"""
    #################################################################################################################################################################################################
    # 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
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from sapiens_transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from sapiens_transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger()
@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: 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 convert_weight_and_push(name: str, config: ResNetConfig, save_directory: Path, push_to_hub: bool = True):
    print(f"Converting {name}...")
    with torch.no_grad():
        from_model = timm.create_model(name, pretrained=True).eval()
        our_model = ResNetForImageClassification(config).eval()
        module_transfer = ModuleTransfer(src=from_model, dest=our_model)
        x = torch.randn((1, 3, 224, 224))
        module_transfer(x)
    assert torch.allclose(from_model(x), our_model(x).logits), "The model logits don't match the original one."
    checkpoint_name = f"resnet{'-'.join(name.split('resnet'))}"
    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
    expected_shape = (1, num_labels)
    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(ResNetConfig, num_labels=num_labels, id2label=id2label, label2id=label2id)
    names_to_config = {"resnet18": ImageNetPreTrainedConfig(depths=[2, 2, 2, 2], hidden_sizes=[64, 128, 256, 512], layer_type="basic"),
    "resnet26": ImageNetPreTrainedConfig(depths=[2, 2, 2, 2], hidden_sizes=[256, 512, 1024, 2048], layer_type="bottleneck"),
    "resnet34": ImageNetPreTrainedConfig(depths=[3, 4, 6, 3], hidden_sizes=[64, 128, 256, 512], layer_type="basic"),
    "resnet50": ImageNetPreTrainedConfig(depths=[3, 4, 6, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type="bottleneck"),
    "resnet101": ImageNetPreTrainedConfig(depths=[3, 4, 23, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type="bottleneck"),
    "resnet152": ImageNetPreTrainedConfig(depths=[3, 8, 36, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type="bottleneck")}
    if model_name: convert_weight_and_push(model_name, names_to_config[model_name], save_directory, push_to_hub)
    else:
        for model_name, config in names_to_config.items(): convert_weight_and_push(model_name, config, save_directory, push_to_hub)
    return config, expected_shape
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: resnet18,26,34,50,101,152. 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("--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)
    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.                                                                 #
    #################################################################################################################################################################################################
"""
