"""
    #################################################################################################################################################################################################
    # 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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from sapiens_transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def create_rename_keys(config, base_model=False):
    rename_keys = []
    for i in range(config.num_hidden_layers):
        rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight"))
        rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias"))
        rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight"))
        rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias"))
        rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight"))
        rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias"))
        rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight"))
        rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias"))
        rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight"))
        rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias"))
    rename_keys.extend([("cls_token", "vit.embeddings.cls_token"), ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
    ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"), ("pos_embed", "vit.embeddings.position_embeddings")])
    if base_model:
        rename_keys.extend([("norm.weight", "layernorm.weight"), ("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"blocks.{i}.attn.qkv.weight")
        in_proj_bias = state_dict.pop(f"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 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_vit_checkpoint(model_name, pytorch_dump_folder_path, base_model=True):
    config = ViTConfig()
    if model_name[-1] == "8": config.patch_size = 8
    if not base_model:
        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 model_name in ["dino_vits8", "dino_vits16"]:
        config.hidden_size = 384
        config.intermediate_size = 1536
        config.num_hidden_layers = 12
        config.num_attention_heads = 6
    original_model = torch.hub.load("facebookresearch/dino:main", model_name)
    original_model.eval()
    state_dict = original_model.state_dict()
    if base_model: remove_classification_head_(state_dict)
    rename_keys = create_rename_keys(config, base_model=base_model)
    for src, dest in rename_keys: rename_key(state_dict, src, dest)
    read_in_q_k_v(state_dict, config, base_model)
    if base_model: model = ViTModel(config, add_pooling_layer=False).eval()
    else: model = ViTForImageClassification(config).eval()
    model.load_state_dict(state_dict)
    image_processor = ViTImageProcessor()
    encoding = image_processor(images=prepare_img(), return_tensors="pt")
    pixel_values = encoding["pixel_values"]
    outputs = model(pixel_values)
    if base_model:
        final_hidden_state_cls_token = original_model(pixel_values)
        assert torch.allclose(final_hidden_state_cls_token, outputs.last_hidden_state[:, 0, :], atol=1e-1)
    else:
        logits = original_model(pixel_values)
        assert logits.shape == outputs.logits.shape
        assert torch.allclose(logits, outputs.logits, atol=1e-3)
    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    print(f"Saving model {model_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("--model_name", default="dino_vitb16", type=str, help="Name of the model trained with DINO 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("--base_model", action="store_true", help="Whether to only convert the base model (no projection head weights).")
    parser.set_defaults(base_model=True)
    args = parser.parse_args()
    convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
