# coding=utf-8
# 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.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)


def get_xclip_config(model_name, num_frames):
    text_config = XCLIPTextConfig()

    # derive patch size from model name
    start_idx = model_name.find("patch")
    patch_size = int(model_name[start_idx + len("patch") : start_idx + len("patch") + 2])
    vision_config = XCLIPVisionConfig(patch_size=patch_size, num_frames=num_frames)

    if "large" in model_name:
        text_config.hidden_size = 768
        text_config.intermediate_size = 3072
        text_config.num_attention_heads = 12

        vision_config.hidden_size = 1024
        vision_config.intermediate_size = 4096
        vision_config.num_attention_heads = 16
        vision_config.num_hidden_layers = 24
        vision_config.mit_hidden_size = 768
        vision_config.mit_intermediate_size = 3072

    if model_name == "xclip-large-patch14-16-frames":
        vision_config.image_size = 336

    config = XCLIPConfig(text_config=text_config, vision_config=vision_config)

    if "large" in model_name:
        config.projection_dim = 768

    return config


def rename_key(name):
    # text encoder
    if name == "token_embedding.weight":
        name = name.replace("token_embedding.weight", "text_model.embeddings.token_embedding.weight")
    if name == "positional_embedding":
        name = name.replace("positional_embedding", "text_model.embeddings.position_embedding.weight")
    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 name.startswith("transformer.resblocks"):
        name = name.replace("transformer.resblocks", "text_model.encoder.layers")
    if "attn.out_proj" in name and "message" not in name:
        name = name.replace("attn.out_proj", "self_attn.out_proj")
    if "ln_final" in name:
        name = name.replace("ln_final", "text_model.final_layer_norm")
    # visual encoder
    if name == "visual.class_embedding":
        name = name.replace("visual.class_embedding", "vision_model.embeddings.class_embedding")
    if name == "visual.positional_embedding":
        name = name.replace("visual.positional_embedding", "vision_model.embeddings.position_embedding.weight")
    if name.startswith("visual.transformer.resblocks"):
        name = name.replace("visual.transformer.resblocks", "vision_model.encoder.layers")
    if "visual.conv1" in name:
        name = name.replace("visual.conv1", "vision_model.embeddings.patch_embedding")
    if "visual.ln_pre" in name:
        name = name.replace("visual.ln_pre", "vision_model.pre_layernorm")
    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")
    # things on top
    if "prompts_visual_proj" in name:
        name = name.replace("prompts_visual_proj", "prompts_visual_projection")
    if "prompts_visual_ln" in name:
        name = name.replace("prompts_visual_ln", "prompts_visual_layernorm")
    # mit
    if name == "mit.positional_embedding":
        name = name.replace("positional", "position")
    if name.startswith("mit.resblocks"):
        name = name.replace("mit.resblocks", "mit.encoder.layers")
    # prompts generator
    if name.startswith("prompts_generator.norm"):
        name = name.replace("prompts_generator.norm", "prompts_generator.layernorm")

    return name


def convert_state_dict(orig_state_dict, config):
    for key in orig_state_dict.copy():
        val = orig_state_dict.pop(key)

