




"""simple docstring"""






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 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					str    ):
					if "xprophetnet" in prophetnet_checkpoint_path:
										_lowerCAmelCase:Dict									=  XLMProphetNetForConditionalGenerationOld.from_pretrained(snake_case    )
										_lowerCAmelCase							,   _lowerCAmelCase:Union[str, Any]									=  XLMProphetNetForConditionalGeneration.from_pretrained(
										    snake_case      ,       output_loading_info=snake_case    )
					else:
										_lowerCAmelCase:Union[str, Any]									=  ProphetNetForConditionalGenerationOld.from_pretrained(snake_case    )
										_lowerCAmelCase							,   _lowerCAmelCase:Union[str, Any]									=  ProphetNetForConditionalGeneration.from_pretrained(
										    snake_case      ,       output_loading_info=snake_case    )

					_lowerCAmelCase:str									=  ['''key_proj''', '''value_proj''', '''query_proj''']

					_lowerCAmelCase:str									=  {
					    '''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"]:
										_lowerCAmelCase:Union[str, Any]									=  key.split('''.'''    )

										if attributes[0] == "lm_head":
															_lowerCAmelCase:Optional[int]									=  prophet
															_lowerCAmelCase:str									=  prophet_old
										else:
															_lowerCAmelCase:Optional[Any]									=  prophet.prophetnet
															_lowerCAmelCase:Optional[int]									=  prophet_old.model

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

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

																				elif attribute == "key_proj":
																									_lowerCAmelCase:Union[str, Any]									=  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]    )
																									_lowerCAmelCase:int									=  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]    )
																				elif attribute == "value_proj":
																									_lowerCAmelCase:Any									=  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]    )
																									_lowerCAmelCase:Optional[int]									=  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]    )
																				_lowerCAmelCase:Dict									=  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."
																				_lowerCAmelCase:Any									=  nn.Parameter(old_model.embed_positions.weight[:512, :]    )
																				_lowerCAmelCase:Dict									=  True
																				break

															if attribute.isdigit():
																				_lowerCAmelCase:List[str]									=  model[int(snake_case    )]
																				_lowerCAmelCase:Dict									=  old_model[int(snake_case    )]
															else:
																				_lowerCAmelCase:Union[str, Any]									=  getattr(snake_case      ,       snake_case    )

																				if old_attribute == "":
																									_lowerCAmelCase:int									=  old_model
																				else:
																									if not hasattr(snake_case      ,       snake_case    ):
																														raise ValueError(F'{old_model} does not have {old_attribute}'    )
																									_lowerCAmelCase:List[str]									=  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)












"""simple docstring"""






def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int    ):
					return x if y == 0 else greatest_common_divisor(snake_case      ,       x % y    )





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int    ):
					return (x * y) // greatest_common_divisor(snake_case      ,       snake_case    )





def 							UpperCAmelCase		(		snake_case			:					int = 20    ):
					_lowerCAmelCase:List[Any]									=  1
					for i in range(1      ,       n + 1    ):
										_lowerCAmelCase:List[str]									=  lcm(snake_case      ,       snake_case    )
					return g


if __name__ == "__main__":
				print(F"{solution() = }")






