code: 
  
  
  
  
  
  
  import argparse
  import json
  import os
  import re
  from collections import OrderedDict
  from dataclasses import dataclass, field
  from functools import partial
  from pathlib import Path
  from pprint import pprint
  from typing import Dict, List, Tuple
  
  import torch
  import torch.nn as nn
  from classy_vision.models.regnet import RegNet, RegNetParams
  from huggingface_hub import cached_download, hf_hub_url
  from torch import Tensor
  from vissl.models.model_helpers import get_trunk_forward_outputs
  
  from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							logging.get_logger()
  
  
  
  
  
  
  
  @dataclass
  class 							UpperCAmelCase_     :
       __lowerCamelCase										=     42
       __lowerCamelCase										=     field(default_factory=__lowerCamelCase		)
       __lowerCamelCase										=     field(default_factory=__lowerCamelCase		)
       __lowerCamelCase										=     field(default_factory=__lowerCamelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     List[str]										= len(list(m.modules()		)		) == 1 or isinstance(_lowerCAmelCase  ,	nn.Convad		) or isinstance(_lowerCAmelCase  ,	nn.BatchNormad		)
            if has_not_submodules:
                 self.traced.append(_lowerCAmelCase		)
                 UpperCAmelCase__			:     Optional[int]										= m
  
  
  
  
       def __call__(							self  ,	_lowerCAmelCase		):
            for name, m in self.module.named_modules():
                 self.handles.append(m.register_forward_hook(partial(self._forward_hook  ,	name=_lowerCAmelCase		)		)		)
            self.module(_lowerCAmelCase		)
            [x.remove() for x in self.handles]
            return self
  
  
  
  
       @property
       def      __UpperCAmelCase     (							self		):
            # check the len of the state_dict keys to see if we have learnable params
            return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()		)		) > 0}
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		nn.Module		):
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase		):
            super().__init__()
  
            UpperCAmelCase__			:     List[Tuple[str, nn.Module]]										= []
            # - get the stem
            feature_blocks.append(("""conv1""", model.stem)		)
            # - get all the feature blocks
            for k, v in model.trunk_output.named_children():
                 assert k.startswith("""block"""		), f"Unexpected layer name {k}"
                 UpperCAmelCase__			:     Dict										= len(_lowerCAmelCase		) + 1
                 feature_blocks.append((f"res{block_index}", v)		)
  
            UpperCAmelCase__			:     Optional[int]										= nn.ModuleDict(_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
            return get_trunk_forward_outputs(
                _lowerCAmelCase  ,	out_feat_keys=_lowerCAmelCase  ,	feature_blocks=self._feature_blocks  ,	)
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> Dict[str, str]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Dict										= RegNetConfig(depths=[2, 7, 17, 1]   ,       hidden_sizes=[8, 8, 8, 8]   ,       groups_width=8					)
       if "in1k" in model_name:
            UpperCAmelCase__			:     Optional[Any]										= RegNetForImageClassification(__lowerCamelCase					)
       else:
            UpperCAmelCase__			:     Optional[Any]										= RegNetModel(__lowerCamelCase					)
       # create from model (with small weights)
       UpperCAmelCase__			:     str										= FakeRegNetVisslWrapper(
           RegNet(FakeRegNetParams(depth=27   ,       group_width=1010   ,       w_a=1744   ,       w_a=620.83   ,       w_m=2.52					)					)					)
  
       with torch.no_grad():
            UpperCAmelCase__			:     List[Any]										= from_model.eval()
            UpperCAmelCase__			:     Dict										= our_model.eval()
  
            UpperCAmelCase__			:     Any										= torch.randn((1, 3, 32, 32)					)
            # trace both
            UpperCAmelCase__			:     str										= Tracker(__lowerCamelCase					)
            UpperCAmelCase__			:     Tuple										= dest_tracker(__lowerCamelCase					).parametrized
  
            pprint(dest_tracker.nameamodule					)
            UpperCAmelCase__			:     List[str]										= Tracker(__lowerCamelCase					)
            UpperCAmelCase__			:     List[str]										= src_tracker(__lowerCamelCase					).parametrized
  
