




"""simple docstring"""







def 							lowercase					(snake_case__   : List[Any]      ,       snake_case__   : Dict )     ->  Tuple:


				'''simple docstring'''






				lowerCAmelCase  =				""""""
				for i in table:
								res += inp[i - 1]
				return res





def 							lowercase					(snake_case__   : Dict )     ->  Tuple:


				'''simple docstring'''






				return data[1:] + data[0]





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : Optional[int] )     ->  Tuple:


				'''simple docstring'''






				lowerCAmelCase  =				""""""
				for i in range(len(snake_case__ ) ):
								if a[i] == b[i]:
												res += "0"
								else:
												res += "1"
				return res





def 							lowercase					(snake_case__   : Any      ,       snake_case__   : Union[str, Any] )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				int("""0b""" + data[0] + data[-1]      ,       2 )
				lowerCAmelCase  =				int("""0b""" + data[1:3]      ,       2 )
				return bin(s[row][col] )[2:]





def 							lowercase					(snake_case__   : Tuple      ,       snake_case__   : Dict      ,       snake_case__   : Union[str, Any]      ,       snake_case__   : Union[str, Any]      ,       snake_case__   : Union[str, Any] )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				message[:4]
				lowerCAmelCase  =				message[4:]
				lowerCAmelCase  =				apply_table(snake_case__      ,       snake_case__ )
				lowerCAmelCase  =				xor(snake_case__      ,       snake_case__ )
				lowerCAmelCase  =				apply_sbox(snake_case__      ,       temp[:4] )  # noqa: E741
				lowerCAmelCase  =				apply_sbox(snake_case__      ,       temp[4:] )
				lowerCAmelCase  =				"""0""" * (2 - len(snake_case__ )) + l  # noqa: E741
				lowerCAmelCase  =				"""0""" * (2 - len(snake_case__ )) + r
				lowerCAmelCase  =				apply_table(l + r      ,       snake_case__ )
				lowerCAmelCase  =				xor(snake_case__      ,       snake_case__ )
				return temp + right


if __name__ == "__main__":
		a      =							input('Enter 10 bit key: ')
		a      =							input('Enter 8 bit message: ')

		a      =							[6, 3, 7, 4, 8, 5, 1_0, 9]
		a      =							[3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
		a      =							[2, 4, 3, 1]
		a      =							[2, 6, 3, 1, 4, 8, 5, 7]
		a      =							[4, 1, 3, 5, 7, 2, 8, 6]
		a      =							[4, 1, 2, 3, 2, 3, 4, 1]
		a      =							[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
		a      =							[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

		# key generation
		a      =							apply_table(key, paa_table)
		a      =							temp[:5]
		a      =							temp[5:]
		a      =							left_shift(left)
		a      =							left_shift(right)
		a      =							apply_table(left + right, pa_table)
		a      =							left_shift(left)
		a      =							left_shift(right)
		a      =							left_shift(left)
		a      =							left_shift(right)
		a      =							apply_table(left + right, pa_table)

		# encryption
		a      =							apply_table(message, IP)
		a      =							function(expansion, sa, sa, keya, temp)
		a      =							temp[4:] + temp[:4]
		a      =							function(expansion, sa, sa, keya, temp)
		a      =							apply_table(temp, IP_inv)
		print('Cipher text is:', CT)

		# decryption
		a      =							apply_table(CT, IP)
		a      =							function(expansion, sa, sa, keya, temp)
		a      =							temp[4:] + temp[:4]
		a      =							function(expansion, sa, sa, keya, temp)
		a      =							apply_table(temp, IP_inv)
		print('Plain text after decypting is:', PT)











"""simple docstring"""







import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 							lowercase					(snake_case__   : Union[str, Any] )     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				[
				    """decoder.version""",
				    """decoder.output_projection.weight""",
				    """_float_tensor""",
				    """decoder.embed_positions._float_tensor""",
				]
				for k in ignore_keys:
								state_dict.pop(snake_case__      ,       snake_case__ )





def 							lowercase					(snake_case__   : List[str] )     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase ,						lowerCAmelCase  =				emb.weight.shape
				lowerCAmelCase  =				nn.Linear(snake_case__      ,       snake_case__      ,       bias=snake_case__ )
				lowerCAmelCase  =				emb.weight.data
				return lin_layer





def 							lowercase					(snake_case__   : str )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				torch.load(snake_case__      ,       map_location="""cpu""" )
				lowerCAmelCase  =				Namespace(**checkpoint["""cfg"""]["""model"""] )
				lowerCAmelCase  =				checkpoint["""model"""]
				remove_ignore_keys_(snake_case__ )
				lowerCAmelCase  =				state_dict["""decoder.embed_tokens.weight"""].shape[0]

				lowerCAmelCase  =				{key.replace("""decoder"""      ,       """model""" ): val for key, val in state_dict.items()}

				lowerCAmelCase  =				XGLMConfig(
				    vocab_size=snake_case__      ,       max_position_embeddings=args.max_target_positions      ,       num_layers=args.decoder_layers      ,       attention_heads=args.decoder_attention_heads      ,       ffn_dim=args.decoder_ffn_embed_dim      ,       d_model=args.decoder_embed_dim      ,       layerdrop=args.decoder_layerdrop      ,       dropout=args.dropout      ,       attention_dropout=args.attention_dropout      ,       activation_dropout=args.activation_dropout      ,       activation_function="""gelu"""      ,       scale_embedding=not args.no_scale_embedding      ,       tie_word_embeddings=args.share_decoder_input_output_embed      ,       )

				lowerCAmelCase  =				XGLMForCausalLM(snake_case__ )
				lowerCAmelCase  =				model.load_state_dict(snake_case__      ,       strict=snake_case__ )
				print(snake_case__ )
				lowerCAmelCase  =				make_linear_from_emb(model.model.embed_tokens )

				return model


if __name__ == "__main__":
		a      =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
		parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		a      =							parser.parse_args()
		a      =							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)





