"""
    #################################################################################################################################################################################################
    # 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 PIL import Image
from sapiens_transformers import BeitConfig, DPTConfig, DPTForDepthEstimation, DPTImageProcessor
from sapiens_transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def get_dpt_config(model_name):
    hidden_size = 768
    num_hidden_layers = 12
    num_attention_heads = 12
    intermediate_size = 3072
    out_features = ["stage3", "stage6", "stage9", "stage12"]
    if "large" in model_name:
        hidden_size = 1024
        num_hidden_layers = 24
        num_attention_heads = 16
        intermediate_size = 4096
        out_features = ["stage6", "stage12", "stage18", "stage24"]
    if "512" in model_name: image_size = 512
    elif "384" in model_name: image_size = 384
    else: raise ValueError("Model not supported")
    backbone_config = BeitConfig(image_size=image_size, num_hidden_layers=num_hidden_layers, hidden_size=hidden_size, intermediate_size=intermediate_size,
    num_attention_heads=num_attention_heads, use_relative_position_bias=True, reshape_hidden_states=False, out_features=out_features)
    neck_hidden_sizes = [256, 512, 1024, 1024] if "large" in model_name else [96, 192, 384, 768]
    config = DPTConfig(backbone_config=backbone_config, neck_hidden_sizes=neck_hidden_sizes)
    return config, image_size
def create_rename_keys(config):
    rename_keys = []
    rename_keys.append(("pretrained.model.cls_token", "backbone.embeddings.cls_token"))
    rename_keys.append(("pretrained.model.patch_embed.proj.weight", "backbone.embeddings.patch_embeddings.projection.weight"))
    rename_keys.append(("pretrained.model.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.model.blocks.{i}.gamma_1", f"backbone.encoder.layer.{i}.lambda_1"))
        rename_keys.append((f"pretrained.model.blocks.{i}.gamma_2", f"backbone.encoder.layer.{i}.lambda_2"))
        rename_keys.append((f"pretrained.model.blocks.{i}.norm1.weight", f"backbone.encoder.layer.{i}.layernorm_before.weight"))
        rename_keys.append((f"pretrained.model.blocks.{i}.norm1.bias", f"backbone.encoder.layer.{i}.layernorm_before.bias"))
        rename_keys.append((f"pretrained.model.blocks.{i}.norm2.weight", f"backbone.encoder.layer.{i}.layernorm_after.weight"))
        rename_keys.append((f"pretrained.model.blocks.{i}.norm2.bias", f"backbone.encoder.layer.{i}.layernorm_after.bias"))
        rename_keys.append((f"pretrained.model.blocks.{i}.mlp.fc1.weight", f"backbone.encoder.layer.{i}.intermediate.dense.weight"))
        rename_keys.append((f"pretrained.model.blocks.{i}.mlp.fc1.bias", f"backbone.encoder.layer.{i}.intermediate.dense.bias"))
        rename_keys.append((f"pretrained.model.blocks.{i}.mlp.fc2.weight", f"backbone.encoder.layer.{i}.output.dense.weight"))
        rename_keys.append((f"pretrained.model.blocks.{i}.mlp.fc2.bias", f"backbone.encoder.layer.{i}.output.dense.bias"))
        rename_keys.append((f"pretrained.model.blocks.{i}.attn.proj.weight", f"backbone.encoder.layer.{i}.attention.output.dense.weight"))
        rename_keys.append((f"pretrained.model.blocks.{i}.attn.proj.bias", f"backbone.encoder.layer.{i}.attention.output.dense.bias"))
        rename_keys.append((f"pretrained.model.blocks.{i}.attn.relative_position_bias_table", f"backbone.encoder.layer.{i}.attention.attention.relative_position_bias.relative_position_bias_table"))
        rename_keys.append((f"pretrained.model.blocks.{i}.attn.relative_position_index", f"backbone.encoder.layer.{i}.attention.attention.relative_position_bias.relative_position_index"))
    for i in range(4):
        rename_keys.append((f"pretrained.act_postprocess{i+1}.0.project.0.weight", f"neck.reassemble_stage.readout_projects.{i}.0.weight"))
        rename_keys.append((f"pretrained.act_postprocess{i+1}.0.project.0.bias", f"neck.reassemble_stage.readout_projects.{i}.0.bias"))
        rename_keys.append((f"pretrained.act_postprocess{i+1}.3.weight", f"neck.reassemble_stage.layers.{i}.projection.weight"))
        rename_keys.append((f"pretrained.act_postprocess{i+1}.3.bias", f"neck.reassemble_stage.layers.{i}.projection.bias"))
        if i != 2:
            rename_keys.append((f"pretrained.act_postprocess{i+1}.4.weight", f"neck.reassemble_stage.layers.{i}.resize.weight"))
            rename_keys.append((f"pretrained.act_postprocess{i+1}.4.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"scratch.refinenet{i}.out_conv.weight", f"neck.fusion_stage.layers.{j}.projection.weight"))
        rename_keys.append((f"scratch.refinenet{i}.out_conv.bias", f"neck.fusion_stage.layers.{j}.projection.bias"))
        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv1.weight", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution1.weight"))
        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv1.bias", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution1.bias"))
        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv2.weight", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution2.weight"))
        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv2.bias", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution2.bias"))
        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv1.weight", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution1.weight"))
        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv1.bias", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution1.bias"))
        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv2.weight", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution2.weight"))
        rename_keys.append((f"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"scratch.layer{i+1}_rn.weight", f"neck.convs.{i}.weight"))
    for i in range(0, 5, 2):
        rename_keys.append((f"scratch.output_conv.{i}.weight", f"head.head.{i}.weight"))
        rename_keys.append((f"scratch.output_conv.{i}.bias", f"head.head.{i}.bias"))
    return rename_keys
def remove_ignore_keys_(state_dict):
    ignore_keys = ["pretrained.model.head.weight", "pretrained.model.head.bias"]
    for k in ignore_keys: state_dict.pop(k, None)
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.model.blocks.{i}.attn.qkv.weight")
        q_bias = state_dict.pop(f"pretrained.model.blocks.{i}.attn.q_bias")
        v_bias = state_dict.pop(f"pretrained.model.blocks.{i}.attn.v_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"] = q_bias
        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.value.weight"] = in_proj_weight[-hidden_size:, :]
        state_dict[f"backbone.encoder.layer.{i}.attention.attention.value.bias"] = v_bias
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
@torch.no_grad()
def convert_dpt_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub):
    name_to_url = {'dpt-beit-large-512': 'https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt', 'dpt-beit-large-384': 'https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_384.pt', 'dpt-beit-base-384': 'https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_base_384.pt'}
    checkpoint_url = name_to_url[model_name]
    config, image_size = get_dpt_config(model_name)
    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
    remove_ignore_keys_(state_dict)
    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 = DPTForDepthEstimation(config)
    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
    print("Missing keys:", missing_keys)
    print("Unexpected keys:", unexpected_keys)
    assert missing_keys == []
    model.eval()
    processor = DPTImageProcessor(size={"height": image_size, "width": image_size}, keep_aspect_ratio=False, ensure_multiple_of=32)
    image = prepare_img()
    pixel_values = processor(image, return_tensors="pt").pixel_values
    print("First values of pixel values:", pixel_values[0, 0, :3, :3])
    print("Mean of pixel values:", pixel_values.mean().item())
    print("Shape of pixel values:", pixel_values.shape)
    import requests
    from PIL import Image
    from torchvision import transforms
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    transforms = transforms.Compose([transforms.Resize((image_size, image_size)), transforms.ToTensor()])
    pixel_values = transforms(image).unsqueeze(0)
    with torch.no_grad(): outputs = model(pixel_values)
    predicted_depth = outputs.predicted_depth
    print("Shape of predicted depth:", predicted_depth.shape)
    print("First values of predicted depth:", predicted_depth[0, :3, :3])
    if model_name == "dpt-beit-large-512":
        expected_shape = torch.Size([1, 512, 512])
        expected_slice = torch.tensor([[2804.6260, 2792.5708, 2812.9263], [2772.0288, 2780.1118, 2796.2529], [2748.1094, 2766.6558, 2766.9834]])
    elif model_name == "dpt-beit-large-384":
        expected_shape = torch.Size([1, 384, 384])
        expected_slice = torch.tensor([[1783.2273, 1780.5729, 1792.6453], [1759.9817, 1765.5359, 1778.5002], [1739.1633, 1754.7903, 1757.1990]])
    elif model_name == "dpt-beit-base-384":
        expected_shape = torch.Size([1, 384, 384])
        expected_slice = torch.tensor([[2898.4482, 2891.3750, 2904.8079], [2858.6685, 2877.2615, 2894.4507], [2842.1235, 2854.1023, 2861.6328]])
    assert predicted_depth.shape == torch.Size(expected_shape)
    assert torch.allclose(predicted_depth[0, :3, :3], expected_slice)
    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"nielsr/{model_name}")
        processor.push_to_hub(repo_id=f"nielsr/{model_name}")
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_name", default="dpt-beit-large-512", type=str, choices=["dpt-beit-large-512", "dpt-beit-large-384", "dpt-beit-base-384"], 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.")
    args = parser.parse_args()
    convert_dpt_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
