.bias"))
        rename_keys.append((f"pretrained.blocks.{i}.mlp.fc2.weight", f"backbone.encoder.layer.{i}.mlp.fc2.weight"))
        rename_keys.append((f"pretrained.blocks.{i}.mlp.fc2.bias", f"backbone.encoder.layer.{i}.mlp.fc2.bias"))
        rename_keys.append((f"pretrained.blocks.{i}.attn.proj.weight", f"backbone.encoder.layer.{i}.attention.output.dense.weight"))
        rename_keys.append((f"pretrained.blocks.{i}.attn.proj.bias", f"backbone.encoder.layer.{i}.attention.output.dense.bias"))
    rename_keys.append(("pretrained.norm.weight", "backbone.layernorm.weight"))
    rename_keys.append(("pretrained.norm.bias", "backbone.layernorm.bias"))
    for i in range(4):
        rename_keys.append((f"depth_head.projects.{i}.weight", f"neck.reassemble_stage.layers.{i}.projection.weight"))
        rename_keys.append((f"depth_head.projects.{i}.bias", f"neck.reassemble_stage.layers.{i}.projection.bias"))
        if i != 2:
            rename_keys.append((f"depth_head.resize_layers.{i}.weight", f"neck.reassemble_stage.layers.{i}.resize.weight"))
            rename_keys.append((f"depth_head.resize_layers.{i}.bias", f"neck.reassemble_stage.layers.{i}.resize.bias"))
    mapping = {1:3, 2:2, 3:1, 4:0}
    for i in range(1, 5):
        j = mapping[i]
        rename_keys.append((f"depth_head.scratch.refinenet{i}.out_conv.weight", f"neck.fusion_stage.layers.{j}.projection.weight"))
        rename_keys.append((f"depth_head.scratch.refinenet{i}.out_conv.bias", f"neck.fusion_stage.layers.{j}.projection.bias"))
        rename_keys.append((f"depth_head.scratch.refinenet{i}.resConfUnit1.conv1.weight", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution1.weight"))
        rename_keys.append((f"depth_head.scratch.refinenet{i}.resConfUnit1.conv1.bias", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution1.bias"))
        rename_keys.append((f"depth_head.scratch.refinenet{i}.resConfUnit1.conv2.weight", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution2.weight"))
        rename_keys.append((f"depth_head.scratch.refinenet{i}.resConfUnit1.conv2.bias", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution2.bias"))
        rename_keys.append((f"depth_head.scratch.refinenet{i}.resConfUnit2.conv1.weight", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution1.weight"))
        rename_keys.append((f"depth_head.scratch.refinenet{i}.resConfUnit2.conv1.bias", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution1.bias"))
        rename_keys.append((f"depth_head.scratch.refinenet{i}.resConfUnit2.conv2.weight", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution2.weight"))
        rename_keys.append((f"depth_head.scratch.refinenet{i}.resConfUnit2.conv2.bias", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution2.bias"))
    for i in range(4): rename_keys.append((f"depth_head.scratch.layer{i+1}_rn.weight", f"neck.convs.{i}.weight"))
    rename_keys.append(("depth_head.scratch.output_conv1.weight", "head.conv1.weight"))
    rename_keys.append(("depth_head.scratch.output_conv1.bias", "head.conv1.bias"))
    rename_keys.append(("depth_head.scratch.output_conv2.0.weight", "head.conv2.weight"))
    rename_keys.append(("depth_head.scratch.output_conv2.0.bias", "head.conv2.bias"))
    rename_keys.append(("depth_head.scratch.output_conv2.2.weight", "head.conv3.weight"))
    rename_keys.append(("depth_head.scratch.output_conv2.2.bias", "head.conv3.bias"))
    return rename_keys
def read_in_q_k_v(state_dict, config):
    hidden_size = config.backbone_config.hidden_size
    for i in range(config.backbone_config.num_hidden_layers):
        in_proj_weight = state_dict.pop(f"pretrained.blocks.{i}.attn.qkv.weight")
        in_proj_bias = state_dict.pop(f"pretrained.blocks.{i}.attn.qkv.bias")
