


import operator as op







def     _a				(      lowercase__  :			List[str] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		lambda lowercase__     ,  lowercase__ : int(x / y )  # noqa: E731 integer division operation
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		{
         '^': op.pow,
         '*': op.mul,
         '/': div,
         '+': op.add,
         '-': op.sub,
     }  # operators & their respective operation

     # print table header
     print('Symbol'.center(8 )     ,  'Action'.center(12 )     ,  'Stack'     ,  sep=' | ' )
     print('-' * (30 + len(lowercase__ )) )

     for x in post_fix:
          if x.isdigit():  # if x in digit
               stack.append(lowercase__ )  # append x to stack
               # output in tabular format
               print(x.rjust(8 )     ,  ('push(' + x + ')').ljust(12 )     ,  ','.join(lowercase__ )     ,  sep=' | ' )
          else:
               SCREAMING_SNAKE_CASE__				:				Optional[int]			=		stack.pop()  # pop stack
               # output in tabular format
               print(''.rjust(8 )     ,  ('pop(' + b + ')').ljust(12 )     ,  ','.join(lowercase__ )     ,  sep=' | ' )

               SCREAMING_SNAKE_CASE__				:				Dict			=		stack.pop()  # pop stack
               # output in tabular format
               print(''.rjust(8 )     ,  ('pop(' + a + ')').ljust(12 )     ,  ','.join(lowercase__ )     ,  sep=' | ' )

               stack.append(
                   str(opr[x](int(lowercase__ )     ,  int(lowercase__ ) ) ) )  # evaluate the 2 values popped from stack & push result to stack
               # output in tabular format
               print(
                   x.rjust(8 )     ,  ('push(' + a + x + b + ')').ljust(12 )     ,  ','.join(lowercase__ )     ,  sep=' | '     ,  )

     return int(stack[0] )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:					Optional[Any]						=					input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
      print("\n\tResult = ", solve(Postfix))




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, RegNetYaagf, RegNetYaagf, RegNetYaaagf
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.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__		:					Tuple						=					logging.get_logger()

@dataclass
class       snake_case					:
  lowercase_			=     42
  lowercase_			=     field(default_factory=UpperCamelCase_						)
  lowercase_			=     field(default_factory=UpperCamelCase_						)




  def 	__lowercase(      self     :	Dict     , a_     :	Dict     , a_     :	Tensor     , a_     :	Tensor				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		len(list(m.modules()				)				) == 1 or isinstance(a_     , nn.Convad				) or isinstance(a_     , nn.BatchNormad				)
       if has_not_submodules:
            self.traced.append(a_				)




  def __call__(      self     :	Tuple     , a_     :	Tensor				)->       Any:




       """simple docstring"""
       for m in self.module.modules():
            self.handles.append(m.register_forward_hook(self._forward_hook				)				)
       self.module(a_				)
       [x.remove() for x in self.handles]
       return self




  @property
  def 	__lowercase(      self     :	Tuple				)->       int:




       """simple docstring"""
       # check the len of the state_dict keys to see if we have learnable params
       return list(filter(lambda a_				: len(list(x.state_dict().keys()				)				) > 0     , self.traced				)				)




@dataclass
class       snake_case					:
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     1
  lowercase_			=     field(default_factory=UpperCamelCase_						)
  lowercase_			=     field(default_factory=UpperCamelCase_						)
  lowercase_			=     True




  def __call__(      self     :	List[Any]     , a_     :	Tensor				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		Tracker(self.dest				)(a_				).parametrized
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		Tracker(self.src				)(a_				).parametrized

       SCREAMING_SNAKE_CASE__				:				List[str]			=		list(filter(lambda a_				: type(a_				) not in self.src_skip     , a_				)				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		list(filter(lambda a_				: type(a_				) not in self.dest_skip     , a_				)				)

       if len(a_				) != len(a_				) and self.raise_if_mismatch:
            raise Exception(
                F'''Numbers of operations are different. Source module has {len(a_				)} operations while'''
                F''' destination module has {len(a_				)}.'''				)

       for dest_m, src_m in zip(a_     , a_				):
            dest_m.load_state_dict(src_m.state_dict()				)
            if self.verbose == 1:
                 print(F'''Transfered from={src_m} to={dest_m}'''				)




class       snake_case					(      nn.Module						):




  def __init__(      self     :	List[Any]     , a_     :	nn.Module				)->       Dict:




       """simple docstring"""
       super().__init__()

       SCREAMING_SNAKE_CASE__				:				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}'''
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		len(a_				) + 1
            feature_blocks.append((F'''res{block_index}''', v)				)

       SCREAMING_SNAKE_CASE__				:				Any			=		nn.ModuleDict(a_				)




  def 	__lowercase(      self     :	Tuple     , a_     :	Tensor				)->       Dict:




       """simple docstring"""
       return get_trunk_forward_outputs(
           a_     , out_feat_keys=a_     , feature_blocks=self._feature_blocks     , )




class       snake_case					(      UpperCamelCase_						):




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	str				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		x.split('-'				)
       return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]				)




  def __getitem__(      self     :	Union[str, Any]     , a_     :	str				)->       Callable[[], Tuple[nn.Module, Dict]]:




       """simple docstring"""
       # default to timm!
       if x not in self:
            SCREAMING_SNAKE_CASE__				:				Any			=		self.convert_name_to_timm(a_				)
            SCREAMING_SNAKE_CASE__				:				Tuple			=		partial(lambda: (timm.create_model(a_     , pretrained=a_				).eval(), None)				)

       else:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		super().__getitem__(a_				)

       return val




class       snake_case					(      UpperCamelCase_						):




  def __getitem__(      self     :	Any     , a_     :	str				)->       Callable[[], nn.Module]:




       """simple docstring"""
       if "seer" in x and "in1k" not in x:
            SCREAMING_SNAKE_CASE__				:				Any			=		RegNetModel
       else:
            SCREAMING_SNAKE_CASE__				:				Any			=		RegNetForImageClassification
       return val







def     _a				(      lowercase__  :			Any     ,  lowercase__  :			Optional[Any]     ,  lowercase__  :			List[Tuple[str, str]] ):







     '''simple docstring'''


     for from_key, to_key in keys:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		from_state_dict[from_key].clone()
          print(f'''Copied key={from_key} to={to_key}''' )
     return to_state_dict







def     _a				(      lowercase__  :			str     ,  lowercase__  :			Callable[[], nn.Module]     ,  lowercase__  :			Callable[[], nn.Module]     ,  lowercase__  :			RegNetConfig     ,  lowercase__  :			Path     ,  lowercase__  :			bool = True     ,  ):







     '''simple docstring'''


     print(f'''Converting {name}...''' )
     with torch.no_grad():
          SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		from_model_func()
          SCREAMING_SNAKE_CASE__				:				int			=		our_model_func(lowercase__ ).eval()
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		ModuleTransfer(src=lowercase__     ,  dest=lowercase__     ,  raise_if_mismatch=lowercase__ )
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.randn((1, 3, 2_24, 2_24) )
          module_transfer(lowercase__ )

     if from_state_dict is not None:
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		[]
          # for seer - in1k finetuned we have to manually copy the head
          if "seer" in name and "in1k" in name:
               SCREAMING_SNAKE_CASE__				:				int			=		[('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		manually_copy_vissl_head(lowercase__     ,  our_model.state_dict()     ,  lowercase__ )
          our_model.load_state_dict(lowercase__ )

     SCREAMING_SNAKE_CASE__				:				Tuple			=		our_model(lowercase__     ,  output_hidden_states=lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Tuple			=		(
         our_outputs.logits if isinstance(lowercase__     ,  lowercase__ ) else our_outputs.last_hidden_state
     )

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		from_model(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[str]			=		from_output[-1] if type(lowercase__ ) is list else from_output

     # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
     if "seer" in name and "in1k" in name:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		our_outputs.hidden_states[-1]

     assert torch.allclose(lowercase__     ,  lowercase__ ), "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=lowercase__     ,  )

          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		2_24 if 'seer' not in name else 3_84
          # we can use the convnext one
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'     ,  size=lowercase__ )
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / name     ,  commit_message='Add image processor'     ,  use_temp_dir=lowercase__     ,  )

          print(f'''Pushed {name}''' )







def     _a				(      lowercase__  :			Path     ,  lowercase__  :			str = None     ,  lowercase__  :			bool = True ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		'imagenet-1k-id2label.json'
     SCREAMING_SNAKE_CASE__				:				Tuple			=		10_00
     SCREAMING_SNAKE_CASE__				:				Tuple			=		(1, num_labels)

     SCREAMING_SNAKE_CASE__				:				str			=		'huggingface/label-files'
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		num_labels
     SCREAMING_SNAKE_CASE__				:				List[str]			=		json.load(open(cached_download(hf_hub_url(lowercase__     ,  lowercase__     ,  repo_type='dataset' ) )     ,  'r' ) )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{int(lowercase__ ): v for k, v in idalabel.items()}

     SCREAMING_SNAKE_CASE__				:				str			=		idalabel
     SCREAMING_SNAKE_CASE__				:				Tuple			=		{v: k for k, v in idalabel.items()}

     SCREAMING_SNAKE_CASE__				:				Any			=		partial(lowercase__     ,  num_labels=lowercase__     ,  idalabel=lowercase__     ,  labelaid=lowercase__ )

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		{
         'regnet-x-002': ImageNetPreTrainedConfig(
             depths=[1, 1, 4, 7]     ,  hidden_sizes=[24, 56, 1_52, 3_68]     ,  groups_width=8     ,  layer_type='x' ),
         'regnet-x-004': ImageNetPreTrainedConfig(
             depths=[1, 2, 7, 12]     ,  hidden_sizes=[32, 64, 1_60, 3_84]     ,  groups_width=16     ,  layer_type='x' ),
         'regnet-x-006': ImageNetPreTrainedConfig(
             depths=[1, 3, 5, 7]     ,  hidden_sizes=[48, 96, 2_40, 5_28]     ,  groups_width=24     ,  layer_type='x' ),
         'regnet-x-008': ImageNetPreTrainedConfig(
             depths=[1, 3, 7, 5]     ,  hidden_sizes=[64, 1_28, 2_88, 6_72]     ,  groups_width=16     ,  layer_type='x' ),
         'regnet-x-016': ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 2]     ,  hidden_sizes=[72, 1_68, 4_08, 9_12]     ,  groups_width=24     ,  layer_type='x' ),
         'regnet-x-032': ImageNetPreTrainedConfig(
             depths=[2, 6, 15, 2]     ,  hidden_sizes=[96, 1_92, 4_32, 10_08]     ,  groups_width=48     ,  layer_type='x' ),
         'regnet-x-040': ImageNetPreTrainedConfig(
             depths=[2, 5, 14, 2]     ,  hidden_sizes=[80, 2_40, 5_60, 13_60]     ,  groups_width=40     ,  layer_type='x' ),
         'regnet-x-064': ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 1]     ,  hidden_sizes=[1_68, 3_92, 7_84, 16_24]     ,  groups_width=56     ,  layer_type='x' ),
         'regnet-x-080': ImageNetPreTrainedConfig(
             depths=[2, 5, 15, 1]     ,  hidden_sizes=[80, 2_40, 7_20, 19_20]     ,  groups_width=1_20     ,  layer_type='x' ),
         'regnet-x-120': ImageNetPreTrainedConfig(
             depths=[2, 5, 11, 1]     ,  hidden_sizes=[2_24, 4_48, 8_96, 22_40]     ,  groups_width=1_12     ,  layer_type='x' ),
         'regnet-x-160': ImageNetPreTrainedConfig(
             depths=[2, 6, 13, 1]     ,  hidden_sizes=[2_56, 5_12, 8_96, 20_48]     ,  groups_width=1_28     ,  layer_type='x' ),
         'regnet-x-320': ImageNetPreTrainedConfig(
             depths=[2, 7, 13, 1]     ,  hidden_sizes=[3_36, 6_72, 13_44, 25_20]     ,  groups_width=1_68     ,  layer_type='x' ),
         # y variant
         'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]     ,  hidden_sizes=[24, 56, 1_52, 3_68]     ,  groups_width=8 ),
         'regnet-y-004': ImageNetPreTrainedConfig(
             depths=[1, 3, 6, 6]     ,  hidden_sizes=[48, 1_04, 2_08, 4_40]     ,  groups_width=8 ),
         'regnet-y-006': ImageNetPreTrainedConfig(
             depths=[1, 3, 7, 4]     ,  hidden_sizes=[48, 1_12, 2_56, 6_08]     ,  groups_width=16 ),
         'regnet-y-008': ImageNetPreTrainedConfig(
             depths=[1, 3, 8, 2]     ,  hidden_sizes=[64, 1_28, 3_20, 7_68]     ,  groups_width=16 ),
         'regnet-y-016': ImageNetPreTrainedConfig(
             depths=[2, 6, 17, 2]     ,  hidden_sizes=[48, 1_20, 3_36, 8_88]     ,  groups_width=24 ),
         'regnet-y-032': ImageNetPreTrainedConfig(
             depths=[2, 5, 13, 1]     ,  hidden_sizes=[72, 2_16, 5_76, 15_12]     ,  groups_width=24 ),
         'regnet-y-040': ImageNetPreTrainedConfig(
             depths=[2, 6, 12, 2]     ,  hidden_sizes=[1_28, 1_92, 5_12, 10_88]     ,  groups_width=64 ),
         'regnet-y-064': ImageNetPreTrainedConfig(
             depths=[2, 7, 14, 2]     ,  hidden_sizes=[1_44, 2_88, 5_76, 12_96]     ,  groups_width=72 ),
         'regnet-y-080': ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 1]     ,  hidden_sizes=[1_68, 4_48, 8_96, 20_16]     ,  groups_width=56 ),
         'regnet-y-120': ImageNetPreTrainedConfig(
             depths=[2, 5, 11, 1]     ,  hidden_sizes=[2_24, 4_48, 8_96, 22_40]     ,  groups_width=1_12 ),
         'regnet-y-160': ImageNetPreTrainedConfig(
             depths=[2, 4, 11, 1]     ,  hidden_sizes=[2_24, 4_48, 12_32, 30_24]     ,  groups_width=1_12 ),
         'regnet-y-320': ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]     ,  hidden_sizes=[2_32, 6_96, 13_92, 37_12]     ,  groups_width=2_32 ),
         # models created by SEER -> https://arxiv.org/abs/2202.08360
         'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1]     ,  hidden_sizes=[2_32, 6_96, 13_92, 37_12]     ,  groups_width=2_32 ),
         'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]     ,  hidden_sizes=[3_28, 9_84, 19_68, 49_20]     ,  groups_width=3_28 ),
         'regnet-y-1280-seer': RegNetConfig(
             depths=[2, 7, 17, 1]     ,  hidden_sizes=[5_28, 10_56, 29_04, 73_92]     ,  groups_width=2_64 ),
         'regnet-y-2560-seer': RegNetConfig(
             depths=[3, 7, 16, 1]     ,  hidden_sizes=[6_40, 16_96, 25_44, 50_88]     ,  groups_width=6_40 ),
         'regnet-y-10b-seer': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]     ,  hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]     ,  groups_width=10_10 ),
         # finetuned on imagenet
         'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]     ,  hidden_sizes=[2_32, 6_96, 13_92, 37_12]     ,  groups_width=2_32 ),
         'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]     ,  hidden_sizes=[3_28, 9_84, 19_68, 49_20]     ,  groups_width=3_28 ),
         'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]     ,  hidden_sizes=[5_28, 10_56, 29_04, 73_92]     ,  groups_width=2_64 ),
         'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
             depths=[3, 7, 16, 1]     ,  hidden_sizes=[6_40, 16_96, 25_44, 50_88]     ,  groups_width=6_40 ),
         'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]     ,  hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]     ,  groups_width=10_10 ),
     }