       # convert the keys -> module dict to keys -> params
       def to_params_dict(__lowerCamelCase					):
            UpperCAmelCase__			:     Optional[Any]										= OrderedDict()
            for name, module in dict_with_modules.items():
                 for param_name, param in module.state_dict().items():
                      UpperCAmelCase__			:     Optional[int]										= param
            return params_dict
  
       UpperCAmelCase__			:     Optional[int]										= {}
  
       UpperCAmelCase__			:     int										= to_params_dict(__lowerCamelCase					)
       UpperCAmelCase__			:     Optional[int]										= to_params_dict(__lowerCamelCase					)
  
       for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()   ,       dst_state_dict.items()					):
            UpperCAmelCase__			:     Optional[int]										= dest_key
            logger.info(F"{src_key} -> {dest_key}"					)
       # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
       if "in1k" in model_name:
            UpperCAmelCase__			:     str										= """classifier.1.weight"""
            UpperCAmelCase__			:     Any										= """classifier.1.bias"""
  
       return from_to_ours_keys
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase = None   ,       __lowerCamelCase = True					)   -> Union[str, Any]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Dict										= """imagenet-1k-id2label.json"""
       UpperCAmelCase__			:     Optional[int]										= 1000
  
       UpperCAmelCase__			:     Tuple										= """huggingface/label-files"""
       UpperCAmelCase__			:     Union[str, Any]										= num_labels
       UpperCAmelCase__			:     Tuple										= json.load(open(cached_download(hf_hub_url(__lowerCamelCase   ,       __lowerCamelCase   ,       repo_type="""dataset"""					)					)   ,       """r"""					)					)
       UpperCAmelCase__			:     int										= {int(__lowerCamelCase					): v for k, v in idalabel.items()}
  
       UpperCAmelCase__			:     Optional[int]										= idalabel
       UpperCAmelCase__			:     str										= {v: k for k, v in idalabel.items()}
  
       UpperCAmelCase__			:     Optional[int]										= partial(__lowerCamelCase   ,       num_labels=__lowerCamelCase   ,       idalabel=__lowerCamelCase   ,       labelaid=__lowerCamelCase					)
  
       UpperCAmelCase__			:     List[str]										= {
           """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]   ,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]   ,       groups_width=1010					),
           # finetuned on imagenet
           """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]   ,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]   ,       groups_width=1010					),
       }
  
       # add seer weights logic
       def load_using_classy_vision(__lowerCamelCase					) -> Tuple[Dict, Dict]:
            UpperCAmelCase__			:     Optional[int]										= torch.hub.load_state_dict_from_url(__lowerCamelCase   ,       model_dir=str(__lowerCamelCase					)   ,       map_location="""cpu"""					)
            # check if we have a head, if yes add it
            UpperCAmelCase__			:     Optional[int]										= files["""classy_state_dict"""]["""base_model"""]["""model"""]
            return model_state_dict["trunk"], model_state_dict["heads"]
  
       UpperCAmelCase__			:     int										= {
           """regnet-y-10b-seer""": partial(
               __lowerCamelCase   ,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""   ,       ),
           """regnet-y-10b-seer-in1k""": partial(
               __lowerCamelCase   ,       """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""   ,       ),
       }
  
       UpperCAmelCase__			:     Union[str, Any]										= get_from_to_our_keys(__lowerCamelCase					)
  
       if not (save_directory / F"{model_name}.pth").exists():
            logger.info("""Loading original state_dict."""					)
            UpperCAmelCase__			,		UpperCAmelCase__			:     List[str]										= names_to_from_model[model_name]()
            UpperCAmelCase__			:     Dict										= from_state_dict_trunk
            if "in1k" in model_name:
                 # add the head
                 UpperCAmelCase__			:     Tuple										= {**from_state_dict_trunk, **from_state_dict_head}
            logger.info("""Done!"""					)
  
