"""
    #################################################################################################################################################################################################
    # 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 collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from sapiens_transformers import (MobileViTImageProcessor, MobileViTV2Config, MobileViTV2ForImageClassification, MobileViTV2ForSemanticSegmentation)
from sapiens_transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def load_orig_config_file(orig_cfg_file):
    print("Loading config file...")
    def flatten_yaml_as_dict(d, parent_key="", sep="."):
        items = []
        for k, v in d.items():
            new_key = parent_key + sep + k if parent_key else k
            if isinstance(v, collections.abc.MutableMapping): items.extend(flatten_yaml_as_dict(v, new_key, sep=sep).items())
            else: items.append((new_key, v))
        return dict(items)
    config = argparse.Namespace()
    with open(orig_cfg_file, "r") as yaml_file:
        try:
            cfg = yaml.load(yaml_file, Loader=yaml.FullLoader)
            flat_cfg = flatten_yaml_as_dict(cfg)
            for k, v in flat_cfg.items(): setattr(config, k, v)
        except yaml.YAMLError as exc: logger.error("Error while loading config file: {}. Error message: {}".format(orig_cfg_file, str(exc)))
    return config
def get_mobilevitv2_config(task_name, orig_cfg_file):
    config = MobileViTV2Config()
    is_segmentation_model = False
    if task_name.startswith("imagenet1k_"):
        config.num_labels = 1000
        if int(task_name.strip().split("_")[-1]) == 384: config.image_size = 384
        else: config.image_size = 256
        filename = "imagenet-1k-id2label.json"
    elif task_name.startswith("imagenet21k_to_1k_"):
        config.num_labels = 21000
        if int(task_name.strip().split("_")[-1]) == 384: config.image_size = 384
        else: config.image_size = 256
        filename = "imagenet-22k-id2label.json"
    elif task_name.startswith("ade20k_"):
        config.num_labels = 151
        config.image_size = 512
        filename = "ade20k-id2label.json"
        is_segmentation_model = True
    elif task_name.startswith("voc_"):
        config.num_labels = 21
        config.image_size = 512
        filename = "pascal-voc-id2label.json"
        is_segmentation_model = True
    orig_config = load_orig_config_file(orig_cfg_file)
    assert getattr(orig_config, "model.classification.name", -1) == "mobilevit_v2", "Invalid model"
    config.width_multiplier = getattr(orig_config, "model.classification.mitv2.width_multiplier", 1.0)
    assert (getattr(orig_config, "model.classification.mitv2.attn_norm_layer", -1) == "layer_norm_2d"), "Norm layers other than layer_norm_2d is not supported"
    config.hidden_act = getattr(orig_config, "model.classification.activation.name", "swish")
    if is_segmentation_model:
        config.output_stride = getattr(orig_config, "model.segmentation.output_stride", 16)
        if "_deeplabv3" in task_name:
            config.atrous_rates = getattr(orig_config, "model.segmentation.deeplabv3.aspp_rates", [12, 24, 36])
            config.aspp_out_channels = getattr(orig_config, "model.segmentation.deeplabv3.aspp_out_channels", 512)
            config.aspp_dropout_prob = getattr(orig_config, "model.segmentation.deeplabv3.aspp_dropout", 0.1)
    repo_id = "huggingface/label-files"
    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
    id2label = {int(k): v for k, v in id2label.items()}
    config.id2label = id2label
    config.label2id = {v: k for k, v in id2label.items()}
    return config
def rename_key(dct, old, new):
    val = dct.pop(old)
    dct[new] = val
def create_rename_keys(state_dict, base_model=False):
    if base_model: model_prefix = ""
    else: model_prefix = "mobilevitv2."
    rename_keys = []
    for k in state_dict.keys():
        if k[:8] == "encoder.": k_new = k[8:]
        else: k_new = k
        if ".block." in k: k_new = k_new.replace(".block.", ".")
        if ".conv." in k: k_new = k_new.replace(".conv.", ".convolution.")
        if ".norm." in k: k_new = k_new.replace(".norm.", ".normalization.")
        if "conv_1." in k: k_new = k_new.replace("conv_1.", f"{model_prefix}conv_stem.")
        for i in [1, 2]:
            if f"layer_{i}." in k: k_new = k_new.replace(f"layer_{i}.", f"{model_prefix}encoder.layer.{i-1}.layer.")
        if ".exp_1x1." in k: k_new = k_new.replace(".exp_1x1.", ".expand_1x1.")
        if ".red_1x1." in k: k_new = k_new.replace(".red_1x1.", ".reduce_1x1.")
        for i in [3, 4, 5]:
            if f"layer_{i}.0." in k: k_new = k_new.replace(f"layer_{i}.0.", f"{model_prefix}encoder.layer.{i-1}.downsampling_layer.")
            if f"layer_{i}.1.local_rep.0." in k: k_new = k_new.replace(f"layer_{i}.1.local_rep.0.", f"{model_prefix}encoder.layer.{i-1}.conv_kxk.")