        state_dict[f"backbone.encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[:hidden_size, :]
        state_dict[f"backbone.encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[:hidden_size]
        state_dict[f"backbone.encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[hidden_size : hidden_size * 2, :]
        state_dict[f"backbone.encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[hidden_size : hidden_size * 2]
        state_dict[f"backbone.encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[-hidden_size:, :]
        state_dict[f"backbone.encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-hidden_size:]
def rename_key(dct, old, new):
    val = dct.pop(old)
    dct[new] = val
def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    im = Image.open(requests.get(url, stream=True).raw)
    return im
name_to_checkpoint = {'depth-anything-small': 'pytorch_model.bin', 'depth-anything-base': 'pytorch_model.bin', 'depth-anything-large': 'pytorch_model.bin',
'depth-anything-v2-small': 'depth_anything_v2_vits.pth', 'depth-anything-v2-base': 'depth_anything_v2_vitb.pth', 'depth-anything-v2-large': 'depth_anything_v2_vitl.pth',
'depth-anything-v2-metric-indoor-small': 'depth_anything_v2_metric_hypersim_vits.pth', 'depth-anything-v2-metric-indoor-base': 'depth_anything_v2_metric_hypersim_vitb.pth',
'depth-anything-v2-metric-indoor-large': 'depth_anything_v2_metric_hypersim_vitl.pth', 'depth-anything-v2-metric-outdoor-small': 'depth_anything_v2_metric_vkitti_vits.pth',
'depth-anything-v2-metric-outdoor-base': 'depth_anything_v2_metric_vkitti_vitb.pth', 'depth-anything-v2-metric-outdoor-large': 'depth_anything_v2_metric_vkitti_vitl.pth'}
@torch.no_grad()
def convert_dpt_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub, verify_logits):
    config = get_dpt_config(model_name)
    model_name_to_repo = {'depth-anything-small': 'LiheYoung/depth_anything_vits14', 'depth-anything-base': 'LiheYoung/depth_anything_vitb14', 'depth-anything-large': 'LiheYoung/depth_anything_vitl14',
    'depth-anything-v2-small': 'depth-anything/Depth-Anything-V2-Small', 'depth-anything-v2-base': 'depth-anything/Depth-Anything-V2-Base', 'depth-anything-v2-large': 'depth-anything/Depth-Anything-V2-Large',
    'depth-anything-v2-metric-indoor-small': 'depth-anything/Depth-Anything-V2-Metric-Hypersim-Small', 'depth-anything-v2-metric-indoor-base': 'depth-anything/Depth-Anything-V2-Metric-Hypersim-Base',
    'depth-anything-v2-metric-indoor-large': 'depth-anything/Depth-Anything-V2-Metric-Hypersim-Large', 'depth-anything-v2-metric-outdoor-small': 'depth-anything/Depth-Anything-V2-Metric-VKITTI-Small',
    'depth-anything-v2-metric-outdoor-base': 'depth-anything/Depth-Anything-V2-Metric-VKITTI-Base', 'depth-anything-v2-metric-outdoor-large': 'depth-anything/Depth-Anything-V2-Metric-VKITTI-Large'}
    repo_id = model_name_to_repo[model_name]
    filename = name_to_checkpoint[model_name]
    filepath = hf_hub_download(repo_id=repo_id, filename=f"{filename}")
    state_dict = torch.load(filepath, map_location="cpu")
    rename_keys = create_rename_keys(config)
    for src, dest in rename_keys: rename_key(state_dict, src, dest)
    read_in_q_k_v(state_dict, config)
    model = DepthAnythingForDepthEstimation(config)
    model.load_state_dict(state_dict)
    model.eval()
    processor = DPTImageProcessor(do_resize=True, size={"height": 518, "width": 518}, ensure_multiple_of=14, keep_aspect_ratio=True, do_rescale=True, do_normalize=True,
    image_mean=[0.485, 0.456, 0.406], image_std=[0.229, 0.224, 0.225])
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    pixel_values = processor(image, return_tensors="pt").pixel_values
    with torch.no_grad():