            UpperCAmelCase__			:     int										= {}
  
            UpperCAmelCase__			:     Dict										= list(from_state_dict.keys()					)
            UpperCAmelCase__			:     Any										= r"""\.block.-part."""
            # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
            for key in from_state_dict.keys():
                 # remove the weird "block[0,1]-part" from the key
                 UpperCAmelCase__			:     int										= re.sub(__lowerCamelCase   ,       """"""   ,       __lowerCamelCase					)
                 # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
                 UpperCAmelCase__			:     Optional[Any]										= from_to_ours_keys[src_key]
                 # store the parameter with our key
                 UpperCAmelCase__			:     Union[str, Any]										= from_state_dict[key]
                 not_used_keys.remove(__lowerCamelCase					)
            # check that all keys have been updated
            assert len(__lowerCamelCase					) == 0, F"Some keys where not used {','.join(__lowerCamelCase					)}"
  
            logger.info(F"The following keys were not used: {','.join(__lowerCamelCase					)}"					)
  
            # save our state dict to disk
            torch.save(__lowerCamelCase   ,       save_directory / F"{model_name}.pth"					)
  
            del converted_state_dict
       else:
            logger.info("""The state_dict was already stored on disk."""					)
       if push_to_hub:
            logger.info(F"Token is {os.environ['HF_TOKEN']}"					)
            logger.info("""Loading our model."""					)
            # create our model
            UpperCAmelCase__			:     Union[str, Any]										= names_to_config[model_name]
            UpperCAmelCase__			:     Tuple										= RegNetModel
            if "in1k" in model_name:
                 UpperCAmelCase__			:     Optional[Any]										= RegNetForImageClassification
            UpperCAmelCase__			:     Optional[int]										= our_model_func(__lowerCamelCase					)
            # place our model to the meta device (so remove all the weights)
            our_model.to(torch.device("""meta"""					)					)
            logger.info("""Loading state_dict in our model."""					)
            # load state dict
            UpperCAmelCase__			:     int										= our_model.state_dict().keys()
            PreTrainedModel._load_pretrained_model_low_mem(
                __lowerCamelCase   ,       __lowerCamelCase   ,       [save_directory / F"{model_name}.pth"]					)
            logger.info("""Finally, pushing!"""					)
            # push it to hub
            our_model.push_to_hub(
                repo_path_or_name=save_directory / model_name   ,       commit_message="""Add model"""   ,       output_dir=save_directory / model_name   ,       )
            UpperCAmelCase__			:     Optional[int]										= 384
            # we can use the convnext one
            UpperCAmelCase__			:     Tuple										= AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""   ,       size=__lowerCamelCase					)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / model_name   ,       commit_message="""Add image processor"""   ,       output_dir=save_directory / model_name   ,       )
  
  
  if __name__ == "__main__":
        SCREAMING_SNAKE_CASE__		:       Tuple   							=							argparse.ArgumentParser()
        # Required parameters
        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.""",
        )
  
        SCREAMING_SNAKE_CASE__		:       str   							=							parser.parse_args()
  
        SCREAMING_SNAKE_CASE__		:       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)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     List[Any]										= value
            UpperCAmelCase__			:     Node | None										= None
            UpperCAmelCase__			:     Node | None										= None
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
       __lowerCamelCase										=     42
  
  
  
  
       def __init__(							self		):
            UpperCAmelCase__			:     Any										= 0
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            if node is None:
                 return
  
            if path_sum == self.target:
                 self.paths += 1
  
            if node.left:
                 self.depth_first_search(node.left  ,	path_sum + node.left.value		)
            if node.right:
                 self.depth_first_search(node.right  ,	path_sum + node.right.value		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None		):
            if node is None:
                 return 0
            if target is not None:
                 UpperCAmelCase__			:     List[Any]										= target
  
            self.depth_first_search(_lowerCAmelCase  ,	node.value		)
            self.path_sum(node.left		)
            self.path_sum(node.right		)
  
            return self.paths
  
  
  if __name__ == "__main__":
        import doctest
  
        doctest.testmod()
  
  
  
  
  
  
  