"""
    #################################################################################################################################################################################################
    # 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
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from sapiens_transformers import (SwiftFormerConfig, SwiftFormerForImageClassification, ViTImageProcessor)
from sapiens_transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
device = torch.device("cpu")
def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    im = Image.open(requests.get(url, stream=True).raw)
    return im
def get_expected_output(swiftformer_name):
    if swiftformer_name == "swiftformer_xs": return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01])
    elif swiftformer_name == "swiftformer_s": return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01])
    elif swiftformer_name == "swiftformer_l1": return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02])
    elif swiftformer_name == "swiftformer_l3": return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02])
def rename_key(dct, old, new):
    val = dct.pop(old)
    dct[new] = val
def create_rename_keys(state_dict):
    rename_keys = []
    for k in state_dict.keys():
        k_new = k
        if ".pwconv" in k: k_new = k_new.replace(".pwconv", ".point_wise_conv")
        if ".dwconv" in k: k_new = k_new.replace(".dwconv", ".depth_wise_conv")
        if ".Proj." in k: k_new = k_new.replace(".Proj.", ".proj.")
        if "patch_embed" in k_new: k_new = k_new.replace("patch_embed", "swiftformer.patch_embed.patch_embedding")
        if "network" in k_new:
            ls = k_new.split(".")
            if ls[2].isdigit(): k_new = "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:])
            else: k_new = k_new.replace("network", "swiftformer.encoder.network")
        rename_keys.append((k, k_new))
    return rename_keys
@torch.no_grad()
def convert_swiftformer_checkpoint(swiftformer_name, pytorch_dump_folder_path, original_ckpt):
    config = SwiftFormerConfig()
    config.num_labels = 1000
    repo_id = "huggingface/label-files"
    filename = "imagenet-1k-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()}
    config.id2label = id2label
    config.label2id = {v: k for k, v in id2label.items()}
    if swiftformer_name == "swiftformer_xs":
        config.depths = [3, 3, 6, 4]
        config.embed_dims = [48, 56, 112, 220]
    elif swiftformer_name == "swiftformer_s":
        config.depths = [3, 3, 9, 6]
        config.embed_dims = [48, 64, 168, 224]
    elif swiftformer_name == "swiftformer_l1":
        config.depths = [4, 3, 10, 5]
        config.embed_dims = [48, 96, 192, 384]
    elif swiftformer_name == "swiftformer_l3":
        config.depths = [4, 4, 12, 6]
        config.embed_dims = [64, 128, 320, 512]
    if original_ckpt:
        if original_ckpt.startswith("https"): checkpoint = torch.hub.load_state_dict_from_url(original_ckpt, map_location="cpu", check_hash=True)
        else: checkpoint = torch.load(original_ckpt, map_location="cpu")
    state_dict = checkpoint
    rename_keys = create_rename_keys(state_dict)
    for rename_key_src, rename_key_dest in rename_keys: rename_key(state_dict, rename_key_src, rename_key_dest)
    hf_model = SwiftFormerForImageClassification(config).eval()
    hf_model.load_state_dict(state_dict)
    image = prepare_img()
    processor = ViTImageProcessor.from_pretrained("preprocessor_config")
    inputs = processor(images=image, return_tensors="pt")
    timm_logits = get_expected_output(swiftformer_name)
    hf_logits = hf_model(inputs["pixel_values"]).logits
    assert hf_logits.shape == torch.Size([1, 1000])
    assert torch.allclose(hf_logits[0, 0:5], timm_logits, atol=1e-3)
    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    print(f"Saving model {swiftformer_name} to {pytorch_dump_folder_path}")
    hf_model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--swiftformer_name", default="swiftformer_xs", choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"], type=str, help="Name of the SwiftFormer model you'd like to convert.")
    parser.add_argument("--pytorch_dump_folder_path", default="./converted_outputs/", type=str, help="Path to the output PyTorch model directory.")
    parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")
    args = parser.parse_args()
    convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