        if "attn.in_proj" in key:
            key_split = key.split(".")
            if key.startswith("visual"):
                layer_num = key_split[3]
                dim = config.vision_config.hidden_size
                if "message_attn" in key:
                    if "weight" in key:
                        orig_state_dict[f"vision_model.encoder.layers.{layer_num}.message_attn.q_proj.weight"] = val[
                            :dim, :
                        ]
                        orig_state_dict[f"vision_model.encoder.layers.{layer_num}.message_attn.k_proj.weight"] = val[
                            dim : dim * 2, :
                        ]
                        orig_state_dict[f"vision_model.encoder.layers.{layer_num}.message_attn.v_proj.weight"] = val[
                            -dim:, :
                        ]
                    else:
                        orig_state_dict[f"vision_model.encoder.layers.{layer_num}.message_attn.q_proj.bias"] = val[
                            :dim
                        ]
                        orig_state_dict[f"vision_model.encoder.layers.{layer_num}.message_attn.k_proj.bias"] = val[
                            dim : dim * 2
                        ]
                        orig_state_dict[f"vision_model.encoder.layers.{layer_num}.message_attn.v_proj.bias"] = val[
                            -dim:
                        ]
                else:
                    if "weight" in key:
                        orig_state_dict[f"vision_model.encoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[
                            :dim, :
                        ]
                        orig_state_dict[f"vision_model.encoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[
                            dim : dim * 2, :
                        ]
                        orig_state_dict[f"vision_model.encoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[
                            -dim:, :
                        ]
                    else:
                        orig_state_dict[f"vision_model.encoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
                        orig_state_dict[f"vision_model.encoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[
                            dim : dim * 2
                        ]
                        orig_state_dict[f"vision_model.encoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
            elif key.startswith("mit"):
                layer_num = key_split[2]
                dim = config.vision_config.mit_hidden_size
                if "weight" in key:
                    orig_state_dict[f"mit.encoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[:dim, :]
                    orig_state_dict[f"mit.encoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[dim : dim * 2, :]
                    orig_state_dict[f"mit.encoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[-dim:, :]
                else:
                    orig_state_dict[f"mit.encoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
                    orig_state_dict[f"mit.encoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[dim : dim * 2]
                    orig_state_dict[f"mit.encoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
            else:
                layer_num = key_split[2]
                dim = config.text_config.hidden_size
                if "weight" in key:
                    orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.q_proj.weight"] = val[:dim, :]
                    orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.k_proj.weight"] = val[
                        dim : dim * 2, :
                    ]
                    orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.v_proj.weight"] = val[-dim:, :]
                else:
                    orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.q_proj.bias"] = val[:dim]
                    orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.k_proj.bias"] = val[
                        dim : dim * 2
                    ]
                    orig_state_dict[f"text_model.encoder.layers.{layer_num}.self_attn.v_proj.bias"] = val[-dim:]
        else:
            new_key_name = rename_key(key)
            if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                val = val.T
            orig_state_dict[new_key_name] = val

    return orig_state_dict


def prepare_video(num_frames):
    if num_frames == 8:
        filename = "eating_spaghetti_8_frames.npy"
    elif num_frames == 16:
        filename = "eating_spaghetti.npy"
    elif num_frames == 32:
        filename = "eating_spaghetti_32_frames.npy"
    file = hf_hub_download(
        repo_id="hf-internal-testing/spaghetti-video",
        filename=filename,
        repo_type="dataset",
    )
    video = np.load(file)
    return list(video)


def convert_xclip_checkpoint(model_name, pytorch_dump_folder_path=None, push_to_hub=False):
    model_to_url = {
        # fully supervised kinetics-400 checkpoints
        "xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
        "xclip-base-patch32-16-frames": (
            "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
        ),
        "xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
        "xclip-base-patch16-16-frames": (
            "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
        ),
        "xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
        "xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f",
        # fully supervised kinetics-600 checkpoints
        "xclip-base-patch16-kinetics-600": (
            "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
        ),
        "xclip-base-patch16-kinetics-600-16-frames": (
            "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
        ),
        "xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
        # few shot
        "xclip-base-patch16-hmdb-2-shot": (
            "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
        ),
        "xclip-base-patch16-hmdb-4-shot": (
            "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
        ),
        "xclip-base-patch16-hmdb-8-shot": (
            "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
        ),
        "xclip-base-patch16-hmdb-16-shot": (
            "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
        ),
        "xclip-base-patch16-ucf-2-shot": (
            "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
        ),
        "xclip-base-patch16-ucf-4-shot": (
            "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
        ),
        "xclip-base-patch16-ucf-8-shot": (
            "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
        ),
        "xclip-base-patch16-ucf-16-shot": (
            "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
        ),
        # zero shot
        "xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
    }

    checkpoint_url = model_to_url[model_name]
    num_frames = 8
    if "16-frames" in model_name:
        num_frames = 16
    elif "shot" in model_name:
        num_frames = 32

    config = get_xclip_config(model_name, num_frames)
    model = XCLIPModel(config)
    model.eval()