        outputs = model(pixel_values)
        predicted_depth = outputs.predicted_depth
    print("Shape of predicted depth:", predicted_depth.shape)
    print("First values:", predicted_depth[0, :3, :3])
    if verify_logits:
        expected_shape = torch.Size([1, 518, 686])
        if model_name == "depth-anything-small": expected_slice = torch.tensor([[8.8204, 8.6468, 8.6195], [8.3313, 8.6027, 8.7526], [8.6526, 8.6866, 8.7453]])
        elif model_name == "depth-anything-base": expected_slice = torch.tensor([[26.3997, 26.3004, 26.3928], [26.2260, 26.2092, 26.3427], [26.0719, 26.0483, 26.1254]])
        elif model_name == "depth-anything-large": expected_slice = torch.tensor([[87.9968, 87.7493, 88.2704], [87.1927, 87.6611, 87.3640], [86.7789, 86.9469, 86.7991]])
        elif model_name == "depth-anything-v2-small": expected_slice = torch.tensor([[2.6751, 2.6211, 2.6571], [2.5820, 2.6138, 2.6271], [2.6160, 2.6141, 2.6306]])
        elif model_name == "depth-anything-v2-base": expected_slice = torch.tensor([[4.3576, 4.3723, 4.3908], [4.3231, 4.3146, 4.3611], [4.3016, 4.3170, 4.3121]])
        elif model_name == "depth-anything-v2-large": expected_slice = torch.tensor([[162.2751, 161.8504, 162.8788], [160.3138, 160.8050, 161.9835], [159.3812, 159.9884, 160.0768]])
        elif model_name == "depth-anything-v2-metric-indoor-small": expected_slice = torch.tensor([[1.3349, 1.2946, 1.2801], [1.2793, 1.2337, 1.2899], [1.2629, 1.2218, 1.2476]])
        elif model_name == "depth-anything-v2-metric-indoor-base": expected_slice = torch.tensor([[1.4601, 1.3824, 1.4904], [1.5031, 1.4349, 1.4274], [1.4570, 1.4578, 1.4200]])
        elif model_name == "depth-anything-v2-metric-indoor-large": expected_slice = torch.tensor([[1.5040, 1.5019, 1.5218], [1.5087, 1.5195, 1.5149], [1.5437, 1.5128, 1.5252]])
        elif model_name == "depth-anything-v2-metric-outdoor-small": expected_slice = torch.tensor([[9.5804, 8.0339, 7.7386], [7.9890, 7.2464, 7.7149], [7.7021, 7.2330, 7.3304]])
        elif model_name == "depth-anything-v2-metric-outdoor-base": expected_slice = torch.tensor([[10.2916, 9.0933, 8.8622], [9.1964, 9.3393, 9.0644], [8.9618, 9.4201, 9.2262]])
        elif model_name == "depth-anything-v2-metric-outdoor-large": expected_slice = torch.tensor([[14.0137, 13.3627, 13.1080], [13.2522, 13.3943, 13.3705], [13.0581, 13.4505, 13.3925]])
        else: raise ValueError("Not supported")
        assert predicted_depth.shape == torch.Size(expected_shape)
        assert torch.allclose(predicted_depth[0, :3, :3], expected_slice, atol=1e-4)
        print("Looks ok!")
    if pytorch_dump_folder_path is not None:
        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
        print(f"Saving model and processor to {pytorch_dump_folder_path}")
        model.save_pretrained(pytorch_dump_folder_path)
        processor.save_pretrained(pytorch_dump_folder_path)
    if push_to_hub:
        print("Pushing model and processor to hub...")
        model.push_to_hub(repo_id=f"{model_name.title()}-hf")
        processor.push_to_hub(repo_id=f"{model_name.title()}-hf")
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_name", default="depth-anything-small", type=str, choices=name_to_checkpoint.keys(), help="Name of the model you'd like to convert.")
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory.")
    parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub after conversion.")
    parser.add_argument("--verify_logits", action="store_false", required=False, help="Whether to verify the logits after conversion.")
    args = parser.parse_args()
    convert_dpt_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.verify_logits)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
