
'''simple docstring'''







import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case				:   Dict     =				logging.get_logger()


@dataclass
class 			lowerCAmelCase :
		a							:					nn.Module
		a							:					List[nn.Module]       =					field(default_factory=__UpperCAmelCase			)
		a							:					list       =					field(default_factory=__UpperCAmelCase			)




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= len(list(m.modules()      )      ) == 1 or isinstance(UpperCamelCase					,						nn.Convad      ) or isinstance(UpperCamelCase					,						nn.BatchNormad      )
								if has_not_submodules:
														self.traced.append(UpperCamelCase      )




		def __call__(				self					,						UpperCamelCase      ):
								for m in self.module.modules():
														self.handles.append(m.register_forward_hook(self._forward_hook      )      )
								self.module(UpperCamelCase      )
								[x.remove() for x in self.handles]
								return self




		@property
		def     lowercase       (				self      ):
								# check the len of the state_dict keys to see if we have learnable params
								return list(filter(lambda UpperCamelCase      : len(list(x.state_dict().keys()      )      ) > 0					,						self.traced      )      )


@dataclass
class 			lowerCAmelCase :
		a							:					nn.Module
		a							:					nn.Module
		a							:					int       =					0
		a							:					List       =					field(default_factory=__UpperCAmelCase			)
		a							:					List       =					field(default_factory=__UpperCAmelCase			)




		def __call__(				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= Tracker(self.dest      )(UpperCamelCase      ).parametrized
								_SCREAMING_SNAKE_CASE				= Tracker(self.src      )(UpperCamelCase      ).parametrized

								_SCREAMING_SNAKE_CASE				= list(filter(lambda UpperCamelCase      : type(UpperCamelCase      ) not in self.src_skip					,						UpperCamelCase      )      )
								_SCREAMING_SNAKE_CASE				= list(filter(lambda UpperCamelCase      : type(UpperCamelCase      ) not in self.dest_skip					,						UpperCamelCase      )      )

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

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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   ResNetConfig	,			_SCREAMING_SNAKE_CASE		:   Path	,			_SCREAMING_SNAKE_CASE		:   bool = True  ):
						print(F'Converting {name}...'  )
						with torch.no_grad():
												_SCREAMING_SNAKE_CASE				= timm.create_model(_SCREAMING_SNAKE_CASE	,			pretrained=_SCREAMING_SNAKE_CASE  ).eval()
												_SCREAMING_SNAKE_CASE				= ResNetForImageClassification(_SCREAMING_SNAKE_CASE  ).eval()
												_SCREAMING_SNAKE_CASE				= ModuleTransfer(src=_SCREAMING_SNAKE_CASE	,			dest=_SCREAMING_SNAKE_CASE  )
												_SCREAMING_SNAKE_CASE				= torch.randn((1, 3, 224, 224)  )
												module_transfer(_SCREAMING_SNAKE_CASE  )

						assert torch.allclose(from_model(_SCREAMING_SNAKE_CASE  )	,			our_model(_SCREAMING_SNAKE_CASE  ).logits  ), "The model logits don't match the original one."

						_SCREAMING_SNAKE_CASE				= F'resnet{"-".join(name.split("resnet"  )  )}'
						print(_SCREAMING_SNAKE_CASE  )

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name	,			commit_message="Add model"	,			use_temp_dir=_SCREAMING_SNAKE_CASE	,			)

												# we can use the convnext one
												_SCREAMING_SNAKE_CASE				= AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"  )
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name	,			commit_message="Add image processor"	,			use_temp_dir=_SCREAMING_SNAKE_CASE	,			)

												print(F'Pushed {checkpoint_name}'  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Path	,			_SCREAMING_SNAKE_CASE		:   str = None	,			_SCREAMING_SNAKE_CASE		:   bool = True  ):
						_SCREAMING_SNAKE_CASE				= "imagenet-1k-id2label.json"
						_SCREAMING_SNAKE_CASE				= 1000
						_SCREAMING_SNAKE_CASE				= (1, num_labels)