    if "drive" in checkpoint_url:
        output = "pytorch_model.bin"
        gdown.cached_download(checkpoint_url, output, quiet=False)
        state_dict = torch.load(output, map_location="cpu", weights_only=True)["model"]
    else:
        state_dict = torch.hub.load_state_dict_from_url(checkpoint_url)["model"]

    state_dict = convert_state_dict(state_dict, config)

    model = XCLIPModel(config)
    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
    assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
    model.eval()

    size = 336 if model_name == "xclip-large-patch14-16-frames" else 224
    image_processor = VideoMAEImageProcessor(size=size)
    slow_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
    fast_tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32")
    processor = XCLIPProcessor(image_processor=image_processor, tokenizer=fast_tokenizer)

    video = prepare_video(num_frames)
    inputs = processor(
        text=["playing sports", "eating spaghetti", "go shopping"], videos=video, return_tensors="pt", padding=True
    )

    print("Shape of pixel values:", inputs.pixel_values.shape)

    with torch.no_grad():
        outputs = model(**inputs)

    # Verify outputs
    logits_per_video = outputs.logits_per_video
    probs = logits_per_video.softmax(dim=1)
    print("Probs:", probs)
    # kinetics-400
    if model_name == "xclip-base-patch32":
        expected_probs = torch.tensor([[0.0019, 0.9951, 0.0030]])
    elif model_name == "xclip-base-patch32-16-frames":
        expected_probs = torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]])
    elif model_name == "xclip-base-patch16":
        expected_probs = torch.tensor([[0.0083, 0.9681, 0.0236]])
    elif model_name == "xclip-base-patch16-16-frames":
        expected_probs = torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]])
    elif model_name == "xclip-large-patch14":
        expected_probs = torch.tensor([[0.0062, 0.9864, 0.0075]])
    elif model_name == "xclip-large-patch14-16-frames":
        expected_probs = torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]])
    # kinetics-600
    elif model_name == "xclip-base-patch16-kinetics-600":
        expected_probs = torch.tensor([[0.0555, 0.8914, 0.0531]])
    elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
        expected_probs = torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]])
    elif model_name == "xclip-large-patch14-kinetics-600":
        expected_probs = torch.tensor([[0.0036, 0.9920, 0.0045]])
    # few shot
    elif model_name == "xclip-base-patch16-hmdb-2-shot":
        expected_probs = torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]])
    elif model_name == "xclip-base-patch16-hmdb-4-shot":
        expected_probs = torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]])
    elif model_name == "xclip-base-patch16-hmdb-8-shot":
        expected_probs = torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]])
    elif model_name == "xclip-base-patch16-hmdb-16-shot":
        expected_probs = torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]])
    elif model_name == "xclip-base-patch16-ucf-2-shot":
        expected_probs = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]])
    elif model_name == "xclip-base-patch16-ucf-4-shot":
        expected_probs = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]])
    elif model_name == "xclip-base-patch16-ucf-8-shot":
        expected_probs = torch.tensor([[0.0027, 0.9904, 0.0070]])
    elif model_name == "xclip-base-patch16-ucf-16-shot":
        expected_probs = torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]])
    # zero shot
    elif model_name == "xclip-base-patch16-zero-shot":
        expected_probs = torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]])
    else:
        raise ValueError(f"Model name {model_name} not supported")
    assert torch.allclose(probs, expected_probs, atol=1e-3)
    print("Looks ok!")

    if pytorch_dump_folder_path is not None:
        print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
        model.save_pretrained(pytorch_dump_folder_path)

    if push_to_hub:
        print("Pushing model, processor and slow tokenizer files to the hub...")
        model.push_to_hub(model_name, organization="nielsr")
        processor.push_to_hub(model_name, organization="nielsr")
        slow_tokenizer.push_to_hub(model_name, organization="nielsr")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--model_name",
        default="xclip-base-patch32",
        type=str,
        help="Name of the model.",
    )
    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 🤗 hub."
    )

    args = parser.parse_args()
    convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
