"""
    #################################################################################################################################################################################################
    # 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
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from sapiens_transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def get_upernet_config(model_name):
    auxiliary_in_channels = 384
    if "tiny" in model_name:
        depths = [3, 3, 9, 3]
        hidden_sizes = [96, 192, 384, 768]
    if "small" in model_name:
        depths = [3, 3, 27, 3]
        hidden_sizes = [96, 192, 384, 768]
    if "base" in model_name:
        depths = [3, 3, 27, 3]
        hidden_sizes = [128, 256, 512, 1024]
        auxiliary_in_channels = 512
    if "large" in model_name:
        depths = [3, 3, 27, 3]
        hidden_sizes = [192, 384, 768, 1536]
        auxiliary_in_channels = 768
    if "xlarge" in model_name:
        depths = [3, 3, 27, 3]
        hidden_sizes = [256, 512, 1024, 2048]
        auxiliary_in_channels = 1024
    num_labels = 150
    repo_id = "huggingface/label-files"
    filename = "ade20k-id2label.json"
    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
    id2label = {int(k): v for k, v in id2label.items()}
    label2id = {v: k for k, v in id2label.items()}
    backbone_config = ConvNextConfig(depths=depths, hidden_sizes=hidden_sizes, out_features=["stage1", "stage2", "stage3", "stage4"])
    config = UperNetConfig(backbone_config=backbone_config, auxiliary_in_channels=auxiliary_in_channels, num_labels=num_labels, id2label=id2label, label2id=label2id)
    return config
def create_rename_keys(config):
    rename_keys = []
    rename_keys.append(("backbone.downsample_layers.0.0.weight", "backbone.embeddings.patch_embeddings.weight"))
    rename_keys.append(("backbone.downsample_layers.0.0.bias", "backbone.embeddings.patch_embeddings.bias"))
    rename_keys.append(("backbone.downsample_layers.0.1.weight", "backbone.embeddings.layernorm.weight"))
    rename_keys.append(("backbone.downsample_layers.0.1.bias", "backbone.embeddings.layernorm.bias"))
    for i in range(len(config.backbone_config.depths)):
        for j in range(config.backbone_config.depths[i]):
            rename_keys.append((f"backbone.stages.{i}.{j}.gamma", f"backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter"))
            rename_keys.append((f"backbone.stages.{i}.{j}.depthwise_conv.weight", f"backbone.encoder.stages.{i}.layers.{j}.dwconv.weight"))
            rename_keys.append((f"backbone.stages.{i}.{j}.depthwise_conv.bias", f"backbone.encoder.stages.{i}.layers.{j}.dwconv.bias"))
            rename_keys.append((f"backbone.stages.{i}.{j}.norm.weight", f"backbone.encoder.stages.{i}.layers.{j}.layernorm.weight"))
            rename_keys.append((f"backbone.stages.{i}.{j}.norm.bias", f"backbone.encoder.stages.{i}.layers.{j}.layernorm.bias"))
            rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv1.weight", f"backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight"))
            rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv1.bias", f"backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias"))
            rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv2.weight", f"backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight"))
            rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv2.bias", f"backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias"))
        if i > 0:
            rename_keys.append((f"backbone.downsample_layers.{i}.0.weight", f"backbone.encoder.stages.{i}.downsampling_layer.0.weight"))
            rename_keys.append((f"backbone.downsample_layers.{i}.0.bias", f"backbone.encoder.stages.{i}.downsampling_layer.0.bias"))
            rename_keys.append((f"backbone.downsample_layers.{i}.1.weight", f"backbone.encoder.stages.{i}.downsampling_layer.1.weight"))
            rename_keys.append((f"backbone.downsample_layers.{i}.1.bias", f"backbone.encoder.stages.{i}.downsampling_layer.1.bias"))
        rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight"))
        rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias"))
    rename_keys.extend([("decode_head.conv_seg.weight", "decode_head.classifier.weight"), ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
    ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"), ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias")])
    return rename_keys
def rename_key(dct, old, new):
    val = dct.pop(old)
    dct[new] = val
def convert_upernet_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub):
    model_name_to_url = {'upernet-convnext-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth',
    'upernet-convnext-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth',
    'upernet-convnext-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth',
    'upernet-convnext-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth',
    'upernet-convnext-xlarge': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth'}
    checkpoint_url = model_name_to_url[model_name]
    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["state_dict"]
    config = get_upernet_config(model_name)
    model = UperNetForSemanticSegmentation(config)
    model.eval()
    for key in state_dict.copy().keys():
        val = state_dict.pop(key)
        if "bn" in key: key = key.replace("bn", "batch_norm")
        state_dict[key] = val
    rename_keys = create_rename_keys(config)
    for src, dest in rename_keys: rename_key(state_dict, src, dest)
    model.load_state_dict(state_dict)
    url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
    image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
    processor = SegformerImageProcessor()
    pixel_values = processor(image, return_tensors="pt").pixel_values
    with torch.no_grad(): outputs = model(pixel_values)
    if model_name == "upernet-convnext-tiny": expected_slice = torch.tensor([[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]])
    elif model_name == "upernet-convnext-small": expected_slice = torch.tensor([[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]])
    elif model_name == "upernet-convnext-base": expected_slice = torch.tensor([[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]])
    elif model_name == "upernet-convnext-large": expected_slice = torch.tensor([[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]])
    elif model_name == "upernet-convnext-xlarge": expected_slice = torch.tensor([[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]])
    print("Logits:", outputs.logits[0, 0, :3, :3])
    assert torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4)
    print("Looks ok!")
    if pytorch_dump_folder_path is not None:
        print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
        model.save_pretrained(pytorch_dump_folder_path)
        print(f"Saving processor to {pytorch_dump_folder_path}")
        processor.save_pretrained(pytorch_dump_folder_path)
    if push_to_hub:
        print(f"Pushing model and processor for {model_name} to hub")
        model.push_to_hub(f"openmmlab/{model_name}")
        processor.push_to_hub(f"openmmlab/{model_name}")
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_name", default="upernet-convnext-tiny", type=str, choices=[f"upernet-convnext-{size}" for size in ["tiny", "small", "base", "large", "xlarge"]], help="Name of the ConvNext UperNet 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 or not to push the converted model to the HF hub.")
    args = parser.parse_args()
    convert_upernet_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.                                                                 #
    #################################################################################################################################################################################################
"""
