






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					,UpperCamelCase       )						->    Dict:
					super().__init__()
					snake_case__      :Union[str, Any]         =   model
					snake_case__      :List[str]         =   2
					snake_case__      :Optional[int]         =   nn.Linear(self.model.config.hidden_size					,self.num_labels       )






	def    lowerCAmelCase_							(		self       )						->    Dict:
					pass





def 					lowercase_ (    __snake_case     :							str	,	__snake_case     :							str	,	__snake_case     :							str      )     -> List[Any]:





				'''simple docstring'''


				snake_case__      :Tuple         =   LongformerModel.from_pretrained(__snake_case      )
				snake_case__      :Optional[Any]         =   LightningModel(__snake_case      )

				snake_case__      :List[str]         =   torch.load(__snake_case	,	map_location=torch.device("cpu"      )      )
				lightning_model.load_state_dict(ckpt["state_dict"]      )

				# init longformer question answering model
				snake_case__      :Tuple         =   LongformerForQuestionAnswering.from_pretrained(__snake_case      )

				# 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(__snake_case      )

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


if __name__ == "__main__":
	__UpperCAmelCase							:   Tuple							=  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							:   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
	)







from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
	from .multicontrolnet import MultiControlNetModel
	from .pipeline_controlnet import StableDiffusionControlNetPipeline
	from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
	from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
	from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline