

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


_lowerCAmelCase  :      Optional[Any]			 =logging.get_logger(__name__)
logging.set_verbosity_info()



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	if "xprophetnet" in prophetnet_checkpoint_path:
		UpperCAmelCase__:	List[Any]									= XLMProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase      )
		UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= XLMProphetNetForConditionalGeneration.from_pretrained(
		    _lowerCAmelCase	,output_loading_info=_lowerCAmelCase      )
	else:
		UpperCAmelCase__:	int									= ProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase      )
		UpperCAmelCase__							,						UpperCAmelCase__:	List[str]									= ProphetNetForConditionalGeneration.from_pretrained(
		    _lowerCAmelCase	,output_loading_info=_lowerCAmelCase      )

	UpperCAmelCase__:	Optional[Any]									= ["key_proj", "value_proj", "query_proj"]

	UpperCAmelCase__:	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"]:
		UpperCAmelCase__:	Optional[int]									= key.split("."      )

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

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

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

				elif attribute == "key_proj":
					UpperCAmelCase__:	str									= nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
					UpperCAmelCase__:	int									= nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
				elif attribute == "value_proj":
					UpperCAmelCase__:	Optional[int]									= nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
					UpperCAmelCase__:	Optional[int]									= nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]      )
				UpperCAmelCase__:	Tuple									= 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] == 5_1_2, "We want 512 position_embeddings."
				UpperCAmelCase__:	Dict									= nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]      )
				UpperCAmelCase__:	str									= True
				break

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

				if old_attribute == "":
					UpperCAmelCase__:	List[str]									= old_model
				else:
					if not hasattr(_lowerCAmelCase	,_lowerCAmelCase      ):
						raise ValueError(f"{old_model} does not have {old_attribute}"      )
					UpperCAmelCase__:	Optional[Any]									= getattr(_lowerCAmelCase	,_lowerCAmelCase      )

		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(_lowerCAmelCase      )


if __name__ == "__main__":
					_lowerCAmelCase  :      Tuple			 =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."""
					)
					_lowerCAmelCase  :      int			 =parser.parse_args()
					convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

"""simple docstring"""
from __future__ import annotations

def  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  2
						__lowerCAmelCase     =  []
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(_lowerCAmelCase						)
						if n > 1:
												factors.append(_lowerCAmelCase						)
						return factors


if __name__ == "__main__":
			import doctest

			doctest.testmod()
