



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


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


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

				lowercase__					:   Any													=					["key_proj", "value_proj", "query_proj"]

				lowercase__					:   Optional[Any]													=					{
				    "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"]:
								lowercase__					:   Optional[int]													=					key.split("."						)

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

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

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

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

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

																if old_attribute == "":
																				lowercase__					:   Union[str, Any]													=					old_model
																else:
																				if not hasattr(lowerCamelCase__					,			lowerCamelCase__						):
																								raise ValueError(F"""{old_model} does not have {old_attribute}"""						)
																				lowercase__					:   Dict													=					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__					  =  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__					  =  parser.parse_args()
				convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)









def  __lowerCamelCase (       lowerCamelCase__ = 1_000						):

				"""simple docstring"""


				lowercase__							,							lowercase__					:   int													=					1, 1
				lowercase__					:   List[Any]													=					[]
				for i in range(1					,			n + 1						):
								lowercase__					:   Dict													=					prev_numerator + 2 * prev_denominator
								lowercase__					:   Tuple													=					prev_numerator + prev_denominator
								if len(str(lowerCamelCase__						)						) > len(str(lowerCamelCase__						)						):
												result.append(lowerCamelCase__						)
								lowercase__					:   int													=					numerator
								lowercase__					:   int													=					denominator

				return len(lowerCamelCase__						)


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