						_SCREAMING_SNAKE_CASE				= "huggingface/label-files"
						_SCREAMING_SNAKE_CASE				= num_labels
						_SCREAMING_SNAKE_CASE				= json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			repo_type="dataset"  )	,			"r"  )  )
						_SCREAMING_SNAKE_CASE				= {int(_SCREAMING_SNAKE_CASE  ): 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(_SCREAMING_SNAKE_CASE	,			num_labels=_SCREAMING_SNAKE_CASE	,			idalabel=_SCREAMING_SNAKE_CASE	,			labelaid=_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE				= {
						    "resnet18": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]	,			hidden_sizes=[64, 128, 256, 512]	,			layer_type="basic"  ),
						    "resnet26": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]	,			hidden_sizes=[256, 512, 1024, 2048]	,			layer_type="bottleneck"  ),
						    "resnet34": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]	,			hidden_sizes=[64, 128, 256, 512]	,			layer_type="basic"  ),
						    "resnet50": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]	,			hidden_sizes=[256, 512, 1024, 2048]	,			layer_type="bottleneck"  ),
						    "resnet101": ImageNetPreTrainedConfig(
						        depths=[3, 4, 23, 3]	,			hidden_sizes=[256, 512, 1024, 2048]	,			layer_type="bottleneck"  ),
						    "resnet152": ImageNetPreTrainedConfig(
						        depths=[3, 8, 36, 3]	,			hidden_sizes=[256, 512, 1024, 2048]	,			layer_type="bottleneck"  ),
						}

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


if __name__ == "__main__":
						_snake_case				:   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 resnet* architecture,"""
						        """ currently: resnet18,26,34,50,101,152. 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.""",
						)

						_snake_case				:   Optional[int]     =				parser.parse_args()
						_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)

'''simple docstring'''







import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812



def 			_a		(	):
						print("Making key files..."  )
						make_key_files("rsa"	,			1024  )
						print("Key files generation successful."  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):
						print("Generating prime p..."  )
						_SCREAMING_SNAKE_CASE				= rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE  )
						print("Generating prime q..."  )
						_SCREAMING_SNAKE_CASE				= rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= p * q

						print("Generating e that is relatively prime to (p - 1) * (q - 1)..."  )
						while True:
												_SCREAMING_SNAKE_CASE				= random.randrange(2 ** (key_size - 1)	,			2 ** (key_size)  )
												if cryptoMath.gcd(_SCREAMING_SNAKE_CASE	,			(p - 1) * (q - 1)  ) == 1:
																		break

						print("Calculating d that is mod inverse of e..."  )
						_SCREAMING_SNAKE_CASE				= cryptoMath.find_mod_inverse(_SCREAMING_SNAKE_CASE	,			(p - 1) * (q - 1)  )

						_SCREAMING_SNAKE_CASE				= (n, e)
						_SCREAMING_SNAKE_CASE				= (n, d)
						return (public_key, private_key)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   int  ):
						if os.path.exists(F'{name}_pubkey.txt'  ) or os.path.exists(F'{name}_privkey.txt'  ):
												print("\nWARNING:"  )
												print(
												    F'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
												    "Use a different name or delete these files and re-run this program."  )
												sys.exit()

						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= generate_key(_SCREAMING_SNAKE_CASE  )
						print(F'\nWriting public key to file {name}_pubkey.txt...'  )
						with open(F'{name}_pubkey.txt'	,			"w"  ) as out_file:
												out_file.write(F'{key_size},{public_key[0]},{public_key[1]}'  )

						print(F'Writing private key to file {name}_privkey.txt...'  )
						with open(F'{name}_privkey.txt'	,			"w"  ) as out_file:
												out_file.write(F'{key_size},{private_key[0]},{private_key[1]}'  )


if __name__ == "__main__":
						main()