"""
    #################################################################################################################################################################################################
    # 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 Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetY32gf, RegNetY64gf, RegNetY128gf
from huggingface_hub import hf_hub_download
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from sapiens_transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
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 = 1
    src_skip: List = field(default_factory=list)
    dest_skip: List = field(default_factory=list)
    raise_if_mismatch: bool = True
    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) and self.raise_if_mismatch: 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}")
class FakeRegNetVisslWrapper(nn.Module):
    def __init__(self, model: nn.Module):
        super().__init__()
        feature_blocks: List[Tuple[str, nn.Module]] = []
        feature_blocks.append(("conv1", model.stem))
        for k, v in model.trunk_output.named_children():
            assert k.startswith("block"), f"Unexpected layer name {k}"
            block_index = len(feature_blocks) + 1
            feature_blocks.append((f"res{block_index}", v))
        self._feature_blocks = nn.ModuleDict(feature_blocks)
    def forward(self, x: Tensor): return get_trunk_forward_outputs(x, out_feat_keys=None, feature_blocks=self._feature_blocks)
class NameToFromModelFuncMap(dict):
    def convert_name_to_timm(self, x: str) -> str:
        x_split = x.split("-")
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:])
    def __getitem__(self, x: str) -> Callable[[], Tuple[nn.Module, Dict]]:
        if x not in self:
            x = self.convert_name_to_timm(x)
            val = partial(lambda: (timm.create_model(x, pretrained=True).eval(), None))
        else: val = super().__getitem__(x)
        return val
class NameToOurModelFuncMap(dict):
    def __getitem__(self, x: str) -> Callable[[], nn.Module]:
        if "seer" in x and "in1k" not in x: val = RegNetModel
        else: val = RegNetForImageClassification
        return val
def manually_copy_vissl_head(from_state_dict, to_state_dict, keys: List[Tuple[str, str]]):
    for from_key, to_key in keys:
        to_state_dict[to_key] = from_state_dict[from_key].clone()
        print(f"Copied key={from_key} to={to_key}")
    return to_state_dict
def convert_weight_and_push(name: str, from_model_func: Callable[[], nn.Module], our_model_func: Callable[[], nn.Module], config: RegNetConfig, save_directory: Path, push_to_hub: bool = True):
    print(f"Converting {name}...")
    with torch.no_grad():
        from_model, from_state_dict = from_model_func()
        our_model = our_model_func(config).eval()
        module_transfer = ModuleTransfer(src=from_model, dest=our_model, raise_if_mismatch=False)
        x = torch.randn((1, 3, 224, 224))
        module_transfer(x)
    if from_state_dict is not None:
        keys = []
        if "seer" in name and "in1k" in name: keys = [("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
        to_state_dict = manually_copy_vissl_head(from_state_dict, our_model.state_dict(), keys)
        our_model.load_state_dict(to_state_dict)
    our_outputs = our_model(x, output_hidden_states=True)
    our_output = (our_outputs.logits if isinstance(our_model, RegNetForImageClassification) else our_outputs.last_hidden_state)
    from_output = from_model(x)
    from_output = from_output[-1] if isinstance(from_output, list) else from_output
    if "seer" in name and "in1k" in name: our_output = our_outputs.hidden_states[-1]
    assert torch.allclose(from_output, our_output), "The model logits don't match the original one."
    if push_to_hub:
        our_model.push_to_hub(repo_path_or_name=save_directory / name, commit_message="Add model", use_temp_dir=True)
        size = 224 if "seer" not in name else 384
        image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k", size=size)
        image_processor.push_to_hub(repo_path_or_name=save_directory / name, commit_message="Add image processor", use_temp_dir=True)
        print(f"Pushed {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.loads(Path(hf_hub_download(repo_id, filename, repo_type="dataset")).read_text())
    id2label = {int(k): v for k, v in id2label.items()}
    id2label = id2label
    label2id = {v: k for k, v in id2label.items()}
    ImageNetPreTrainedConfig = partial(RegNetConfig, num_labels=num_labels, id2label=id2label, label2id=label2id)
    names_to_config = {"regnet-x-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8, layer_type="x"),
    "regnet-x-004": ImageNetPreTrainedConfig(depths=[1, 2, 7, 12], hidden_sizes=[32, 64, 160, 384], groups_width=16, layer_type="x"),
    "regnet-x-006": ImageNetPreTrainedConfig(depths=[1, 3, 5, 7], hidden_sizes=[48, 96, 240, 528], groups_width=24, layer_type="x"),
    "regnet-x-008": ImageNetPreTrainedConfig(depths=[1, 3, 7, 5], hidden_sizes=[64, 128, 288, 672], groups_width=16, layer_type="x"),
    "regnet-x-016": ImageNetPreTrainedConfig(depths=[2, 4, 10, 2], hidden_sizes=[72, 168, 408, 912], groups_width=24, layer_type="x"),
    "regnet-x-032": ImageNetPreTrainedConfig(depths=[2, 6, 15, 2], hidden_sizes=[96, 192, 432, 1008], groups_width=48, layer_type="x"),
    "regnet-x-040": ImageNetPreTrainedConfig(depths=[2, 5, 14, 2], hidden_sizes=[80, 240, 560, 1360], groups_width=40, layer_type="x"),
    "regnet-x-064": ImageNetPreTrainedConfig(depths=[2, 4, 10, 1], hidden_sizes=[168, 392, 784, 1624], groups_width=56, layer_type="x"),
