




from copy import deepcopy







class __snake_case			:







   def __init__(      self		:						List[str]					,						_snake_case		:						list[int] | None = None					,						_snake_case		:						int | None = None):

        """simple docstring"""


        if arr is None and size is not None:
             UpperCAmelCase_          =							size
             UpperCAmelCase_          =							[0] * size
        elif arr is not None:
             self.init(_snake_case)
        else:
             raise ValueError('''Either arr or size must be specified''')







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						list[int]):

        """simple docstring"""


        UpperCAmelCase_          =							len(_snake_case)
        UpperCAmelCase_          =							deepcopy(_snake_case)
        for i in range(1					,						self.size):
             UpperCAmelCase_          =							self.next_(_snake_case)
             if j < self.size:
                  self.tree[j] += self.tree[i]







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.tree[:]
        for i in range(self.size - 1					,						0					,						-1):
             UpperCAmelCase_          =							self.next_(_snake_case)
             if j < self.size:
                  arr[j] -= arr[i]
        return arr







   @staticmethod
   def lowerCamelCase						(      _snake_case		:						int):

        """simple docstring"""


        return index + (index & (-index))







   @staticmethod
   def lowerCamelCase						(      _snake_case		:						int):

        """simple docstring"""


        return index - (index & (-index))







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						int					,						_snake_case		:						int):

        """simple docstring"""


        if index == 0:
             self.tree[0] += value
             return
        while index < self.size:
             self.tree[index] += value
             UpperCAmelCase_          =							self.next_(_snake_case)







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						int					,						_snake_case		:						int):

        """simple docstring"""


        self.add(_snake_case					,						value - self.get(_snake_case))







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						int):

        """simple docstring"""


        if right == 0:
             return 0
        UpperCAmelCase_          =							self.tree[0]
        right -= 1  # make right inclusive
        while right > 0:
             result += self.tree[right]
             UpperCAmelCase_          =							self.prev(_snake_case)
        return result







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						int					,						_snake_case		:						int):

        """simple docstring"""


        return self.prefix(_snake_case) - self.prefix(_snake_case)







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						int):

        """simple docstring"""


        return self.query(_snake_case					,						index + 1)







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						int):

        """simple docstring"""


        value -= self.tree[0]
        if value < 0:
             return -1

        UpperCAmelCase_          =							1  # Largest power of 2 <= size
        while j * 2 < self.size:
             j *= 2

        UpperCAmelCase_          =							0

        while j > 0:
             if i + j < self.size and self.tree[i + j] <= value:
                  value -= self.tree[i + j]
                  i += j
             j //= 2
        return i


if __name__ == "__main__":
    import doctest

    doctest.testmod()












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						:Union[str, Any]		,      __magic_name__						:Optional[int]				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								pass







def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Optional[int]:
	# load longformer model from model identifier
	a										=							LongformerModel.from_pretrained(__lowerCamelCase    )
	a										=							LightningModel(__lowerCamelCase    )

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

	# init longformer question answering model
	a										=							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__":
		__UpperCamelCase					:       List[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."
		)
		__UpperCamelCase					:       List[Any]    					=      parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)

