'''simple docstring'''







import string

import numpy


def    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int		)			->	int:


							'''simple docstring'''






							return b if a == 0 else greatest_common_divisor(b % a				,						UpperCAmelCase__		)




class _SCREAMING_SNAKE_CASE:
						A_			:							int     		= string.ascii_uppercase + string.digits
						# This cipher takes alphanumerics into account
						# i.e. a total of 36 characters

						# take x and return x % len(key_string)
						A_			:							Optional[int]     		= numpy.vectorize(lambda _SCREAMING_SNAKE_CASE					: x % 36					)

						A_			:							Optional[Any]     		= numpy.vectorize(_SCREAMING_SNAKE_CASE					)




						def __init__(    self :       str							,							UpperCamelCase_ :       numpy.ndarray      )     ->				None:
													SCREAMING_SNAKE_CASE__     :str						    = self.modulus(UpperCamelCase_      )  # mod36 calc's on the encrypt key
													self.check_determinant()  # validate the determinant of the encryption key
													SCREAMING_SNAKE_CASE__     :Tuple						    = encrypt_key.shape[0]




						def      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       str      )     ->				int:
													return self.key_string.index(UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       int      )     ->				str:
													return self.key_string[round(UpperCamelCase_      )]




						def      __lowerCamelCase       (    self :       int      )     ->				None:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = round(numpy.linalg.det(self.encrypt_key      )      )

													if det < 0:
																				SCREAMING_SNAKE_CASE__     :str						    = det % len(self.key_string      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = len(self.key_string      )
													if greatest_common_divisor(UpperCamelCase_							,							len(self.key_string      )      ) != 1:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = (
																				    f'''determinant modular {req_l} of encryption key({det}) '''
																				    f'''is not co prime w.r.t {req_l}.\nTry another key.'''
																				)
																				raise ValueError(UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       List[str]							,							UpperCamelCase_ :       str      )     ->				str:
													SCREAMING_SNAKE_CASE__     :int						    = [char for char in text.upper() if char in self.key_string]

													SCREAMING_SNAKE_CASE__     :Tuple						    = chars[-1]
													while len(UpperCamelCase_      ) % self.break_key != 0:
																				chars.append(UpperCamelCase_      )

													return "".join(UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Tuple							,							UpperCamelCase_ :       str      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.process_text(text.upper()      )
													SCREAMING_SNAKE_CASE__     :int						    = ''

													for i in range(0							,							len(UpperCamelCase_      ) - self.break_key + 1							,							self.break_key      ):
																				SCREAMING_SNAKE_CASE__     :Dict						    = text[i : i + self.break_key]
																				SCREAMING_SNAKE_CASE__     :Any						    = [self.replace_letters(UpperCamelCase_      ) for char in batch]
																				SCREAMING_SNAKE_CASE__     :int						    = numpy.array([vec]      ).T
																				SCREAMING_SNAKE_CASE__     :str						    = self.modulus(self.encrypt_key.dot(UpperCamelCase_      )      ).T.tolist()[
																				    0
																				]
																				SCREAMING_SNAKE_CASE__     :Any						    = ''.join(
																				    self.replace_digits(UpperCamelCase_      ) for num in batch_encrypted      )
																				encrypted += encrypted_batch

													return encrypted




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				numpy.ndarray:
													SCREAMING_SNAKE_CASE__     :Tuple						    = round(numpy.linalg.det(self.encrypt_key      )      )

													if det < 0:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = det % len(self.key_string      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = None
													for i in range(len(self.key_string      )      ):
																				if (det * i) % len(self.key_string      ) == 1:
																											SCREAMING_SNAKE_CASE__     :Any						    = i
																											break

													SCREAMING_SNAKE_CASE__     :Dict						    = (
													    det_inv
													    * numpy.linalg.det(self.encrypt_key      )
													    * numpy.linalg.inv(self.encrypt_key      )
													)

													return self.to_int(self.modulus(UpperCamelCase_      )      )




						def      __lowerCamelCase       (    self :       Tuple							,							UpperCamelCase_ :       str      )     ->				str:
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.make_decrypt_key()
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.process_text(text.upper()      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = ''

													for i in range(0							,							len(UpperCamelCase_      ) - self.break_key + 1							,							self.break_key      ):
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = text[i : i + self.break_key]
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = [self.replace_letters(UpperCamelCase_      ) for char in batch]
																				SCREAMING_SNAKE_CASE__     :str						    = numpy.array([vec]      ).T
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.modulus(decrypt_key.dot(UpperCamelCase_      )      ).T.tolist()[0]
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = ''.join(
																				    self.replace_digits(UpperCamelCase_      ) for num in batch_decrypted      )
																				decrypted += decrypted_batch

													return decrypted


def    lowerCamelCase       (			)			->	None:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[Any]						    = int(input('Enter the order of the encryption key: '		)		)
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = []

							print('Enter each row of the encryption key with space separated integers'		)
							for _ in range(UpperCAmelCase__		):
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = [int(UpperCAmelCase__		) for x in input().split()]
														hill_matrix.append(UpperCAmelCase__		)

