




'''simple docstring'''



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()
lowerCamelCase_	 =		logging.get_logger()







@dataclass
class 	lowercase_					:


   """simple docstring"""

   lowerCamelCase_       						=		42
   lowerCamelCase_       						=		field(default_factory=lowerCamelCase__     )
   lowerCamelCase_       						=		field(default_factory=lowerCamelCase__     )
   lowerCamelCase_       						=		field(default_factory=lowerCamelCase__     )



   def 							lowerCAmelCase_    (					self      :			Dict						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Tensor						,   __lowerCamelCase      :			Tensor						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   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_					)
         _SCREAMING_SNAKE_CASE =   m



   def __call__(					self      :			str						,   __lowerCamelCase      :			Tensor					):

      """simple docstring"""
      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 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      # 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      :			Any						,   __lowerCamelCase      :			nn.Module					):

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

      _SCREAMING_SNAKE_CASE =   []
      # - 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 =   len(SCREAMING_SNAKE_CASE_					) + 1
         feature_blocks.append((F"""res{block_index}""", v)					)

      _SCREAMING_SNAKE_CASE =   nn.ModuleDict(SCREAMING_SNAKE_CASE_					)



   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			Tensor					):

      """simple docstring"""
      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 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]


def SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				Dict[str, str]:
   _SCREAMING_SNAKE_CASE =   RegNetConfig(depths=[2, 7, 17, 1] ,  hidden_sizes=[8, 8, 8, 8] ,  groups_width=8						)
   if "in1k" in model_name:
      _SCREAMING_SNAKE_CASE =   RegNetForImageClassification(_UpperCAmelCase						)
   else:
      _SCREAMING_SNAKE_CASE =   RegNetModel(_UpperCAmelCase						)
   # create from model (with small weights)
   _SCREAMING_SNAKE_CASE =   FakeRegNetVisslWrapper(
       RegNet(FakeRegNetParams(depth=27 ,  group_width=10_10 ,  w_a=17_44 ,  w_a=6_2_0.8_3 ,  w_m=2.5_2						)						)						)

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

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

      pprint(dest_tracker.nameamodule						)
      _SCREAMING_SNAKE_CASE =   Tracker(_UpperCAmelCase						)
      _SCREAMING_SNAKE_CASE =   src_tracker(_UpperCAmelCase						).parametrized

   # convert the keys -> module dict to keys -> params
   def to_params_dict(__A       :				List[Any]						):
      _SCREAMING_SNAKE_CASE =   OrderedDict()
      for name, module in dict_with_modules.items():
         for param_name, param in module.state_dict().items():
            _SCREAMING_SNAKE_CASE =   param
      return params_dict

   _SCREAMING_SNAKE_CASE =   {}

   _SCREAMING_SNAKE_CASE =   to_params_dict(_UpperCAmelCase						)
   _SCREAMING_SNAKE_CASE =   to_params_dict(_UpperCAmelCase						)

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

   return from_to_ours_keys
def SCREAMING_SNAKE_CASE_				(							__A       :				Optional[Any] ,  __A       :				Any = None ,  __A       :				Tuple = True						)				->				Union[str, Any]:
   _SCREAMING_SNAKE_CASE =   "imagenet-1k-id2label.json"
   _SCREAMING_SNAKE_CASE =   10_00

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

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

   _SCREAMING_SNAKE_CASE =   partial(_UpperCAmelCase ,  num_labels=_UpperCAmelCase ,  idalabel=_UpperCAmelCase ,  labelaid=_UpperCAmelCase						)

   _SCREAMING_SNAKE_CASE =   {
       "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(__A       :				Any						) -> Tuple[Dict, Dict]:
      _SCREAMING_SNAKE_CASE =   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
      _SCREAMING_SNAKE_CASE =   files["classy_state_dict"]["base_model"]["model"]
      return model_state_dict["trunk"], model_state_dict["heads"]

   _SCREAMING_SNAKE_CASE =   {
       "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" ,  ),
   }

   _SCREAMING_SNAKE_CASE =   get_from_to_our_keys(_UpperCAmelCase						)

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

      _SCREAMING_SNAKE_CASE =   {}

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

 lowerCamelCase_	 =		parser.parse_args()

 lowerCamelCase_	 =		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)







'''simple docstring'''



def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[Any] ,  __A       :				Dict						)				->				Union[str, Any]:
   if n == 0:
      return 1

   elif n % 2 == 1:
      return binary_exponentiation(__A ,  n - 1						) * a

   else:
      _SCREAMING_SNAKE_CASE =   binary_exponentiation(__A ,  n / 2						)
      return b * b


if __name__ == "__main__":
 try:
  lowerCamelCase_	 =		int(input('Enter Base : ').strip())
  lowerCamelCase_	 =		int(input('Enter Power : ').strip())
 except ValueError:
  print('Invalid literal for integer')

 lowerCamelCase_	 =		binary_exponentiation(BASE, POWER)
 print(f'''{BASE}^({POWER}) : {RESULT}''')

