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     __UpperCAmelCase:






 """simple docstring"""




 __magic_name__          =   42
 __magic_name__          =   field(default_factory=lowerCAmelCase_				)
 __magic_name__          =   field(default_factory=lowerCAmelCase_				)
 __magic_name__          =   field(default_factory=lowerCAmelCase_				)
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__      ):

        """simple docstring"""

        A_							:       List[Any]	      =				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      )
               A_							:       Any	      =				m
 def __call__(    self ,       __magic_name__      ):

        """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  UpperCAmelCase   (    self      ):

        """simple docstring"""

        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 ,       __magic_name__      ):

        """simple docstring"""

        super().__init__()

        A_							:       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}"""
               A_							:       List[Any]	      =				len(_SCREAMING_SNAKE_CASE      ) + 1
               feature_blocks.append((f"""res{block_index}""", v)      )

        A_							:       List[Any]	      =				nn.ModuleDict(_SCREAMING_SNAKE_CASE      )
 def  UpperCAmelCase   (    self ,       __magic_name__      ):

        """simple docstring"""

        return get_trunk_forward_outputs(
            _SCREAMING_SNAKE_CASE ,       out_feat_keys=_SCREAMING_SNAKE_CASE ,       feature_blocks=self._feature_blocks ,       )






class     __UpperCAmelCase(    lowerCAmelCase_				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        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       a__      (					a							)		->	Dict[str, str]:
       A_							:       Dict	      =				RegNetConfig(depths=[2, 7, 1_7, 1]  ,   hidden_sizes=[8, 8, 8, 8]  ,   groups_width=8							)
       if "in1k" in model_name:
              A_							:       Union[str, Any]	      =				RegNetForImageClassification(UpperCamelCase_							)
       else:
              A_							:       Optional[int]	      =				RegNetModel(UpperCamelCase_							)
       # create from model (with small weights)
       A_							:       str	      =				FakeRegNetVisslWrapper(
           RegNet(FakeRegNetParams(depth=2_7  ,   group_width=1_0_1_0  ,   w_a=1_7_4_4  ,   w_a=6_2_0.8_3  ,   w_m=2.5_2							)							)							)

       with torch.no_grad():
              A_							:       Dict	      =				from_model.eval()
              A_							:       Dict	      =				our_model.eval()

              A_							:       Tuple	      =				torch.randn((1, 3, 3_2, 3_2)							)
              # trace both
              A_							:       str	      =				Tracker(UpperCamelCase_							)
              A_							:       List[str]	      =				dest_tracker(UpperCamelCase_							).parametrized

              pprint(dest_tracker.nameamodule							)
              A_							:       Optional[int]	      =				Tracker(UpperCamelCase_							)
              A_							:       List[Any]	      =				src_tracker(UpperCamelCase_							).parametrized

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

       A_							:       str	      =				{}

       A_							:       Optional[Any]	      =				to_params_dict(UpperCamelCase_							)
       A_							:       Tuple	      =				to_params_dict(UpperCamelCase_							)

       for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()  ,   dst_state_dict.items()							):
              A_							:       List[Any]	      =				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:
              A_							:       Dict	      =				'''classifier.1.weight'''
              A_							:       Optional[Any]	      =				'''classifier.1.bias'''

       return from_to_ours_keys


def       a__      (					a  ,   a = None  ,   a = True							)		->	Dict:
       A_							:       str	      =				'''imagenet-1k-id2label.json'''
       A_							:       List[str]	      =				1_0_0_0

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

       A_							:       Optional[Any]	      =				idalabel
       A_							:       str	      =				{v: k for k, v in idalabel.items()}

       A_							:       Union[str, Any]	      =				partial(UpperCamelCase_  ,   num_labels=UpperCamelCase_  ,   idalabel=UpperCamelCase_  ,   labelaid=UpperCamelCase_							)

       A_							:       Any	      =				{
           '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
               depths=[2, 7, 1_7, 1]  ,   hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]  ,   groups_width=1_0_1_0							),
           # finetuned on imagenet
           '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
               depths=[2, 7, 1_7, 1]  ,   hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]  ,   groups_width=1_0_1_0							),
       }

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

       A_							:       Optional[Any]	      =				{
           '''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'''  ,   ),
       }

       A_							:       List[Any]	      =				get_from_to_our_keys(UpperCamelCase_							)

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

              A_							:       Any	      =				{}

              A_							:       Any	      =				list(from_state_dict.keys()							)
              A_							:       Optional[int]	      =				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
                     A_							:       Optional[int]	      =				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
                     A_							:       Any	      =				from_to_ours_keys[src_key]
                     # store the parameter with our key
                     A_							:       Optional[Any]	      =				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
              A_							:       int	      =				names_to_config[model_name]
              A_							:       Union[str, Any]	      =				RegNetModel
              if "in1k" in model_name:
                     A_							:       Optional[Any]	      =				RegNetForImageClassification
              A_							:       List[Any]	      =				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
              A_							:       str	      =				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  ,   )
              A_							:       Dict	      =				3_8_4
              # we can use the convnext one
              A_							:       Tuple	      =				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)









from __future__ import annotations




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		int | None:
       for r in range(1       ,		UpperCamelCase_	):
              UpperCamelCase_ 							=	[]
              for x in range(n - 1	):
                     UpperCamelCase_ 							=	pow(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)
                     if val in li:
                            break
                     li.append(UpperCamelCase_	)
              else:
                     return r
       return None


if __name__ == "__main__":
     _UpperCAmelCase										=					int(input('Enter a prime number q: '))
     _UpperCAmelCase										=					find_primitive(q)
     if a is None:
          print(f'''Cannot find the primitive for the value: {a!r}''')
     else:
          _UpperCAmelCase										=					int(input('Enter private key of A: '))
          _UpperCAmelCase										=					pow(a, a_private, q)
          _UpperCAmelCase										=					int(input('Enter private key of B: '))
          _UpperCAmelCase										=					pow(a, b_private, q)

          _UpperCAmelCase										=					pow(b_public, a_private, q)
          _UpperCAmelCase										=					pow(a_public, b_private, q)

          print('The key value generated by A is: ', a_secret)
          print('The key value generated by B is: ', b_secret)

