# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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"""Convert CLIPSeg checkpoints from the original repository. URL: https://github.com/timojl/clipseg."""

import argparse

import requests
import torch
from PIL import Image

from 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 = "refined" in model_name
    reduce_dim = 16 if "rd16" in model_name else 64

    config = CLIPSegConfig(
        text_config=text_config,
        vision_config=vision_config,
        use_complex_transposed_convolution=use_complex_transposed_convolution,
        reduce_dim=reduce_dim,
    )
    return config


def rename_key(name):
    # update prefixes
    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")
    # text encoder
    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")
    # vision encoder
    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")
    # projection layers
    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")
    # decoder
    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():
        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


# We will verify our results on an image of cute cats
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", weights_only=True)

    # remove some keys
    for key in state_dict.copy():
        if key.startswith("model"):
            state_dict.pop(key, None)

    # rename some keys
    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(f"Missing keys that are not expected: {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)

    # verify values
    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()
    # Required parameters
    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 Hugging Face hub.",
    )

    args = parser.parse_args()
    convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
