




import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__				:      Optional[int]	   = logging.get_logger(__name__)

lowercase__				:      Dict	   = {"vocab_file": "sentencepiece.model"}

lowercase__				:      List[Any]	   = {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

lowercase__				:      List[Any]	   = {
    "google/rembert": 256,
}




class       a__	( UpperCamelCase__				):
     a		:      int		 =     VOCAB_FILES_NAMES
     a		:      Tuple		 =     PRETRAINED_VOCAB_FILES_MAP
     a		:      Dict		 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







     def __init__(     self  ,		A  ,		A=False  ,		A=True  ,		A=True  ,		A="[CLS]"  ,		A="[SEP]"  ,		A="[UNK]"  ,		A="[SEP]"  ,		A="[PAD]"  ,		A="[CLS]"  ,		A="[MASK]"  ,		**A  ,		)  ->   str:



          '''simple docstring'''



          super().__init__(
              do_lower_case=A  ,		remove_space=A  ,		keep_accents=A  ,		bos_token=A  ,		eos_token=A  ,		unk_token=A  ,		sep_token=A  ,		pad_token=A  ,		cls_token=A  ,		mask_token=A  ,		**A  ,		)

          a     =				do_lower_case
          a     =				remove_space
          a     =				keep_accents
          a     =				vocab_file

          a     =				spm.SentencePieceProcessor()
          self.sp_model.Load(A			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          return len(self.sp_model			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				{self.convert_ids_to_tokens(A			): i for i in range(self.vocab_size			)}
          vocab.update(self.added_tokens_encoder			)
          return vocab







     def __getstate__(     self			)  ->   int:



          '''simple docstring'''



          a     =				self.__dict__.copy()
          a     =				None
          return state







     def __setstate__(     self  ,		A			)  ->   int:



          '''simple docstring'''



          a     =				d
          a     =				spm.SentencePieceProcessor()
          self.sp_model.Load(self.vocab_file			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A=False			)  ->   int:



          '''simple docstring'''



          a     =				self.sp_model.EncodeAsPieces(A			)
          return pieces







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          return self.sp_model.PieceToId(A			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          return self.sp_model.IdToPiece(A			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.sp_model.decode_pieces(A			)
          return out_string







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None			)  ->   List[int]:



          '''simple docstring'''



          a     =				[self.sep_token_id]
          a     =				[self.cls_token_id]
          if token_ids_a is None:
               return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None  ,		A = False			)  ->   List[int]:



          '''simple docstring'''



          if already_has_special_tokens:
               if token_ids_a is not None:
                    raise ValueError(
                        "You should not supply a second sequence if the provided sequence of "
                        "ids is already formatted with special tokens for the model."			)
               return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is not None:
               return [1] + ([0] * len(A			)) + [1] + ([0] * len(A			)) + [1]
          return [1] + ([0] * len(A			)) + [1]







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None			)  ->   List[int]:



          '''simple docstring'''



          a     =				[self.sep_token_id]
          a     =				[self.cls_token_id]

          if token_ids_a is None:
               return len(cls + token_ids_a + sep			) * [0]
          return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None			)  ->   Tuple[str]:



          '''simple docstring'''



          if not os.path.isdir(A			):
               logger.error("Vocabulary path ({}) should be a directory".format(A			)			)
               return
          a     =				os.path.join(
              A  ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

          if os.path.abspath(self.vocab_file			) != os.path.abspath(A			):
               copyfile(self.vocab_file  ,		A			)

          return (out_vocab_file,)







import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class       a__	( pl.LightningModule				):







     def __init__(     self  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          super().__init__()
          a     =				model
          a     =				2
          a     =				nn.Linear(self.model.config.hidden_size  ,		self.num_labels			)







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          pass







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
     # load longformer model from model identifier
     a     =				LongformerModel.from_pretrained(__UpperCamelCase)
     a     =				LightningModel(__UpperCamelCase)

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

     # init longformer question answering model
     a     =				LongformerForQuestionAnswering.from_pretrained(__UpperCamelCase)

     # 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(__UpperCamelCase)

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


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

