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()
  a     :			Optional[Any]					    =						logging.get_logger()
  
  @dataclass
  class 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Tuple        =						42
      SCREAMING_SNAKE_CASE:							Any        =						field(default_factory=lowerCamelCase__ )
      SCREAMING_SNAKE_CASE:							Any        =						field(default_factory=lowerCamelCase__ )
      SCREAMING_SNAKE_CASE:							Optional[Any]        =						field(default_factory=lowerCamelCase__ )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
          lowerCAmelCase_:       Optional[int]         =  len(list(m.modules()   )   ) == 1 or isinstance(SCREAMING_SNAKE_CASE_					,	nn.Convad   ) or isinstance(SCREAMING_SNAKE_CASE_					,	nn.BatchNormad   )
          if has_not_submodules:
              self.traced.append(SCREAMING_SNAKE_CASE_   )
              lowerCAmelCase_:       Union[str, Any]         =  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=SCREAMING_SNAKE_CASE_   )   )   )
          self.module(SCREAMING_SNAKE_CASE_   )
          [x.remove() for x in self.handles]
          return self
  
  
  
  
      @property
      def     _a						(						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 				_lowercase						(    nn.Module ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__   ):
          super().__init__()
  
          lowerCAmelCase_:       Union[str, Any]         =  []
          # - 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}'''
              lowerCAmelCase_:       Union[str, Any]         =  len(SCREAMING_SNAKE_CASE_   ) + 1
              feature_blocks.append((F'''res{block_index}''', v)   )
  
          lowerCAmelCase_:       Optional[int]         =  nn.ModuleDict(SCREAMING_SNAKE_CASE_   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
          return get_trunk_forward_outputs(
              SCREAMING_SNAKE_CASE_					,	out_feat_keys=SCREAMING_SNAKE_CASE_					,	feature_blocks=self._feature_blocks					,	)
  
  
  
  
  
  class 				_lowercase						(    lowerCamelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def     _a						(						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 		snake_case__  (		lowercase				):
      lowerCAmelCase_:       List[Any]         =  RegNetConfig(depths=[2, 7, 17, 1]       ,							hidden_sizes=[8, 8, 8, 8]       ,							groups_width=8				)
      if "in1k" in model_name:
          lowerCAmelCase_:       Optional[int]         =  RegNetForImageClassification(_UpperCAmelCase				)
      else:
          lowerCAmelCase_:       Optional[int]         =  RegNetModel(_UpperCAmelCase				)
      # create from model (with small weights)
      lowerCAmelCase_:       str         =  FakeRegNetVisslWrapper(
          RegNet(FakeRegNetParams(depth=27       ,							group_width=1010       ,							w_a=1744       ,							w_a=620.83       ,							w_m=2.52				)				)				)
  
      with torch.no_grad():
          lowerCAmelCase_:       int         =  from_model.eval()
          lowerCAmelCase_:       str         =  our_model.eval()
  
          lowerCAmelCase_:       List[str]         =  torch.randn((1, 3, 32, 32)				)
          # trace both
          lowerCAmelCase_:       Dict         =  Tracker(_UpperCAmelCase				)
          lowerCAmelCase_:       Dict         =  dest_tracker(_UpperCAmelCase				).parametrized
  
          pprint(dest_tracker.nameamodule				)
          lowerCAmelCase_:       List[str]         =  Tracker(_UpperCAmelCase				)
          lowerCAmelCase_:       int         =  src_tracker(_UpperCAmelCase				).parametrized
  
      # convert the keys -> module dict to keys -> params
      def to_params_dict(lowercase				):
          lowerCAmelCase_:       Union[str, Any]         =  OrderedDict()
          for name, module in dict_with_modules.items():
              for param_name, param in module.state_dict().items():
                  lowerCAmelCase_:       Tuple         =  param
          return params_dict
  
      lowerCAmelCase_:       int         =  {}
  
      lowerCAmelCase_:       int         =  to_params_dict(_UpperCAmelCase				)
      lowerCAmelCase_:       Tuple         =  to_params_dict(_UpperCAmelCase				)
  
      for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()       ,							dst_state_dict.items()				):
          lowerCAmelCase_:       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:
          lowerCAmelCase_:       Optional[Any]         =  "classifier.1.weight"
          lowerCAmelCase_:       Dict         =  "classifier.1.bias"
  
      return from_to_ours_keys
  def 		snake_case__  (		lowercase       ,							lowercase = None       ,							lowercase = True				):
      lowerCAmelCase_:       int         =  "imagenet-1k-id2label.json"
      lowerCAmelCase_:       Optional[int]         =  1000
  
      lowerCAmelCase_:       str         =  "huggingface/label-files"
      lowerCAmelCase_:       int         =  num_labels
      lowerCAmelCase_:       List[Any]         =  json.load(open(cached_download(hf_hub_url(_UpperCAmelCase       ,							_UpperCAmelCase       ,							repo_type="dataset"				)				)       ,							"r"				)				)
      lowerCAmelCase_:       int         =  {int(_UpperCAmelCase				): v for k, v in idalabel.items()}
  
