


'''simple docstring'''



import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel


class 						_snake_case   (       pl.LightningModule		):




					def __init__( self							,							_lowerCamelCase):
								super().__init__()
								UpperCAmelCase__					:   List[str]       				=       model
								UpperCAmelCase__					:   Tuple       				=       2
								UpperCAmelCase__					:   int       				=       nn.Linear(self.model.config.hidden_size							,							self.num_labels)




					def 			snake_case__  ( self):
								pass







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ) ->    Optional[Any]:
			UpperCAmelCase__					:   int       				=       LongformerModel.from_pretrained(__A   )
			UpperCAmelCase__					:   Any       				=       LightningModel(__A   )

			UpperCAmelCase__					:   Optional[int]       				=       torch.load(__A	, map_location=torch.device("""cpu"""   )   )
			lightning_model.load_state_dict(ckpt["""state_dict"""]   )

			# init longformer question answering model
			UpperCAmelCase__					:   List[Any]       				=       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__":
		__A		=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.'
		)
		__A		=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'''



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 						_snake_case   (       a__		):
					lowerCAmelCase :Optional[int]						=   ['''image_processor''', '''tokenizer''']
					lowerCAmelCase :Optional[int]						=   '''BridgeTowerImageProcessor'''
					lowerCAmelCase :List[str]						=   ('''RobertaTokenizer''', '''RobertaTokenizerFast''')




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase):
								super().__init__(_lowerCamelCase							,							_lowerCamelCase)




					def __call__( self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = True							,							_lowerCamelCase = False							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = 0							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   List[str]       				=       self.tokenizer(
								    text=_lowerCamelCase							,							add_special_tokens=_lowerCamelCase							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase							,							max_length=_lowerCamelCase							,							stride=_lowerCamelCase							,							pad_to_multiple_of=_lowerCamelCase							,							return_token_type_ids=_lowerCamelCase							,							return_attention_mask=_lowerCamelCase							,							return_overflowing_tokens=_lowerCamelCase							,							return_special_tokens_mask=_lowerCamelCase							,							return_offsets_mapping=_lowerCamelCase							,							return_length=_lowerCamelCase							,							verbose=_lowerCamelCase							,							return_tensors=_lowerCamelCase							,							**_lowerCamelCase							,							)
								# add pixel_values + pixel_mask
								UpperCAmelCase__					:   Optional[Any]       				=       self.image_processor(
								    _lowerCamelCase							,							return_tensors=_lowerCamelCase							,							do_normalize=_lowerCamelCase							,							do_center_crop=_lowerCamelCase							,							**_lowerCamelCase)
								encoding.update(_lowerCamelCase)

								return encoding




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.batch_decode(*_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.decode(*_lowerCamelCase							,							**_lowerCamelCase)




					@property
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       self.tokenizer.model_input_names
								UpperCAmelCase__					:   str       				=       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))