






import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


__UpperCAmelCase     							=						logging.get_logger(__name__)
logging.set_verbosity_info()




def 					lowercase__				(					__snake_case   :							str					,	__snake_case   :							str  ):


					'''simple docstring'''


					if "xprophetnet" in prophetnet_checkpoint_path:
										UpperCAmelCase_   :				List[Any]						=						XLMProphetNetForConditionalGenerationOld.from_pretrained(__snake_case  )
										UpperCAmelCase_	,  UpperCAmelCase_   :				Any						=						XLMProphetNetForConditionalGeneration.from_pretrained(
										    __snake_case					,	output_loading_info=__snake_case  )
					else:
										UpperCAmelCase_   :				Tuple						=						ProphetNetForConditionalGenerationOld.from_pretrained(__snake_case  )
										UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						ProphetNetForConditionalGeneration.from_pretrained(
										    __snake_case					,	output_loading_info=__snake_case  )

					UpperCAmelCase_   :				Tuple						=						['key_proj', 'value_proj', 'query_proj']

					UpperCAmelCase_   :				int						=						{
					    'self_attn': 'ngram_self_attn',
					    'cross_attn': 'encoder_attn',
					    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
					    'feed_forward_layer_norm': 'final_layer_norm',
					    'feed_forward': '',
					    'intermediate': 'fc1',
					    'output': 'fc2',
					    'key_proj': 'k_proj',
					    'query_proj': 'q_proj',
					    'value_proj': 'v_proj',
					    'word_embeddings': 'embed_tokens',
					    'embeddings_layer_norm': 'emb_layer_norm',
					    'relative_pos_embeddings': 'relative_linear',
					    'ngram_embeddings': 'ngram_input_embed',
					    'position_embeddings': 'embed_positions',
					}

					for key in loading_info["missing_keys"]:
										UpperCAmelCase_   :				List[str]						=						key.split('.'  )

										if attributes[0] == "lm_head":
															UpperCAmelCase_   :				Union[str, Any]						=						prophet
															UpperCAmelCase_   :				Dict						=						prophet_old
										else:
															UpperCAmelCase_   :				Optional[Any]						=						prophet.prophetnet
															UpperCAmelCase_   :				int						=						prophet_old.model

										UpperCAmelCase_   :				List[str]						=						False
										for attribute in attributes:
															if attribute in mapping:
																				UpperCAmelCase_   :				Optional[int]						=						mapping[attribute]
																				if not hasattr(__snake_case					,	__snake_case  ) and len(__snake_case  ) > 0:
																									UpperCAmelCase_   :				Any						=						attribute
															elif hasattr(__snake_case					,	__snake_case  ):
																				UpperCAmelCase_   :				List[str]						=						attribute

															if attribute == "weight":
																				assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																				UpperCAmelCase_   :				Optional[int]						=						old_model.weight
																				logger.info(F"{attribute} is initialized."  )
																				UpperCAmelCase_   :				str						=						True
																				break
															elif attribute == "bias":
																				assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																				UpperCAmelCase_   :				str						=						old_model.bias
																				logger.info(F"{attribute} is initialized"  )
																				UpperCAmelCase_   :				Optional[int]						=						True
																				break
															elif attribute in special_keys and hasattr(__snake_case					,	'in_proj_weight'  ):
																				UpperCAmelCase_   :				Union[str, Any]						=						old_model.in_proj_weight.shape[0] // 3
																				UpperCAmelCase_   :				List[Any]						=						getattr(__snake_case					,	__snake_case  )
																				param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																				param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																				if attribute == "query_proj":
																									UpperCAmelCase_   :				int						=						nn.Parameter(old_model.in_proj_weight[:embed_dim, :]  )
																									UpperCAmelCase_   :				Dict						=						nn.Parameter(old_model.in_proj_bias[:embed_dim]  )

