"""simple docstring"""






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def 						a_		(      lowerCamelCase ):
				if "model" in orig_key:
								UpperCAmelCase__    =							orig_key.replace('model.'      ,    '' )
				if "norm1" in orig_key:
								UpperCAmelCase__    =							orig_key.replace('norm1'      ,    'attention.output.LayerNorm' )
				if "norm2" in orig_key:
								UpperCAmelCase__    =							orig_key.replace('norm2'      ,    'output.LayerNorm' )
				if "norm" in orig_key:
								UpperCAmelCase__    =							orig_key.replace('norm'      ,    'LayerNorm' )
				if "transformer" in orig_key:
								UpperCAmelCase__    =							orig_key.split('.' )[0].split('_' )[-1]
								UpperCAmelCase__    =							orig_key.replace(f'''transformer_{layer_num}'''      ,    f'''encoder.layer.{layer_num}''' )
				if "mha.attn" in orig_key:
								UpperCAmelCase__    =							orig_key.replace('mha.attn'      ,    'attention.self' )
				if "mha" in orig_key:
								UpperCAmelCase__    =							orig_key.replace('mha'      ,    'attention' )
				if "W_q" in orig_key:
								UpperCAmelCase__    =							orig_key.replace('W_q'      ,    'self.query' )
				if "W_k" in orig_key:
								UpperCAmelCase__    =							orig_key.replace('W_k'      ,    'self.key' )
				if "W_v" in orig_key:
								UpperCAmelCase__    =							orig_key.replace('W_v'      ,    'self.value' )
				if "ff1" in orig_key:
								UpperCAmelCase__    =							orig_key.replace('ff1'      ,    'intermediate.dense' )
				if "ff2" in orig_key:
								UpperCAmelCase__    =							orig_key.replace('ff2'      ,    'output.dense' )
				if "ff" in orig_key:
								UpperCAmelCase__    =							orig_key.replace('ff'      ,    'output.dense' )
				if "mlm_class" in orig_key:
								UpperCAmelCase__    =							orig_key.replace('mlm.mlm_class'      ,    'cls.predictions.decoder' )
				if "mlm" in orig_key:
								UpperCAmelCase__    =							orig_key.replace('mlm'      ,    'cls.predictions.transform' )
				if "cls" not in orig_key:
								UpperCAmelCase__    =							'yoso.' + orig_key

				return orig_key



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				for key in orig_state_dict.copy().keys():
								UpperCAmelCase__    =							orig_state_dict.pop(lowerCamelCase )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												UpperCAmelCase__    =							val

				UpperCAmelCase__    =							orig_state_dict['cls.predictions.decoder.bias']
				UpperCAmelCase__    =							torch.arange(lowerCamelCase ).expand((1, -1) ) + 2

				return orig_state_dict



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							torch.load(lowerCamelCase      ,    map_location='cpu' )['model_state_dict']
				UpperCAmelCase__    =							YosoConfig.from_json_file(lowerCamelCase )
				UpperCAmelCase__    =							YosoForMaskedLM(lowerCamelCase )

				UpperCAmelCase__    =							convert_checkpoint_helper(config.max_position_embeddings      ,    lowerCamelCase )

				print(model.load_state_dict(lowerCamelCase ) )
				model.eval()
				model.save_pretrained(lowerCamelCase )

				print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )


if __name__ == "__main__":
						lowerCAmelCase__						:						List[str]		  =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
						)
						parser.add_argument(
						    '--config_file',
						    default=None,
						    type=str,
						    required=True,
						    help='The json file for YOSO model config.',
						)
						parser.add_argument(
						    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						lowerCAmelCase__						:						List[str]		  =			parser.parse_args()
						convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






"""simple docstring"""






import random
class        snake_case :




					"""simple docstring"""

					@staticmethod
					def 	__lowerCAmelCase		(       lowerCamelCase__     :			str    ):
									UpperCAmelCase__    =							[ord(lowerCamelCase__    ) for i in text]
									UpperCAmelCase__    =							[]
									UpperCAmelCase__    =							[]
									for i in plain:
													UpperCAmelCase__    =							random.randint(1					,300    )
													UpperCAmelCase__    =							(i + k) * k
													cipher.append(lowerCamelCase__    )
													key.append(lowerCamelCase__    )
									return cipher, key







					@staticmethod
					def 	__lowerCAmelCase		(       lowerCamelCase__     :			list[int]					,lowerCamelCase__     :			list[int]    ):
									UpperCAmelCase__    =							[]
									for i in range(len(lowerCamelCase__    )    ):
													UpperCAmelCase__    =							int((cipher[i] - (key[i]) ** 2) / key[i]    )
													plain.append(chr(lowerCamelCase__    )    )
									return "".join(lowerCamelCase__    )


if __name__ == "__main__":
						lowerCAmelCase__  ,			lowerCAmelCase__						:						Dict		  =			Onepad().encrypt('Hello')
						print(c, k)
						print(Onepad().decrypt(c, k))



