


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									=		logging.get_logger()






@dataclass
class        UpperCAmelCase__			:



    """simple docstring"""






    UpperCAmelCase__		:						nn.Module
    UpperCAmelCase__		:						List[nn.Module]    =    field(default_factory=_a						)
    UpperCAmelCase__		:						list    =    field(default_factory=_a						)
    UpperCAmelCase__		:						Dict[str, nn.Module]    =    field(default_factory=_a						)






    def _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_       )    ->			Any:
         __UpperCamelCase       				=len(list(m.modules()       )       ) == 1 or isinstance(lowerCAmelCase__   ,  nn.Convad       ) or isinstance(lowerCAmelCase__   ,  nn.BatchNormad       )
         if has_not_submodules:
              self.traced.append(lowerCAmelCase__       )
              __UpperCamelCase       				=m






    def __call__(     self   ,  A_       )    ->			Any:
         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 _a   (     self       )    ->			Any:
         # 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						):



    """simple docstring"""






    def __init__(     self   ,  A_       )    ->			Union[str, Any]:
         super().__init__()

         __UpperCamelCase       				=[]
         # - 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       				=len(lowerCAmelCase__       ) + 1
              feature_blocks.append((f'res{block_index}', v)       )

         __UpperCamelCase       				=nn.ModuleDict(lowerCAmelCase__       )






    def _a   (     self   ,  A_       )    ->			int:
         return get_trunk_forward_outputs(
             lowerCAmelCase__   ,  out_feat_keys=lowerCAmelCase__   ,  feature_blocks=self._feature_blocks   ,  )






class        UpperCAmelCase__			(   _a						):



    """simple docstring"""






    def _a   (     self       )    ->			Optional[Any]:
         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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]							):
     __UpperCamelCase       				=RegNetConfig(depths=[2, 7, 17, 1]     ,	hidden_sizes=[8, 8, 8, 8]     ,	groups_width=8							)
     if "in1k" in model_name:
          __UpperCamelCase       				=RegNetForImageClassification(lowerCAmelCase_							)
     else:
          __UpperCamelCase       				=RegNetModel(lowerCAmelCase_							)
     # create from model (with small weights)
     __UpperCamelCase       				=FakeRegNetVisslWrapper(
         RegNet(FakeRegNetParams(depth=27     ,	group_width=10_10     ,	w_a=17_44     ,	w_a=620.83     ,	w_m=2.52							)							)							)

     with torch.no_grad():
          __UpperCamelCase       				=from_model.eval()
          __UpperCamelCase       				=our_model.eval()

          __UpperCamelCase       				=torch.randn((1, 3, 32, 32)							)
          # trace both
          __UpperCamelCase       				=Tracker(lowerCAmelCase_							)
          __UpperCamelCase       				=dest_tracker(lowerCAmelCase_							).parametrized

          pprint(dest_tracker.nameamodule							)
          __UpperCamelCase       				=Tracker(lowerCAmelCase_							)
          __UpperCamelCase       				=src_tracker(lowerCAmelCase_							).parametrized

     # convert the keys -> module dict to keys -> params
     def to_params_dict(SCREAMING_SNAKE_CASE__     :   Dict							):
          __UpperCamelCase       				=OrderedDict()
          for name, module in dict_with_modules.items():
               for param_name, param in module.state_dict().items():
                    __UpperCamelCase       				=param
          return params_dict

     __UpperCamelCase       				={}

     __UpperCamelCase       				=to_params_dict(lowerCAmelCase_							)
     __UpperCamelCase       				=to_params_dict(lowerCAmelCase_							)

     for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()     ,	dst_state_dict.items()							):
          __UpperCamelCase       				=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       				='classifier.1.weight'
          __UpperCamelCase       				='classifier.1.bias'

     return from_to_ours_keys

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   str     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any] = None     ,	SCREAMING_SNAKE_CASE__     :   List[Any] = True							):
     __UpperCamelCase       				='imagenet-1k-id2label.json'
     __UpperCamelCase       				=10_00

     __UpperCamelCase       				='huggingface/label-files'
     __UpperCamelCase       				=num_labels
     __UpperCamelCase       				=json.load(open(cached_download(hf_hub_url(lowerCAmelCase_     ,	lowerCAmelCase_     ,	repo_type='dataset'							)							)     ,	'r'							)							)
     __UpperCamelCase       				={int(lowerCAmelCase_							): v for k, v in idalabel.items()}

     __UpperCamelCase       				=idalabel
     __UpperCamelCase       				={v: k for k, v in idalabel.items()}

     __UpperCamelCase       				=partial(lowerCAmelCase_     ,	num_labels=lowerCAmelCase_     ,	idalabel=lowerCAmelCase_     ,	labelaid=lowerCAmelCase_							)

     __UpperCamelCase       				={
         '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-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							),
     }

     # add seer weights logic
     def load_using_classy_vision(SCREAMING_SNAKE_CASE__     :   Dict							) -> Tuple[Dict, Dict]:
          __UpperCamelCase       				=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       				=files['classy_state_dict']['base_model']['model']
          return model_state_dict["trunk"], model_state_dict["heads"]

     __UpperCamelCase       				={
         '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       				=get_from_to_our_keys(lowerCAmelCase_							)

     if not (save_directory / F'{model_name}.pth').exists():
          logger.info('Loading original state_dict.'							)
          __UpperCamelCase				, __UpperCamelCase       				=names_to_from_model[model_name]()
          __UpperCamelCase       				=from_state_dict_trunk
          if "in1k" in model_name:
               # add the head
               __UpperCamelCase       				={**from_state_dict_trunk, **from_state_dict_head}
          logger.info('Done!'							)

          __UpperCamelCase       				={}

          __UpperCamelCase       				=list(from_state_dict.keys()							)
          __UpperCamelCase       				=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       				=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       				=from_to_ours_keys[src_key]
               # store the parameter with our key
               __UpperCamelCase       				=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       				=names_to_config[model_name]
          __UpperCamelCase       				=RegNetModel
          if "in1k" in model_name:
               __UpperCamelCase       				=RegNetForImageClassification
          __UpperCamelCase       				=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       				=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       				=3_84
          # we can use the convnext one
          __UpperCamelCase       				=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__":
   _A									=		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									=		parser.parse_args()

   _A									=		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)






from __future__ import annotations

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    set[int]:




       '''simple docstring'''






       _A=						{1}
       _A=						[1]
       for _ in range(2							,       depth + 1 ):
              _A=						[*previous_coefficients, 0]
              _A=						[0, *previous_coefficients]
              _A=						[]
              for x, y in zip(lowerCAmelCase_							,       lowerCAmelCase_ ):
                     coefficients.add(x + y )
                     previous_coefficients.append(x + y )
       return coefficients

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    set[int]:




       '''simple docstring'''






       _A=						set()
       for number in unique_coefficients:
              _A=						2
              _A=						number
              while divisor**2 <= copy_number:
                     _A=						0
                     while copy_number % divisor == 0:
                            copy_number //= divisor
                            multiplicity += 1
                     if multiplicity >= 2:
                            non_squarefrees.add(lowerCAmelCase_ )
                            break
                     divisor += 1

       return unique_coefficients.difference(lowerCAmelCase_ )

def 				UpperCamelCase					(     lowerCAmelCase_ = 51 )		->    int:




       '''simple docstring'''






       _A=						get_pascal_triangle_unique_coefficients(lowerCAmelCase_ )
       _A=						get_squarefrees(lowerCAmelCase_ )
       return sum(lowerCAmelCase_ )


if __name__ == "__main__":
      print(F"{solution() = }")