





import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class        __SCREAMING_SNAKE_CASE					(     pl.LightningModule       ):






			def __init__(						self  , __lowerCAmelCase  ):
										super().__init__()
										UpperCamelCase__				     =			model
										UpperCamelCase__				     =			2
										UpperCamelCase__				     =			nn.Linear(self.model.config.hidden_size  , self.num_labels  )







			def 				_lowerCamelCase		(						self  ):
										pass






def     _UpperCamelCase     (a__   :str   ,			a__   :str   ,			a__   :str       ):






							"""simple docstring"""





							UpperCamelCase__				     =			LongformerModel.from_pretrained(A__       )
							UpperCamelCase__				     =			LightningModel(A__       )

							UpperCamelCase__				     =			torch.load(A__   ,			map_location=torch.device("""cpu"""       )       )
							lightning_model.load_state_dict(ckpt["""state_dict"""]       )

							# init longformer question answering model
							UpperCamelCase__				     =			LongformerForQuestionAnswering.from_pretrained(A__       )

							# transfer weights
							longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()       )
							longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()       )
							longformer_for_qa.eval()

							# save model
							longformer_for_qa.save_pretrained(A__       )

							print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""       )


if __name__ == "__main__":
					UpperCamelCase__											= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--longformer_model",
					    default=None,
					    type=str,
					    required=True,
					    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
					)
					parser.add_argument(
					    "--longformer_question_answering_ckpt_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path the official PyTorch Lightning Checkpoint.",
					)
					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_longformer_qa_checkpoint_to_pytorch(
					    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
					)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__											= logging.get_logger(__name__)

UpperCamelCase__											= {
    "studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
    "studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}

class        __SCREAMING_SNAKE_CASE					(     _a       ):
							snake_case     :      Optional[int]						     =       """luke"""






							def __init__(						self  , __lowerCAmelCase=50267  , __lowerCAmelCase=500000  , __lowerCAmelCase=768  , __lowerCAmelCase=256  , __lowerCAmelCase=12  , __lowerCAmelCase=12  , __lowerCAmelCase=3072  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=512  , __lowerCAmelCase=2  , __lowerCAmelCase=0.02  , __lowerCAmelCase=1E-12  , __lowerCAmelCase=True  , __lowerCAmelCase=None  , __lowerCAmelCase=1  , __lowerCAmelCase=0  , __lowerCAmelCase=2  , **__lowerCAmelCase  , ):
														super().__init__(pad_token_id=__lowerCAmelCase  , bos_token_id=__lowerCAmelCase  , eos_token_id=__lowerCAmelCase  , **__lowerCAmelCase  )

														UpperCamelCase__				     =			vocab_size
														UpperCamelCase__				     =			entity_vocab_size
														UpperCamelCase__				     =			hidden_size
														UpperCamelCase__				     =			entity_emb_size
														UpperCamelCase__				     =			num_hidden_layers
														UpperCamelCase__				     =			num_attention_heads
														UpperCamelCase__				     =			hidden_act
														UpperCamelCase__				     =			intermediate_size
														UpperCamelCase__				     =			hidden_dropout_prob
														UpperCamelCase__				     =			attention_probs_dropout_prob
														UpperCamelCase__				     =			max_position_embeddings
														UpperCamelCase__				     =			type_vocab_size
														UpperCamelCase__				     =			initializer_range
														UpperCamelCase__				     =			layer_norm_eps
														UpperCamelCase__				     =			use_entity_aware_attention
														UpperCamelCase__				     =			classifier_dropout




