
'''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


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


def A				(__lowerCamelCase				:str			,     __lowerCamelCase				:str		):
						if "xprophetnet" in prophetnet_checkpoint_path:
												_lowerCAmelCase  						=			XLMProphetNetForConditionalGenerationOld.from_pretrained(__lowerCamelCase		)
												_lowerCAmelCase						,   _lowerCAmelCase  						=			XLMProphetNetForConditionalGeneration.from_pretrained(
												    __lowerCamelCase			,     output_loading_info=__lowerCamelCase		)
						else:
												_lowerCAmelCase  						=			ProphetNetForConditionalGenerationOld.from_pretrained(__lowerCamelCase		)
												_lowerCAmelCase						,   _lowerCAmelCase  						=			ProphetNetForConditionalGeneration.from_pretrained(
												    __lowerCamelCase			,     output_loading_info=__lowerCamelCase		)

						_lowerCAmelCase  						=			["""key_proj""", """value_proj""", """query_proj"""]

						_lowerCAmelCase  						=			{
						    """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  						=			key.split("""."""		)

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

												_lowerCAmelCase  						=			False
												for attribute in attributes:
																		if attribute in mapping:
																								_lowerCAmelCase  						=			mapping[attribute]
																								if not hasattr(__lowerCamelCase			,     __lowerCamelCase		) and len(__lowerCamelCase		) > 0:
																														_lowerCAmelCase  						=			attribute
																		elif hasattr(__lowerCamelCase			,     __lowerCamelCase		):
																								_lowerCAmelCase  						=			attribute

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

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

																		if attribute.isdigit():
																								_lowerCAmelCase  						=			model[int(__lowerCamelCase		)]
																								_lowerCAmelCase  						=			old_model[int(__lowerCamelCase		)]
																		else:
																								_lowerCAmelCase  						=			getattr(__lowerCamelCase			,     __lowerCamelCase		)

																								if old_attribute == "":
																														_lowerCAmelCase  						=			old_model
																								else:
																														if not hasattr(__lowerCamelCase			,     __lowerCamelCase		):
																																				raise ValueError(f'{old_model} does not have {old_attribute}'		)
																														_lowerCAmelCase  						=			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__":
						_lowercase    =		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."""
						)
						_lowercase    =		parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


'''simple docstring'''



import logging

from transformers import PretrainedConfig


_lowercase    =		logging.getLogger(__name__)


_lowercase    =		{
    """bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
}




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				Optional[Any]									=       '''bertabs'''




						def __init__( self   ,       _lowercase=30_522   ,       _lowercase=512   ,       _lowercase=6   ,       _lowercase=512   ,       _lowercase=8   ,       _lowercase=512   ,       _lowercase=0.2   ,       _lowercase=6   ,       _lowercase=768   ,       _lowercase=8   ,       _lowercase=2_048   ,       _lowercase=0.2   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(**_lowercase				)

												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			max_pos

												_lowerCAmelCase  						=			enc_layers
												_lowerCAmelCase  						=			enc_hidden_size
												_lowerCAmelCase  						=			enc_heads
												_lowerCAmelCase  						=			enc_ff_size
												_lowerCAmelCase  						=			enc_dropout

												_lowerCAmelCase  						=			dec_layers
												_lowerCAmelCase  						=			dec_hidden_size
												_lowerCAmelCase  						=			dec_heads
												_lowerCAmelCase  						=			dec_ff_size
												_lowerCAmelCase  						=			dec_dropout
