

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class      UpperCamelCase (     pl.LightningModule   ):

				def __init__(			self :      Optional[Any]							,    snake_case__ :      str    ):

					"""simple docstring"""






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




				def UpperCamelCase			(			self :      List[Any]    ):

					"""simple docstring"""






					pass


def        __lowerCAmelCase    (       _UpperCamelCase		:  int		,							_UpperCamelCase		:  Optional[Any]		,							_UpperCamelCase		:  Optional[int]	)		->							Optional[Any]:
	'''simple docstring'''


	SCREAMING_SNAKE_CASE								=			LongformerModel.from_pretrained(lowerCamelCase__	)
	SCREAMING_SNAKE_CASE								=			LightningModel(lowerCamelCase__	)

	SCREAMING_SNAKE_CASE								=			torch.load(lowerCamelCase__		,							map_location=torch.device('cpu'	)	)
	lightning_model.load_state_dict(ckpt['state_dict']	)

	# init longformer question answering model
	SCREAMING_SNAKE_CASE								=			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__":
						a_    :						Union[str, 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."
						)
						a_    :						int    		=      parser.parse_args()
						convert_longformer_qa_checkpoint_to_pytorch(
						    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
						)




from ....configuration_utils import PretrainedConfig
from ....utils import logging


a_    :						Any    		=      logging.get_logger(__name__)

a_    :						Dict    		=      {
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="van"

    def __init__(			self :      Optional[Any]							,    snake_case__ :      Tuple=2_2_4							,    snake_case__ :      Dict=3							,    snake_case__ :      Union[str, Any]=[7, 3, 3, 3]							,    snake_case__ :      str=[4, 2, 2, 2]							,    snake_case__ :      Optional[Any]=[6_4, 1_2_8, 3_2_0, 5_1_2]							,    snake_case__ :      Optional[Any]=[3, 3, 1_2, 3]							,    snake_case__ :      Tuple=[8, 8, 4, 4]							,    snake_case__ :      Any="gelu"							,    snake_case__ :      Dict=0.02							,    snake_case__ :      List[str]=1E-6							,    snake_case__ :      int=1E-2							,    snake_case__ :      Any=0.0							,    snake_case__ :      Tuple=0.0							,    **snake_case__ :      Any							,    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			image_size
     SCREAMING_SNAKE_CASE								=			num_channels
     SCREAMING_SNAKE_CASE								=			patch_sizes
     SCREAMING_SNAKE_CASE								=			strides
     SCREAMING_SNAKE_CASE								=			hidden_sizes
     SCREAMING_SNAKE_CASE								=			depths
     SCREAMING_SNAKE_CASE								=			mlp_ratios
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			layer_norm_eps
     SCREAMING_SNAKE_CASE								=			layer_scale_init_value
     SCREAMING_SNAKE_CASE								=			drop_path_rate
     SCREAMING_SNAKE_CASE								=			dropout_rate

