"""
    #################################################################################################################################################################################################
    # 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 sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor
from sapiens_transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from sapiens_transformers.models.deprecated.van.modeling_van import VanLayerScaling
from sapiens_transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
@dataclass
class Tracker:
    module: nn.Module
    traced: List[nn.Module] = field(default_factory=list)
    handles: list = field(default_factory=list)
    def _forward_hook(self, m, inputs: Tensor, outputs: Tensor):
        has_not_submodules = len(list(m.modules())) == 1 or isinstance(m, nn.Conv2d) or isinstance(m, nn.BatchNorm2d)
        if has_not_submodules:
            if not isinstance(m, VanLayerScaling): self.traced.append(m)
    def __call__(self, x: Tensor):
        for m in self.module.modules(): self.handles.append(m.register_forward_hook(self._forward_hook))
        self.module(x)
        [x.remove() for x in self.handles]
        return self
    @property
    def parametrized(self): return list(filter(lambda x: len(list(x.state_dict().keys())) > 0, self.traced))
@dataclass
class ModuleTransfer:
    src: nn.Module
    dest: nn.Module
    verbose: int = 0
    src_skip: List = field(default_factory=list)
    dest_skip: List = field(default_factory=list)
    def __call__(self, x: Tensor):
        dest_traced = Tracker(self.dest)(x).parametrized
        src_traced = Tracker(self.src)(x).parametrized
        src_traced = list(filter(lambda x: type(x) not in self.src_skip, src_traced))
        dest_traced = list(filter(lambda x: type(x) not in self.dest_skip, dest_traced))
        if len(dest_traced) != len(src_traced): raise Exception(f"Numbers of operations are different. Source module has {len(src_traced)} operations while destination module has {len(dest_traced)}.")
        for dest_m, src_m in zip(dest_traced, src_traced):
            dest_m.load_state_dict(src_m.state_dict())
            if self.verbose == 1: print(f"Transfered from={src_m} to={dest_m}")
def copy_parameters(from_model: nn.Module, our_model: nn.Module) -> nn.Module:
    from_state_dict = from_model.state_dict()
    our_state_dict = our_model.state_dict()
    config = our_model.config
    all_keys = []
    for stage_idx in range(len(config.hidden_sizes)):
        for block_id in range(config.depths[stage_idx]):
            from_key = f"block{stage_idx + 1}.{block_id}.layer_scale_1"
            to_key = f"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"
            all_keys.append((from_key, to_key))
            from_key = f"block{stage_idx + 1}.{block_id}.layer_scale_2"
            to_key = f"van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"
            all_keys.append((from_key, to_key))
    for from_key, to_key in all_keys: our_state_dict[to_key] = from_state_dict.pop(from_key)
    our_model.load_state_dict(our_state_dict)
    return our_model
def convert_weight_and_push(name: str, config: VanConfig, checkpoint: str, from_model: nn.Module, save_directory: Path, push_to_hub: bool = True):
    print(f"Downloading weights for {name}...")
    checkpoint_path = cached_download(checkpoint)
    print(f"Converting {name}...")
    from_state_dict = torch.load(checkpoint_path)["state_dict"]
    from_model.load_state_dict(from_state_dict)
    from_model.eval()
    with torch.no_grad():
        our_model = VanForImageClassification(config).eval()
        module_transfer = ModuleTransfer(src=from_model, dest=our_model)
        x = torch.randn((1, 3, 224, 224))
        module_transfer(x)
        our_model = copy_parameters(from_model, our_model)
    if not torch.allclose(from_model(x), our_model(x).logits): raise ValueError("The model logits don't match the original one.")
    checkpoint_name = name
    print(checkpoint_name)
    if push_to_hub:
        our_model.push_to_hub(repo_path_or_name=save_directory / checkpoint_name, commit_message="Add model", use_temp_dir=True)
        image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k")
        image_processor.push_to_hub(repo_path_or_name=save_directory / checkpoint_name, commit_message="Add image processor", use_temp_dir=True)
        print(f"Pushed {checkpoint_name}")
def convert_weights_and_push(save_directory: Path, model_name: str = None, push_to_hub: bool = True):
    filename = "imagenet-1k-id2label.json"
    num_labels = 1000
    repo_id = "huggingface/label-files"
    num_labels = num_labels
    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
    id2label = {int(k): v for k, v in id2label.items()}
    id2label = id2label
    label2id = {v: k for k, v in id2label.items()}
    ImageNetPreTrainedConfig = partial(VanConfig, num_labels=num_labels, id2label=id2label, label2id=label2id)
    names_to_config = {"van-tiny": ImageNetPreTrainedConfig(hidden_sizes=[32, 64, 160, 256], depths=[3, 3, 5, 2], mlp_ratios=[8, 8, 4, 4]),
    "van-small": ImageNetPreTrainedConfig(hidden_sizes=[64, 128, 320, 512], depths=[2, 2, 4, 2], mlp_ratios=[8, 8, 4, 4]),
    "van-base": ImageNetPreTrainedConfig(hidden_sizes=[64, 128, 320, 512], depths=[3, 3, 12, 3], mlp_ratios=[8, 8, 4, 4]),
    "van-large": ImageNetPreTrainedConfig(hidden_sizes=[64, 128, 320, 512], depths=[3, 5, 27, 3], mlp_ratios=[8, 8, 4, 4])}
    names_to_original_models = {"van-tiny": van_tiny, "van-small": van_small, "van-base": van_base, "van-large": van_large}
    names_to_original_checkpoints = {'van-tiny': 'https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar', 'van-small': 'https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar', 'van-base': 'https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar', 'van-large': 'https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'}
    if model_name: convert_weight_and_push(model_name, names_to_config[model_name], checkpoint=names_to_original_checkpoints[model_name], from_model=names_to_original_models[model_name](), save_directory=save_directory, push_to_hub=push_to_hub)
    else:
        for model_name, config in names_to_config.items(): convert_weight_and_push(model_name, config, checkpoint=names_to_original_checkpoints[model_name], from_model=names_to_original_models[model_name](), save_directory=save_directory, push_to_hub=push_to_hub)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-name", default=None, type=str, help=("The name of the model you wish to convert, it must be one of the supported resnet* architecture, currently: van-tiny/small/base/large. If `None`, all of them will the converted."))
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=Path, required=True, help="Path to the output PyTorch model directory.")
    parser.add_argument("--van_dir", required=True, type=Path, help=("A path to VAN's original implementation directory. You can download from here: https://github.com/Visual-Attention-Network/VAN-Classification"))
    parser.add_argument("--push_to_hub", default=True, type=bool, required=False, help="If True, push model and image processor to the hub.")
    args = parser.parse_args()
    pytorch_dump_folder_path: Path = args.pytorch_dump_folder_path
    pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
    van_dir = args.van_dir
    sys.path.append(str(van_dir.parent))
    from van.models.van import van_base, van_large, van_small, van_tiny
    convert_weights_and_push(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.                                                                 #
    #################################################################################################################################################################################################
"""
