





'''simple docstring'''







import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel
class 				__SCREAMING_SNAKE_CASE  (pl.LightningModule      ):






				"""simple docstring"""







				def __init__(  self     :					List[Any]					,				__a     :					Optional[Any]					):
								super().__init__()
								_a								= model
								_a								= 2
								_a								= nn.Linear(self.model.config.hidden_size					,				self.num_labels					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								pass






def  _lowerCamelCase				( lowercase						:      str    ,							lowercase						:      str    ,							lowercase						:      str						)	->	Optional[Any]:
				# load longformer model from model identifier
				_a								= LongformerModel.from_pretrained(lowercase						)
				_a								= LightningModel(lowercase						)

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

				# init longformer question answering model
				_a								= LongformerForQuestionAnswering.from_pretrained(lowercase						)

				# 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(lowercase						)

				print(F'Conversion successful. Model saved under {pytorch_dump_folder_path}'						)


if __name__ == "__main__":
					lowerCAmelCase_					:    List[str]      	=			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.'
					)
					lowerCAmelCase_					:    Dict      	=			parser.parse_args()
					convert_longformer_qa_checkpoint_to_pytorch(
					    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
					)







'''simple docstring'''







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      int    ,							lowercase						:      int=1024    ,							lowercase						:      int=1024    ,							lowercase						:      Tuple=False    ,							**lowercase						:      Optional[int]						)	->	Union[str, Any]:
				_a								= AutoTokenizer.from_pretrained(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="train"    ,							**lowercase						)
				_a								= tok.pad_token_id

				def get_lens(lowercase						:      Optional[int]						):
								_a								= tqdm(
								    DataLoader(lowercase    ,							batch_size=512    ,							num_workers=8    ,							shuffle=lowercase    ,							collate_fn=ds.collate_fn						)    ,							desc=str(ds.len_file						)    ,							)
								_a								= []
								for batch in dl:
												_a								= batch["input_ids"].ne(lowercase						).sum(1						).tolist()
												_a								= batch["labels"].ne(lowercase						).sum(1						).tolist()
												if consider_target:
																for src, tgt in zip(lowercase    ,							lowercase						):
																				max_lens.append(max(lowercase    ,							lowercase						)						)
												else:
																max_lens.extend(lowercase						)
								return max_lens

				_a								= get_lens(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="val"    ,							**lowercase						)
				_a								= get_lens(lowercase						)
				pickle_save(lowercase    ,							train_ds.len_file						)
				pickle_save(lowercase    ,							val_ds.len_file						)


if __name__ == "__main__":
					fire.Fire(save_len_file)
