"""
    #################################################################################################################################################################################################
    # 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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from sapiens_transformers.image_utils import PILImageResampling
from sapiens_transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def create_rename_keys(config, has_lm_head=False, is_semantic=False):
    prefix = "backbone." if is_semantic else ""
    rename_keys = []
    for i in range(config.num_hidden_layers):
        rename_keys.append((f"{prefix}blocks.{i}.norm1.weight", f"beit.encoder.layer.{i}.layernorm_before.weight"))
        rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"beit.encoder.layer.{i}.layernorm_before.bias"))
        rename_keys.append((f"{prefix}blocks.{i}.attn.proj.weight", f"beit.encoder.layer.{i}.attention.output.dense.weight"))
        rename_keys.append((f"{prefix}blocks.{i}.attn.proj.bias", f"beit.encoder.layer.{i}.attention.output.dense.bias"))
        rename_keys.append((f"{prefix}blocks.{i}.norm2.weight", f"beit.encoder.layer.{i}.layernorm_after.weight"))
        rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"beit.encoder.layer.{i}.layernorm_after.bias"))
        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.weight", f"beit.encoder.layer.{i}.intermediate.dense.weight"))
        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.bias", f"beit.encoder.layer.{i}.intermediate.dense.bias"))
        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"beit.encoder.layer.{i}.output.dense.weight"))
        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"beit.encoder.layer.{i}.output.dense.bias"))
    rename_keys.extend([(f"{prefix}cls_token", "beit.embeddings.cls_token"), (f"{prefix}patch_embed.proj.weight", "beit.embeddings.patch_embeddings.projection.weight"),
    (f"{prefix}patch_embed.proj.bias", "beit.embeddings.patch_embeddings.projection.bias"), (f"{prefix}pos_embed", "beit.embeddings.position_embeddings")])
    if has_lm_head: rename_keys.extend([("mask_token", "beit.embeddings.mask_token"), ("norm.weight", "layernorm.weight"), ("norm.bias", "layernorm.bias")])
    else: rename_keys.extend([("fc_norm.weight", "beit.pooler.layernorm.weight"), ("fc_norm.bias", "beit.pooler.layernorm.bias"), ("head.weight", "classifier.weight"), ("head.bias", "classifier.bias")])
    return rename_keys
def read_in_q_k_v(state_dict, config, has_lm_head=False, is_semantic=False):
    for i in range(config.num_hidden_layers):
        prefix = "backbone." if is_semantic else ""
        in_proj_weight = state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight")
        q_bias = state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias")
        v_bias = state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias")
        state_dict[f"beit.encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[: config.hidden_size, :]
        state_dict[f"beit.encoder.layer.{i}.attention.attention.query.bias"] = q_bias
        state_dict[f"beit.encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[config.hidden_size : config.hidden_size * 2, :]
        state_dict[f"beit.encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[-config.hidden_size :, :]
        state_dict[f"beit.encoder.layer.{i}.attention.attention.value.bias"] = v_bias
        gamma_1 = state_dict.pop(f"{prefix}blocks.{i}.gamma_1")
        gamma_2 = state_dict.pop(f"{prefix}blocks.{i}.gamma_2")
        state_dict[f"beit.encoder.layer.{i}.lambda_1"] = gamma_1
        state_dict[f"beit.encoder.layer.{i}.lambda_2"] = gamma_2
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_dit_checkpoint(checkpoint_url, pytorch_dump_folder_path, push_to_hub=False):
    has_lm_head = False if "rvlcdip" in checkpoint_url else True
    config = BeitConfig(use_absolute_position_embeddings=True, use_mask_token=has_lm_head)
    if "large" in checkpoint_url or "dit-l" in checkpoint_url:
        config.hidden_size = 1024
        config.intermediate_size = 4096
        config.num_hidden_layers = 24
        config.num_attention_heads = 16
    if "rvlcdip" in checkpoint_url:
        config.num_labels = 16
        repo_id = "huggingface/label-files"
        filename = "rvlcdip-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()}
    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["model"]
    rename_keys = create_rename_keys(config, has_lm_head=has_lm_head)
    for src, dest in rename_keys: rename_key(state_dict, src, dest)
    read_in_q_k_v(state_dict, config, has_lm_head=has_lm_head)
    model = BeitForMaskedImageModeling(config) if has_lm_head else BeitForImageClassification(config)
    model.eval()
    model.load_state_dict(state_dict)
    image_processor = BeitImageProcessor(size=config.image_size, resample=PILImageResampling.BILINEAR, do_center_crop=False)
    image = prepare_img()
    encoding = image_processor(images=image, return_tensors="pt")
    pixel_values = encoding["pixel_values"]
    outputs = model(pixel_values)
    logits = outputs.logits
    expected_shape = [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
    assert logits.shape == torch.Size(expected_shape), "Shape of logits not as expected"
    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    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 push_to_hub:
        if has_lm_head: model_name = "dit-base" if "base" in checkpoint_url else "dit-large"
        else: model_name = "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
        image_processor.push_to_hub(repo_path_or_name=Path(pytorch_dump_folder_path, model_name), organization="nielsr", commit_message="Add image processor", use_temp_dir=True)
        model.push_to_hub(repo_path_or_name=Path(pytorch_dump_folder_path, model_name), organization="nielsr", commit_message="Add model", use_temp_dir=True)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--checkpoint_url", default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth", type=str, help="URL to the original PyTorch checkpoint (.pth file).")
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model.")
    parser.add_argument("--push_to_hub", action="store_true")
    args = parser.parse_args()
    convert_dit_checkpoint(args.checkpoint_url, 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.                                                                 #
    #################################################################################################################################################################################################
"""