    "regnet-x-080": ImageNetPreTrainedConfig(depths=[2, 5, 15, 1], hidden_sizes=[80, 240, 720, 1920], groups_width=120, layer_type="x"),
    "regnet-x-120": ImageNetPreTrainedConfig(depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112, layer_type="x"),
    "regnet-x-160": ImageNetPreTrainedConfig(depths=[2, 6, 13, 1], hidden_sizes=[256, 512, 896, 2048], groups_width=128, layer_type="x"),
    "regnet-x-320": ImageNetPreTrainedConfig(depths=[2, 7, 13, 1], hidden_sizes=[336, 672, 1344, 2520], groups_width=168, layer_type="x"),
    "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8),
    "regnet-y-004": ImageNetPreTrainedConfig(depths=[1, 3, 6, 6], hidden_sizes=[48, 104, 208, 440], groups_width=8),
    "regnet-y-006": ImageNetPreTrainedConfig(depths=[1, 3, 7, 4], hidden_sizes=[48, 112, 256, 608], groups_width=16),
    "regnet-y-008": ImageNetPreTrainedConfig(depths=[1, 3, 8, 2], hidden_sizes=[64, 128, 320, 768], groups_width=16),
    "regnet-y-016": ImageNetPreTrainedConfig(depths=[2, 6, 17, 2], hidden_sizes=[48, 120, 336, 888], groups_width=24),
    "regnet-y-032": ImageNetPreTrainedConfig(depths=[2, 5, 13, 1], hidden_sizes=[72, 216, 576, 1512], groups_width=24),
    "regnet-y-040": ImageNetPreTrainedConfig(depths=[2, 6, 12, 2], hidden_sizes=[128, 192, 512, 1088], groups_width=64),
    "regnet-y-064": ImageNetPreTrainedConfig(depths=[2, 7, 14, 2], hidden_sizes=[144, 288, 576, 1296], groups_width=72),
    "regnet-y-080": ImageNetPreTrainedConfig(depths=[2, 4, 10, 1], hidden_sizes=[168, 448, 896, 2016], groups_width=56),
    "regnet-y-120": ImageNetPreTrainedConfig(depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112),
    "regnet-y-160": ImageNetPreTrainedConfig(depths=[2, 4, 11, 1], hidden_sizes=[224, 448, 1232, 3024], groups_width=112),
    "regnet-y-320": ImageNetPreTrainedConfig(depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232),
    "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232),
    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328),
    "regnet-y-1280-seer": RegNetConfig(depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264),
    "regnet-y-2560-seer": RegNetConfig(depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640),
    "regnet-y-10b-seer": ImageNetPreTrainedConfig(depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010),
    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232),
    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328),
    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264),
    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640),
    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010)}
    names_to_ours_model_map = NameToOurModelFuncMap()
    names_to_from_model_map = NameToFromModelFuncMap()
    def load_using_classy_vision(checkpoint_url: str, model_func: Callable[[], nn.Module]) -> Tuple[nn.Module, Dict]:
        files = torch.hub.load_state_dict_from_url(checkpoint_url, model_dir=str(save_directory), map_location="cpu")
        model = model_func()
        model_state_dict = files["classy_state_dict"]["base_model"]["model"]
        state_dict = model_state_dict["trunk"]
        model.load_state_dict(state_dict)
        return model.eval(), model_state_dict["heads"]
    names_to_from_model_map["regnet-y-320-seer"] = partial(load_using_classy_vision, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch", lambda: FakeRegNetVisslWrapper(RegNetY32gf()))
    names_to_from_model_map["regnet-y-640-seer"] = partial(load_using_classy_vision, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch", lambda: FakeRegNetVisslWrapper(RegNetY64gf()))
    names_to_from_model_map["regnet-y-1280-seer"] = partial(load_using_classy_vision, "https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch", lambda: FakeRegNetVisslWrapper(RegNetY128gf()))
    names_to_from_model_map["regnet-y-10b-seer"] = partial(load_using_classy_vision, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch", lambda: FakeRegNetVisslWrapper(RegNet(RegNetParams(depth=27, group_width=1010, w_0=1744, w_a=620.83, w_m=2.52))))
    names_to_from_model_map["regnet-y-320-seer-in1k"] = partial(load_using_classy_vision, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch", lambda: FakeRegNetVisslWrapper(RegNetY32gf()))
    names_to_from_model_map["regnet-y-640-seer-in1k"] = partial(load_using_classy_vision, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch", lambda: FakeRegNetVisslWrapper(RegNetY64gf()))
    names_to_from_model_map["regnet-y-1280-seer-in1k"] = partial(load_using_classy_vision, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch", lambda: FakeRegNetVisslWrapper(RegNetY128gf()))
    names_to_from_model_map["regnet-y-10b-seer-in1k"] = partial(load_using_classy_vision, "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch", lambda: FakeRegNetVisslWrapper(RegNet(RegNetParams(depth=27, group_width=1010, w_0=1744, w_a=620.83, w_m=2.52))))
    if model_name: convert_weight_and_push(model_name, names_to_from_model_map[model_name], names_to_ours_model_map[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, names_to_from_model_map[model_name], names_to_ours_model_map[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 regnet* architecture, currently: regnetx-*, regnety-*. 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.                                                                 #
    #################################################################################################################################################################################################
"""
