"""
    #################################################################################################################################################################################################
    # 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 ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from sapiens_transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def create_rename_keys(config, base_model=False):
    rename_keys = []
    for i in range(config.num_hidden_layers):
        rename_keys.append((f"module.blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight"))
        rename_keys.append((f"module.blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias"))
        rename_keys.append((f"module.blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight"))
        rename_keys.append((f"module.blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias"))
        rename_keys.append((f"module.blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight"))
        rename_keys.append((f"module.blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias"))
        rename_keys.append((f"module.blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight"))
        rename_keys.append((f"module.blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias"))
        rename_keys.append((f"module.blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight"))
        rename_keys.append((f"module.blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias"))
    rename_keys.extend([("module.cls_token", "vit.embeddings.cls_token"), ("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
    ("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"), ("module.pos_embed", "vit.embeddings.position_embeddings")])
    if base_model:
        rename_keys.extend([("module.norm.weight", "layernorm.weight"), ("module.norm.bias", "layernorm.bias")])
        rename_keys = [(pair[0], pair[1][4:]) if pair[1].startswith("vit") else pair for pair in rename_keys]
    else: rename_keys.extend([("norm.weight", "vit.layernorm.weight"), ("norm.bias", "vit.layernorm.bias"), ("head.weight", "classifier.weight"), ("head.bias", "classifier.bias")])
    return rename_keys
def read_in_q_k_v(state_dict, config, base_model=False):
    for i in range(config.num_hidden_layers):
        if base_model: prefix = ""
        else: prefix = "vit."
        in_proj_weight = state_dict.pop(f"module.blocks.{i}.attn.qkv.weight")
        in_proj_bias = state_dict.pop(f"module.blocks.{i}.attn.qkv.bias")
        state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[: config.hidden_size, :]
        state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size]
        state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[config.hidden_size : config.hidden_size * 2, :]
        state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[config.hidden_size : config.hidden_size * 2]
        state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[-config.hidden_size :, :]
        state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :]
def remove_classification_head_(state_dict):
    ignore_keys = ["head.weight", "head.bias"]
    for k in ignore_keys: state_dict.pop(k, None)
def remove_projection_head(state_dict):
    ignore_keys = ["module.fc.fc1.weight", "module.fc.fc1.bias", "module.fc.bn1.weight", "module.fc.bn1.bias", "module.fc.bn1.running_mean", "module.fc.bn1.running_var",
    "module.fc.bn1.num_batches_tracked", "module.fc.fc2.weight", "module.fc.fc2.bias", "module.fc.bn2.weight", "module.fc.bn2.bias", "module.fc.bn2.running_mean",
    "module.fc.bn2.running_var", "module.fc.bn2.num_batches_tracked", "module.fc.fc3.weight", "module.fc.fc3.bias"]
    for k in ignore_keys: state_dict.pop(k, None)
def rename_key(dct, old, new):
    val = dct.pop(old)
    dct[new] = val
def convert_vit_msn_checkpoint(checkpoint_url, pytorch_dump_folder_path):
    config = ViTMSNConfig()
    config.num_labels = 1000
    repo_id = "datasets/huggingface/label-files"
    filename = "imagenet-1k-id2label.json"
    id2label = json.load(open(hf_hub_download(repo_id, filename), "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 "s16" in checkpoint_url:
        config.hidden_size = 384
        config.intermediate_size = 1536
        config.num_attention_heads = 6
    elif "l16" in checkpoint_url:
        config.hidden_size = 1024
        config.intermediate_size = 4096
        config.num_hidden_layers = 24
        config.num_attention_heads = 16
        config.hidden_dropout_prob = 0.1
    elif "b4" in checkpoint_url: config.patch_size = 4
    elif "l7" in checkpoint_url:
        config.patch_size = 7
        config.hidden_size = 1024
        config.intermediate_size = 4096
        config.num_hidden_layers = 24
        config.num_attention_heads = 16
        config.hidden_dropout_prob = 0.1
    model = ViTMSNModel(config)
    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["target_encoder"]
    image_processor = ViTImageProcessor(size=config.image_size)
    remove_projection_head(state_dict)
    rename_keys = create_rename_keys(config, base_model=True)
    for src, dest in rename_keys: rename_key(state_dict, src, dest)
    read_in_q_k_v(state_dict, config, base_model=True)
    model.load_state_dict(state_dict)
    model.eval()
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    image_processor = ViTImageProcessor(size=config.image_size, image_mean=IMAGENET_DEFAULT_MEAN, image_std=IMAGENET_DEFAULT_STD)
    inputs = image_processor(images=image, return_tensors="pt")
    torch.manual_seed(2)
    outputs = model(**inputs)
    last_hidden_state = outputs.last_hidden_state
    if "s16" in checkpoint_url: expected_slice = torch.tensor([[-1.0915, -1.4876, -1.1809]])
    elif "b16" in checkpoint_url: expected_slice = torch.tensor([[14.2889, -18.9045, 11.7281]])
    elif "l16" in checkpoint_url: expected_slice = torch.tensor([[41.5028, -22.8681, 45.6475]])
    elif "b4" in checkpoint_url: expected_slice = torch.tensor([[-4.3868, 5.2932, -0.4137]])
    else: expected_slice = torch.tensor([[-0.1792, -0.6465, 2.4263]])
    assert torch.allclose(last_hidden_state[:, 0, :3], expected_slice, atol=1e-4)
    print(f"Saving model 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("--checkpoint_url", default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar", type=str, help="URL of the checkpoint you'd like to convert.")
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory.")
    args = parser.parse_args()
    convert_vit_msn_checkpoint(args.checkpoint_url, 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.                                                                 #
    #################################################################################################################################################################################################
"""