							SCREAMING_SNAKE_CASE__     :str						    = HillCipher(numpy.array(UpperCAmelCase__		)		)

							print('Would you like to encrypt or decrypt some text? (1 or 2)'		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = input('\n1. Encrypt\n2. Decrypt\n'		)
							if option == "1":
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = input('What text would you like to encrypt?: '		)
														print('Your encrypted text is:'		)
														print(hc.encrypt(UpperCAmelCase__		)		)
							elif option == "2":
														SCREAMING_SNAKE_CASE__     :Tuple						    = input('What text would you like to decrypt?: '		)
														print('Your decrypted text is:'		)
														print(hc.decrypt(UpperCAmelCase__		)		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		main()







'''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()
UpperCamelCase_      	=	logging.get_logger()




@dataclass
class _SCREAMING_SNAKE_CASE:
						A_			:							nn.Module
						A_			:							List[nn.Module]     		= field(default_factory=_SCREAMING_SNAKE_CASE					)
						A_			:							list     		= field(default_factory=_SCREAMING_SNAKE_CASE					)




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       Tensor							,							UpperCamelCase_ :       Tensor      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 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 :       Tuple							,							UpperCamelCase_ :       Tensor      )     ->				int:
													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      __lowerCamelCase       (    self :       Tuple      )     ->				str:
													# 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 _SCREAMING_SNAKE_CASE:
						A_			:							nn.Module
						A_			:							nn.Module
						A_			:							int     		= 0
						A_			:							List     		= field(default_factory=_SCREAMING_SNAKE_CASE					)
						A_			:							List     		= field(default_factory=_SCREAMING_SNAKE_CASE					)




						def __call__(    self :       List[Any]							,							UpperCamelCase_ :       Tensor      )     ->				int:
													SCREAMING_SNAKE_CASE__     :List[str]						    = Tracker(self.dest      )(UpperCamelCase_      ).parametrized
													SCREAMING_SNAKE_CASE__     :Any						    = Tracker(self.src      )(UpperCamelCase_      ).parametrized

													SCREAMING_SNAKE_CASE__     :Dict						    = list(filter(lambda UpperCamelCase_      : type(UpperCamelCase_      ) not in self.src_skip							,							UpperCamelCase_      )      )
													SCREAMING_SNAKE_CASE__     :str						    = 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    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  ResNetConfig				,						UpperCAmelCase__				:  Path				,						UpperCAmelCase__				:  bool = True		)			->	Union[str, Any]:


							'''simple docstring'''






							print(F'''Converting {name}...'''		)
							with torch.no_grad():
														SCREAMING_SNAKE_CASE__     :str						    = timm.create_model(UpperCAmelCase__				,						pretrained=UpperCAmelCase__		).eval()
														SCREAMING_SNAKE_CASE__     :List[str]						    = ResNetForImageClassification(UpperCAmelCase__		).eval()
														SCREAMING_SNAKE_CASE__     :Dict						    = ModuleTransfer(src=UpperCAmelCase__				,						dest=UpperCAmelCase__		)
														SCREAMING_SNAKE_CASE__     :List[Any]						    = torch.randn((1, 3, 2_2_4, 2_2_4)		)
														module_transfer(UpperCAmelCase__		)

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

							SCREAMING_SNAKE_CASE__     :List[str]						    = F'''resnet{"-".join(name.split("resnet"		)		)}'''
							print(UpperCAmelCase__		)

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

														# we can use the convnext one
														SCREAMING_SNAKE_CASE__     :int						    = 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=UpperCAmelCase__				,						)

														print(F'''Pushed {checkpoint_name}'''		)


def    lowerCamelCase       (			UpperCAmelCase__				:  Path				,						UpperCAmelCase__				:  str = None				,						UpperCAmelCase__				:  bool = True		)			->	int:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[str]						    = 'imagenet-1k-id2label.json'
							SCREAMING_SNAKE_CASE__     :Any						    = 1_0_0_0
							SCREAMING_SNAKE_CASE__     :List[Any]						    = (1, num_labels)

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

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

							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = partial(UpperCAmelCase__				,						num_labels=UpperCAmelCase__				,						idalabel=UpperCAmelCase__				,						labelaid=UpperCAmelCase__		)

							SCREAMING_SNAKE_CASE__     :List[str]						    = {
							    'resnet18': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]				,						hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]				,						layer_type='basic'		),
							    'resnet26': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet34': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]				,						hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]				,						layer_type='basic'		),
							    'resnet50': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet101': ImageNetPreTrainedConfig(
							        depths=[3, 4, 2_3, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet152': ImageNetPreTrainedConfig(
							        depths=[3, 8, 3_6, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							}

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


if __name__ == "__main__":
		UpperCamelCase_      	=	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.''',
		)

		UpperCamelCase_      	=	parser.parse_args()
		UpperCamelCase_      	=	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)