            if f"layer_{i}.1.local_rep.1." in k: k_new = k_new.replace(f"layer_{i}.1.local_rep.1.", f"{model_prefix}encoder.layer.{i-1}.conv_1x1.")
        for i in [3, 4, 5]:
            if i == 3: j_in = [0, 1]
            elif i == 4: j_in = [0, 1, 2, 3]
            elif i == 5: j_in = [0, 1, 2]
            for j in j_in:
                if f"layer_{i}.1.global_rep.{j}." in k: k_new = k_new.replace(f"layer_{i}.1.global_rep.{j}.", f"{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.")
            if f"layer_{i}.1.global_rep.{j+1}." in k: k_new = k_new.replace(f"layer_{i}.1.global_rep.{j+1}.", f"{model_prefix}encoder.layer.{i-1}.layernorm.")
            if f"layer_{i}.1.conv_proj." in k: k_new = k_new.replace(f"layer_{i}.1.conv_proj.", f"{model_prefix}encoder.layer.{i-1}.conv_projection.")
        if "pre_norm_attn.0." in k: k_new = k_new.replace("pre_norm_attn.0.", "layernorm_before.")
        if "pre_norm_attn.1." in k: k_new = k_new.replace("pre_norm_attn.1.", "attention.")
        if "pre_norm_ffn.0." in k: k_new = k_new.replace("pre_norm_ffn.0.", "layernorm_after.")
        if "pre_norm_ffn.1." in k: k_new = k_new.replace("pre_norm_ffn.1.", "ffn.conv1.")
        if "pre_norm_ffn.3." in k: k_new = k_new.replace("pre_norm_ffn.3.", "ffn.conv2.")
        if "classifier.1." in k: k_new = k_new.replace("classifier.1.", "classifier.")
        if "seg_head." in k: k_new = k_new.replace("seg_head.", "segmentation_head.")
        if ".aspp_layer." in k: k_new = k_new.replace(".aspp_layer.", ".")
        if ".aspp_pool." in k: k_new = k_new.replace(".aspp_pool.", ".")
        rename_keys.append((k, k_new))
    return rename_keys
def remove_unused_keys(state_dict):
    keys_to_ignore = []
    for k in state_dict.keys():
        if k.startswith("seg_head.aux_head."): keys_to_ignore.append(k)
    for k in keys_to_ignore: state_dict.pop(k, None)
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_mobilevitv2_checkpoint(task_name, checkpoint_path, orig_config_path, pytorch_dump_folder_path):
    config = get_mobilevitv2_config(task_name, orig_config_path)
    checkpoint = torch.load(checkpoint_path, map_location="cpu")
    if task_name.startswith("ade20k_") or task_name.startswith("voc_"):
        model = MobileViTV2ForSemanticSegmentation(config).eval()
        base_model = False
    else:
        model = MobileViTV2ForImageClassification(config).eval()
        base_model = False
    state_dict = checkpoint
    remove_unused_keys(state_dict)
    rename_keys = create_rename_keys(state_dict, base_model=base_model)
    for rename_key_src, rename_key_dest in rename_keys: rename_key(state_dict, rename_key_src, rename_key_dest)
    model.load_state_dict(state_dict)
    image_processor = MobileViTImageProcessor(crop_size=config.image_size, size=config.image_size + 32)
    encoding = image_processor(images=prepare_img(), return_tensors="pt")
    outputs = model(**encoding)
    if task_name.startswith("imagenet"):
        logits = outputs.logits
        predicted_class_idx = logits.argmax(-1).item()
        print("Predicted class:", model.config.id2label[predicted_class_idx])
        if task_name.startswith("imagenet1k_256") and config.width_multiplier == 1.0:
            expected_logits = torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01])
            assert torch.allclose(logits[0, :3], expected_logits, atol=1e-4)
    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    print(f"Saving model {task_name} to {pytorch_dump_folder_path}")
    model.save_pretrained(pytorch_dump_folder_path)
    print(f"Saving image processor to {pytorch_dump_folder_path}")
    image_processor.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--task", default="imagenet1k_256", type=str, help=("Name of the task for which the MobileViTV2 model you'd like to convert is trained on.\n\tClassification (ImageNet-1k)\n\t\t- MobileViTV2 (256x256): imagenet1k_256\n\t\t- MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n\t\t- MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256):\n\t\t imagenet21k_to_1k_256\n\t\t- MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n\t\t ImageNet-1k 384x384): imagenet21k_to_1k_384\n\tSegmentation\n\t\t- ADE20K Dataset: ade20k_deeplabv3\n\t\t- Pascal VOC 2012 Dataset: voc_deeplabv3"),
    choices=["imagenet1k_256", "imagenet1k_384", "imagenet21k_to_1k_256", "imagenet21k_to_1k_384", "ade20k_deeplabv3", "voc_deeplabv3"])
    parser.add_argument("--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file).")
    parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
    parser.add_argument("--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory.")
    args = parser.parse_args()
    convert_mobilevitv2_checkpoint(args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
