


'''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


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


def 			a      (			__a ,					__a   )  ->	List[str]:



					'''simple docstring'''






					if "xprophetnet" in prophetnet_checkpoint_path:
										UpperCamelCase__   :Optional[Any]												= XLMProphetNetForConditionalGenerationOld.from_pretrained(lowercase__   )
										UpperCamelCase__					,				UpperCamelCase__   :Dict												= XLMProphetNetForConditionalGeneration.from_pretrained(
										    lowercase__ ,					output_loading_info=lowercase__   )
					else:
										UpperCamelCase__   :Dict												= ProphetNetForConditionalGenerationOld.from_pretrained(lowercase__   )
										UpperCamelCase__					,				UpperCamelCase__   :List[Any]												= ProphetNetForConditionalGeneration.from_pretrained(
										    lowercase__ ,					output_loading_info=lowercase__   )

					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__   :List[str]												= key.split('''.'''   )

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

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

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

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

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

																				if old_attribute == "":
																									UpperCamelCase__   :Optional[Any]												= old_model
																				else:
																									if not hasattr(lowercase__ ,					lowercase__   ):
																														raise ValueError(f'''{old_model} does not have {old_attribute}'''   )
																									UpperCamelCase__   :Union[str, Any]												= getattr(lowercase__ ,					lowercase__   )

										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(lowercase__   )


if __name__ == "__main__":
		__snake_case							      =     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.'''
		)
		__snake_case							      =     parser.parse_args()
		convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)



def        _lowerCamelCase(	lowercase__   , lowercase__ = " "	)						->	list:

		'''simple docstring'''







		__lowercase=			[]

		__lowercase=			0
		for index, char in enumerate(lowercase__	):
				if char == separator:
						split_words.append(string[last_index:index]	)
						__lowercase=			index + 1
				elif index + 1 == len(lowercase__	):
						split_words.append(string[last_index : index + 1]	)
		return split_words


if __name__ == "__main__":
	from doctest import testmod

	testmod()