      lowerCAmelCase_:       List[Any]         =  idalabel
      lowerCAmelCase_:       Tuple         =  {v: k for k, v in idalabel.items()}
  
      lowerCAmelCase_:       Dict         =  partial(_UpperCAmelCase       ,							num_labels=_UpperCAmelCase       ,							idalabel=_UpperCAmelCase       ,							labelaid=_UpperCAmelCase				)
  
      lowerCAmelCase_:       Optional[Any]         =  {
          "regnet-y-10b-seer": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]       ,							hidden_sizes=[2020, 4040, 11110, 28280]       ,							groups_width=1010				),
          # finetuned on imagenet
          "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]       ,							hidden_sizes=[2020, 4040, 11110, 28280]       ,							groups_width=1010				),
      }
  
      # add seer weights logic
      def load_using_classy_vision(lowercase				) -> Tuple[Dict, Dict]:
          lowerCAmelCase_:       Dict         =  torch.hub.load_state_dict_from_url(_UpperCAmelCase       ,							model_dir=str(_UpperCAmelCase				)       ,							map_location="cpu"				)
          # check if we have a head, if yes add it
          lowerCAmelCase_:       List[Any]         =  files["classy_state_dict"]["base_model"]["model"]
          return model_state_dict["trunk"], model_state_dict["heads"]
  
      lowerCAmelCase_:       int         =  {
          "regnet-y-10b-seer": partial(
              _UpperCAmelCase       ,							"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"       ,							),
          "regnet-y-10b-seer-in1k": partial(
              _UpperCAmelCase       ,							"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"       ,							),
      }
  
      lowerCAmelCase_:       List[Any]         =  get_from_to_our_keys(_UpperCAmelCase				)
  
      if not (save_directory / F'''{model_name}.pth''').exists():
          logger.info("Loading original state_dict."				)
          lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[int]         =  names_to_from_model[model_name]()
          lowerCAmelCase_:       int         =  from_state_dict_trunk
          if "in1k" in model_name:
              # add the head
              lowerCAmelCase_:       Optional[Any]         =  {**from_state_dict_trunk, **from_state_dict_head}
          logger.info("Done!"				)
  
          lowerCAmelCase_:       Dict         =  {}
  
          lowerCAmelCase_:       Optional[int]         =  list(from_state_dict.keys()				)
          lowerCAmelCase_:       Tuple         =  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
              lowerCAmelCase_:       Optional[Any]         =  re.sub(_UpperCAmelCase       ,							""       ,							_UpperCAmelCase				)
              # 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
              lowerCAmelCase_:       int         =  from_to_ours_keys[src_key]
              # store the parameter with our key
              lowerCAmelCase_:       List[str]         =  from_state_dict[key]
              not_used_keys.remove(_UpperCAmelCase				)
          # check that all keys have been updated
          assert len(_UpperCAmelCase				) == 0, F'''Some keys where not used {",".join(_UpperCAmelCase				)}'''
  
          logger.info(F'''The following keys were not used: {",".join(_UpperCAmelCase				)}'''				)
  
          # save our state dict to disk
          torch.save(_UpperCAmelCase       ,							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
          lowerCAmelCase_:       List[Any]         =  names_to_config[model_name]
          lowerCAmelCase_:       List[str]         =  RegNetModel
          if "in1k" in model_name:
              lowerCAmelCase_:       Dict         =  RegNetForImageClassification
          lowerCAmelCase_:       Tuple         =  our_model_func(_UpperCAmelCase				)
          # 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
          lowerCAmelCase_:       List[Any]         =  our_model.state_dict().keys()
          PreTrainedModel._load_pretrained_model_low_mem(
              _UpperCAmelCase       ,							_UpperCAmelCase       ,							[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       ,							)
          lowerCAmelCase_:       Union[str, Any]         =  384
          # we can use the convnext one
          lowerCAmelCase_:       Dict         =  AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"       ,							size=_UpperCAmelCase				)
          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__":
   a     :			Optional[int]					    =						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.""",
   )
  
   a     :			Union[str, Any]					    =						parser.parse_args()
  
   a     :			List[str]					    =						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: def 		snake_case__  (		lowercase = 998001				):
  
     # fetches the next number
     for number in range(n - 1       ,							9999       ,							-1				):
        lowerCAmelCase_:       str         =  str(lowercase				)
  
        # checks whether 'str_number' is a palindrome.
        if str_number == str_number[::-1]:
           lowerCAmelCase_:       List[str]         =  999
  
           # if 'number' is a product of two 3-digit numbers
           # then number is the answer otherwise fetch next number.
           while divisor != 99:
              if (number % divisor == 0) and (len(str(number // divisor				)				) == 3.0):
                 return number
              divisor -= 1
     raise ValueError("That number is larger than our acceptable range."				)
  
  
  if __name__ == "__main__":
   print(f'''{solution() = }''')