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		NameToOurModelFuncMap()
     SCREAMING_SNAKE_CASE__				:				Dict			=		NameToFromModelFuncMap()
     # add seer weights logic

     def load_using_classy_vision(lowercase__  :			str     ,  lowercase__  :			Callable[[], nn.Module] ) -> Tuple[nn.Module, Dict]:
          SCREAMING_SNAKE_CASE__				:				str			=		torch.hub.load_state_dict_from_url(lowercase__     ,  model_dir=str(lowercase__ )     ,  map_location='cpu' )
          SCREAMING_SNAKE_CASE__				:				Tuple			=		model_func()
          # check if we have a head, if yes add it
          SCREAMING_SNAKE_CASE__				:				str			=		files['classy_state_dict']['base_model']['model']
          SCREAMING_SNAKE_CASE__				:				str			=		model_state_dict['trunk']
          model.load_state_dict(lowercase__ )
          return model.eval(), model_state_dict["heads"]

     # pretrained
     SCREAMING_SNAKE_CASE__				:				Any			=		partial(
         lowercase__     ,  'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'     ,  lambda: FakeRegNetVisslWrapper(RegNetYaagf() )     ,  )

     SCREAMING_SNAKE_CASE__				:				int			=		partial(
         lowercase__     ,  'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'     ,  lambda: FakeRegNetVisslWrapper(RegNetYaagf() )     ,  )

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		partial(
         lowercase__     ,  'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'     ,  lambda: FakeRegNetVisslWrapper(RegNetYaaagf() )     ,  )

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		partial(
         lowercase__     ,  'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'     ,  lambda: FakeRegNetVisslWrapper(
             RegNet(RegNetParams(depth=27     ,  group_width=10_10     ,  w_a=17_44     ,  w_a=620.83     ,  w_m=2.52 ) ) )     ,  )

     # IN1K finetuned
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		partial(
         lowercase__     ,  'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,  lambda: FakeRegNetVisslWrapper(RegNetYaagf() )     ,  )

     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		partial(
         lowercase__     ,  'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,  lambda: FakeRegNetVisslWrapper(RegNetYaagf() )     ,  )

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		partial(
         lowercase__     ,  'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,  lambda: FakeRegNetVisslWrapper(RegNetYaaagf() )     ,  )

     SCREAMING_SNAKE_CASE__				:				Any			=		partial(
         lowercase__     ,  '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=10_10     ,  w_a=17_44     ,  w_a=620.83     ,  w_m=2.52 ) ) )     ,  )

     if model_name:
          convert_weight_and_push(
              lowercase__     ,  names_to_from_model_map[model_name]     ,  names_to_ours_model_map[model_name]     ,  names_to_config[model_name]     ,  lowercase__     ,  lowercase__     ,  )
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(
                   lowercase__     ,  names_to_from_model_map[model_name]     ,  names_to_ours_model_map[model_name]     ,  lowercase__     ,  lowercase__     ,  lowercase__     ,  )
     return config, expected_shape


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:					str						=					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__		:					List[Any]						=					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)



