





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 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    int  		=					["""image_processor""", """tokenizer"""]
   _UpperCamelCase			:    List[str]  		=					"""BridgeTowerImageProcessor"""
   _UpperCamelCase			:    Optional[int]  		=					("""RobertaTokenizer""", """RobertaTokenizerFast""")





   def __init__(			self       ,      snake_case       ,      snake_case   )					->	Dict:
      """simple docstring"""





      super().__init__(snake_case       ,      snake_case   )





   def __call__(			self       ,      snake_case       ,      snake_case = None       ,      snake_case = True       ,      snake_case = False       ,      snake_case = None       ,      snake_case = None       ,      snake_case = 0       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = False       ,      snake_case = False       ,      snake_case = False       ,      snake_case = False       ,      snake_case = True       ,      snake_case = None       ,      **snake_case       ,      )					->	BatchEncoding:
      """simple docstring"""





      a__						:    List[Any]   		=	self.tokenizer(
          text=snake_case       ,      add_special_tokens=snake_case       ,      padding=snake_case       ,      truncation=snake_case       ,      max_length=snake_case       ,      stride=snake_case       ,      pad_to_multiple_of=snake_case       ,      return_token_type_ids=snake_case       ,      return_attention_mask=snake_case       ,      return_overflowing_tokens=snake_case       ,      return_special_tokens_mask=snake_case       ,      return_offsets_mapping=snake_case       ,      return_length=snake_case       ,      verbose=snake_case       ,      return_tensors=snake_case       ,      **snake_case       ,      )
      # add pixel_values + pixel_mask
      a__						:    str   		=	self.image_processor(
          snake_case       ,      return_tensors=snake_case       ,      do_normalize=snake_case       ,      do_center_crop=snake_case       ,      **snake_case   )
      encoding.update(snake_case   )

      return encoding





   def 						_snake_case			(			self       ,      *snake_case       ,      **snake_case   )					->	List[str]:
      """simple docstring"""





      return self.tokenizer.batch_decode(*snake_case       ,      **snake_case   )





   def 						_snake_case			(			self       ,      *snake_case       ,      **snake_case   )					->	List[str]:
      """simple docstring"""





      return self.tokenizer.decode(*snake_case       ,      **snake_case   )





   @property
   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      a__						:    Optional[int]   		=	self.tokenizer.model_input_names
      a__						:    List[Any]   		=	self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )








import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel







class 	__lowerCAmelCase      (       pl.LightningModule	):





   def __init__(			self       ,      snake_case   )					->	Dict:
      """simple docstring"""





      super().__init__()
      a__						:    int   		=	model
      a__						:    Any   		=	2
      a__						:    List[str]   		=	nn.Linear(self.model.config.hidden_size       ,      self.num_labels   )





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      pass







def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # load longformer model from model identifier
   a__						:    List[str]   		=	LongformerModel.from_pretrained(lowerCamelCase )
   a__						:    Dict   		=	LightningModel(lowerCamelCase )

   a__						:    str   		=	torch.load(lowerCamelCase				,			map_location=torch.device("cpu" ) )
   lightning_model.load_state_dict(ckpt["state_dict"] )

   # init longformer question answering model
   a__						:    Dict   		=	LongformerForQuestionAnswering.from_pretrained(lowerCamelCase )

   # 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(lowerCamelCase )

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Any         =    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."""
  )
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

