"""
    #################################################################################################################################################################################################
    # 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
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from sapiens_transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor
def get_timesformer_config(model_name):
    config = TimesformerConfig()
    if "large" in model_name: config.num_frames = 96
    if "hr" in model_name:
        config.num_frames = 16
        config.image_size = 448
    repo_id = "huggingface/label-files"
    if "k400" in model_name:
        config.num_labels = 400
        filename = "kinetics400-id2label.json"
    elif "k600" in model_name:
        config.num_labels = 600
        filename = "kinetics600-id2label.json"
    elif "ssv2" in model_name:
        config.num_labels = 174
        filename = "something-something-v2-id2label.json"
    else: raise ValueError("Model name should either contain 'k400', 'k600' or 'ssv2'.")
    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()}
    return config
def rename_key(name):
    if "encoder." in name: name = name.replace("encoder.", "")
    if "cls_token" in name: name = name.replace("cls_token", "timesformer.embeddings.cls_token")
    if "pos_embed" in name: name = name.replace("pos_embed", "timesformer.embeddings.position_embeddings")
    if "time_embed" in name: name = name.replace("time_embed", "timesformer.embeddings.time_embeddings")
    if "patch_embed.proj" in name: name = name.replace("patch_embed.proj", "timesformer.embeddings.patch_embeddings.projection")
    if "patch_embed.norm" in name: name = name.replace("patch_embed.norm", "timesformer.embeddings.norm")
    if "blocks" in name: name = name.replace("blocks", "timesformer.encoder.layer")
    if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense")
    if "attn" in name and "bias" not in name and "temporal" not in name: name = name.replace("attn", "attention.self")
    if "attn" in name and "temporal" not in name: name = name.replace("attn", "attention.attention")
    if "temporal_norm1" in name: name = name.replace("temporal_norm1", "temporal_layernorm")
    if "temporal_attn.proj" in name: name = name.replace("temporal_attn", "temporal_attention.output.dense")
    if "temporal_fc" in name: name = name.replace("temporal_fc", "temporal_dense")
    if "norm1" in name and "temporal" not in name: name = name.replace("norm1", "layernorm_before")
    if "norm2" in name: name = name.replace("norm2", "layernorm_after")
    if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense")
    if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense")
    if "norm.weight" in name and "fc" not in name and "temporal" not in name: name = name.replace("norm.weight", "timesformer.layernorm.weight")
    if "norm.bias" in name and "fc" not in name and "temporal" not in name: name = name.replace("norm.bias", "timesformer.layernorm.bias")
    if "head" in name: name = name.replace("head", "classifier")
    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("model."): key = key.replace("model.", "")
        if "qkv" in key:
            key_split = key.split(".")
            layer_num = int(key_split[1])
            prefix = "timesformer.encoder.layer."
            if "temporal" in key: postfix = ".temporal_attention.attention.qkv."
            else: postfix = ".attention.attention.qkv."
            if "weight" in key: orig_state_dict[f"{prefix}{layer_num}{postfix}weight"] = val
            else: orig_state_dict[f"{prefix}{layer_num}{postfix}bias"] = val
        else: orig_state_dict[rename_key(key)] = val
    return orig_state_dict
def prepare_video():
    file = hf_hub_download(repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti.npy", repo_type="dataset")
    video = np.load(file)
    return list(video)
def convert_timesformer_checkpoint(checkpoint_url, pytorch_dump_folder_path, model_name, push_to_hub):
    config = get_timesformer_config(model_name)
    model = TimesformerForVideoClassification(config)
    output = "pytorch_model.bin"
    gdown.cached_download(checkpoint_url, output, quiet=False)
    files = torch.load(output, map_location="cpu")
    if "model" in files: state_dict = files["model"]
    elif "module" in files: state_dict = files["module"]
    else: state_dict = files["model_state"]
    new_state_dict = convert_state_dict(state_dict, config)
    model.load_state_dict(new_state_dict)
    model.eval()
    image_processor = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5])
    video = prepare_video()
    inputs = image_processor(video[:8], return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits
    model_names = ["timesformer-base-finetuned-k400", "timesformer-large-finetuned-k400", "timesformer-hr-finetuned-k400", "timesformer-base-finetuned-k600",
    "timesformer-large-finetuned-k600", "timesformer-hr-finetuned-k600", "timesformer-base-finetuned-ssv2", "timesformer-large-finetuned-ssv2", "timesformer-hr-finetuned-ssv2"]
    if model_name == "timesformer-base-finetuned-k400":
        expected_shape = torch.Size([1, 400])
        expected_slice = torch.tensor([-0.3016, -0.7713, -0.4205])
    elif model_name == "timesformer-base-finetuned-k600":
        expected_shape = torch.Size([1, 600])
        expected_slice = torch.tensor([-0.7267, -0.7466, 3.2404])
    elif model_name == "timesformer-base-finetuned-ssv2":
        expected_shape = torch.Size([1, 174])
        expected_slice = torch.tensor([-0.9059, 0.6433, -3.1457])
    elif model_name == "timesformer-large-finetuned-k400":
        expected_shape = torch.Size([1, 400])
        expected_slice = torch.tensor([0, 0, 0])
    elif model_name == "timesformer-large-finetuned-k600":
        expected_shape = torch.Size([1, 600])
        expected_slice = torch.tensor([0, 0, 0])
    elif model_name == "timesformer-large-finetuned-ssv2":
        expected_shape = torch.Size([1, 174])
        expected_slice = torch.tensor([0, 0, 0])
    elif model_name == "timesformer-hr-finetuned-k400":
        expected_shape = torch.Size([1, 400])
        expected_slice = torch.tensor([-0.9617, -3.7311, -3.7708])
    elif model_name == "timesformer-hr-finetuned-k600":
        expected_shape = torch.Size([1, 600])
        expected_slice = torch.tensor([2.5273, 0.7127, 1.8848])
    elif model_name == "timesformer-hr-finetuned-ssv2":
        expected_shape = torch.Size([1, 174])
        expected_slice = torch.tensor([-3.6756, -0.7513, 0.7180])
    else: raise ValueError(f"Model name not supported. Should be one of {model_names}")
    assert logits.shape == expected_shape
    assert torch.allclose(logits[0, :3], expected_slice, atol=1e-4)
    print("Logits ok!")
    if pytorch_dump_folder_path is not None:
        print(f"Saving model and image processor to {pytorch_dump_folder_path}")
        image_processor.save_pretrained(pytorch_dump_folder_path)
        model.save_pretrained(pytorch_dump_folder_path)
    if push_to_hub:
        print("Pushing to the hub...")
        model.push_to_hub(f"fcakyon/{model_name}")
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--checkpoint_url", default="https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download", type=str, help=("URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct download link."))
    parser.add_argument("--pytorch_dump_folder_path", default="", type=str, help="Path to the output PyTorch model directory.")
    parser.add_argument("--model_name", default="timesformer-base-finetuned-k400", type=str, help="Name of the model.")
    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_timesformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, 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.                                                                 #
    #################################################################################################################################################################################################
"""