																				elif attribute == "key_proj":
																									UpperCAmelCase_   :				Tuple						=						nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]  )
																									UpperCAmelCase_   :				Any						=						nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]  )
																				elif attribute == "value_proj":
																									UpperCAmelCase_   :				int						=						nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]  )
																									UpperCAmelCase_   :				List[str]						=						nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]  )
																				UpperCAmelCase_   :				Optional[Any]						=						True
																				break
															elif attribute == "position_embeddings":
																				assert (
																				    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																				), "Hidden size has to match"
																				assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																				UpperCAmelCase_   :				Tuple						=						nn.Parameter(old_model.embed_positions.weight[:512, :]  )
																				UpperCAmelCase_   :				List[Any]						=						True
																				break

															if attribute.isdigit():
																				UpperCAmelCase_   :				Optional[Any]						=						model[int(__snake_case  )]
																				UpperCAmelCase_   :				Tuple						=						old_model[int(__snake_case  )]
															else:
																				UpperCAmelCase_   :				Union[str, Any]						=						getattr(__snake_case					,	__snake_case  )

																				if old_attribute == "":
																									UpperCAmelCase_   :				Any						=						old_model
																				else:
																									if not hasattr(__snake_case					,	__snake_case  ):
																														raise ValueError(F"{old_model} does not have {old_attribute}"  )
																									UpperCAmelCase_   :				Tuple						=						getattr(__snake_case					,	__snake_case  )

										if not is_key_init:
															raise ValueError(F"{key} was not correctly initialized!"  )

					print(F"Saving model to {pytorch_dump_folder_path}"  )
					prophet.save_pretrained(__snake_case  )


if __name__ == "__main__":
		__UpperCAmelCase     							=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__UpperCAmelCase     							=						parser.parse_args()
		convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)














def 					lowercase__				(					__snake_case   :							Tuple					,	__snake_case   :							List[Any]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						''
					for i in table:
										res += inp[i - 1]
					return res




def 					lowercase__				(					__snake_case   :							Optional[int]  ):


					'''simple docstring'''


					return data[1:] + data[0]




def 					lowercase__				(					__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[int]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						''
					for i in range(len(__snake_case  )  ):
										if a[i] == b[i]:
															res += "0"
										else:
															res += "1"
					return res




def 					lowercase__				(					__snake_case   :							Dict					,	__snake_case   :							Optional[int]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						int('0b' + data[0] + data[-1]					,	2  )
					UpperCAmelCase_   :				Any						=						int('0b' + data[1:3]					,	2  )
					return bin(s[row][col]  )[2:]




def 					lowercase__				(					__snake_case   :							Tuple					,	__snake_case   :							List[Any]					,	__snake_case   :							List[Any]					,	__snake_case   :							Optional[int]					,	__snake_case   :							str  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						message[:4]
					UpperCAmelCase_   :				Optional[int]						=						message[4:]
					UpperCAmelCase_   :				Union[str, Any]						=						apply_table(__snake_case					,	__snake_case  )
					UpperCAmelCase_   :				Union[str, Any]						=						xor(__snake_case					,	__snake_case  )
					UpperCAmelCase_   :				Dict						=						apply_sbox(__snake_case					,	temp[:4]  )  # noqa: E741
					UpperCAmelCase_   :				Any						=						apply_sbox(__snake_case					,	temp[4:]  )
					UpperCAmelCase_   :				str						=						'0' * (2 - len(__snake_case  )) + l  # noqa: E741
					UpperCAmelCase_   :				int						=						'0' * (2 - len(__snake_case  )) + r
					UpperCAmelCase_   :				int						=						apply_table(l + r					,	__snake_case  )
					UpperCAmelCase_   :				int						=						xor(__snake_case					,	__snake_case  )
					return temp + right


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

		__UpperCAmelCase     							=						[6, 3, 7, 4, 8, 5, 10, 9]
		__UpperCAmelCase     							=						[3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
		__UpperCAmelCase     							=						[2, 4, 3, 1]
		__UpperCAmelCase     							=						[2, 6, 3, 1, 4, 8, 5, 7]
		__UpperCAmelCase     							=						[4, 1, 3, 5, 7, 2, 8, 6]
		__UpperCAmelCase     							=						[4, 1, 2, 3, 2, 3, 4, 1]
		__UpperCAmelCase     							=						[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
		__UpperCAmelCase     							=						[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

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

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

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






