"""
    #################################################################################################################################################################################################
    # 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 requests
import torch
from PIL import Image
from sapiens_transformers import (CLIPSegConfig, CLIPSegForImageSegmentation, CLIPSegProcessor, CLIPSegTextConfig, CLIPSegVisionConfig, CLIPTokenizer, ViTImageProcessor)
def get_clipseg_config(model_name):
    text_config = CLIPSegTextConfig()
    vision_config = CLIPSegVisionConfig(patch_size=16)
    use_complex_transposed_convolution = True if "refined" in model_name else False
    reduce_dim = 16 if "rd16" in model_name else 64
    config = CLIPSegConfig.from_text_vision_configs(text_config, vision_config, use_complex_transposed_convolution=use_complex_transposed_convolution, reduce_dim=reduce_dim)
    return config
def rename_key(name):
    if "clip_model" in name: name = name.replace("clip_model", "clip")
    if "transformer" in name:
        if "visual" in name: name = name.replace("visual.transformer", "vision_model")
        else: name = name.replace("transformer", "text_model")
    if "resblocks" in name: name = name.replace("resblocks", "encoder.layers")
    if "ln_1" in name: name = name.replace("ln_1", "layer_norm1")
    if "ln_2" in name: name = name.replace("ln_2", "layer_norm2")
    if "c_fc" in name: name = name.replace("c_fc", "fc1")
    if "c_proj" in name: name = name.replace("c_proj", "fc2")
    if "attn" in name and "self" not in name: name = name.replace("attn", "self_attn")
    if "token_embedding" in name: name = name.replace("token_embedding", "text_model.embeddings.token_embedding")
    if "positional_embedding" in name and "visual" not in name: name = name.replace("positional_embedding", "text_model.embeddings.position_embedding.weight")
    if "ln_final" in name: name = name.replace("ln_final", "text_model.final_layer_norm")
    if "visual.class_embedding" in name: name = name.replace("visual.class_embedding", "vision_model.embeddings.class_embedding")
    if "visual.conv1" in name: name = name.replace("visual.conv1", "vision_model.embeddings.patch_embedding")
    if "visual.positional_embedding" in name: name = name.replace("visual.positional_embedding", "vision_model.embeddings.position_embedding.weight")
    if "visual.ln_pre" in name: name = name.replace("visual.ln_pre", "vision_model.pre_layrnorm")
    if "visual.ln_post" in name: name = name.replace("visual.ln_post", "vision_model.post_layernorm")
    if "visual.proj" in name: name = name.replace("visual.proj", "visual_projection.weight")
    if "text_projection" in name: name = name.replace("text_projection", "text_projection.weight")
    if "trans_conv" in name: name = name.replace("trans_conv", "transposed_convolution")
    if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name: name = "decoder." + name
    if "blocks" in name: name = name.replace("blocks", "decoder.layers")
    if "linear1" in name: name = name.replace("linear1", "mlp.fc1")
    if "linear2" in name: name = name.replace("linear2", "mlp.fc2")
    if "norm1" in name and "layer_" not in name: name = name.replace("norm1", "layer_norm1")
    if "norm2" in name and "layer_" not in name: name = name.replace("norm2", "layer_norm2")
    return name
def convert_state_dict(orig_state_dict, config):
    for key in orig_state_dict.copy().keys():
        val = orig_state_dict.pop(key)
        if key.startswith("clip_model") and "attn.in_proj" in key:
            key_split = key.split(".")
            if "visual" in key:
                layer_num = int(key_split[4])
                dim = config.vision_config.hidden_size
                prefix = "vision_model"
            else:
                layer_num = int(key_split[3])
                dim = config.text_config.hidden_size
                prefix = "text_model"
            if "weight" in key:
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[:dim, :]
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[dim : dim * 2, :]
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[-dim:, :]
            else:
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[dim : dim * 2]
                orig_state_dict[f"clip.{prefix}.encoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
        elif "self_attn" in key and "out_proj" not in key:
            key_split = key.split(".")
            layer_num = int(key_split[1])
            dim = config.reduce_dim
            if "weight" in key:
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[:dim, :]
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[dim : dim * 2, :]
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[-dim:, :]
            else:
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[dim : dim * 2]
                orig_state_dict[f"decoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
        else:
            new_name = rename_key(key)
            if "visual_projection" in new_name or "text_projection" in new_name: val = val.T
            orig_state_dict[new_name] = val
    return orig_state_dict
def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    return image
def convert_clipseg_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub):
    config = get_clipseg_config(model_name)
    model = CLIPSegForImageSegmentation(config)
    model.eval()
    state_dict = torch.load(checkpoint_path, map_location="cpu")
    for key in state_dict.copy().keys():
        if key.startswith("model"): state_dict.pop(key, None)
    state_dict = convert_state_dict(state_dict, config)
    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
    if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]: raise ValueError("Missing keys that are not expected: {}".format(missing_keys))
    if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]: raise ValueError(f"Unexpected keys: {unexpected_keys}")
    image_processor = ViTImageProcessor(size=352)
    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
    processor = CLIPSegProcessor(image_processor=image_processor, tokenizer=tokenizer)
    image = prepare_img()
    text = ["a glass", "something to fill", "wood", "a jar"]
    inputs = processor(text=text, images=[image] * len(text), padding="max_length", return_tensors="pt")
    with torch.no_grad(): outputs = model(**inputs)
    expected_conditional = torch.tensor([0.1110, -0.1882, 0.1645])
    expected_pooled_output = torch.tensor([0.2692, -0.7197, -0.1328])
    if model_name == "clipseg-rd64-refined": expected_masks_slice = torch.tensor([[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]])
    elif model_name == "clipseg-rd64": expected_masks_slice = torch.tensor([[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]])
    elif model_name == "clipseg-rd16": expected_masks_slice = torch.tensor([[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]])
    else: raise ValueError(f"Model name {model_name} not supported.")
    assert torch.allclose(outputs.logits[0, :3, :3], expected_masks_slice, atol=1e-3)
    assert torch.allclose(outputs.conditional_embeddings[0, :3], expected_conditional, atol=1e-3)
    assert torch.allclose(outputs.pooled_output[0, :3], expected_pooled_output, atol=1e-3)
    print("Looks ok!")
    if pytorch_dump_folder_path is not None:
        print(f"Saving model and processor to {pytorch_dump_folder_path}")
        model.save_pretrained(pytorch_dump_folder_path)
        processor.save_pretrained(pytorch_dump_folder_path)
    if push_to_hub:
        print(f"Pushing model and processor for {model_name} to the hub")
        model.push_to_hub(f"CIDAS/{model_name}")
        processor.push_to_hub(f"CIDAS/{model_name}")
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_name", default="clipseg-rd64", type=str, choices=["clipseg-rd16", "clipseg-rd64", "clipseg-rd64-refined"], help=("Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning reduce dimension)"))
    parser.add_argument("--checkpoint_path", default="/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth", type=str, help=("Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and the decoder weights."))
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory.")
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the converted model to the HF hub.")
    args = parser.parse_args()
    convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, 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.                                                                 #
    #################################################################################################################################################################################################
"""
