


'''simple docstring'''
import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class        _UpperCamelCase	(    pl.LightningModule):


				'''simple docstring'''


				def __init__(     self							,      a_							)    ->  Union[str, Any]:
					super().__init__()
					lowercase						:		Optional[Any]   					=		model
					lowercase						:		Optional[Any]   					=		2
					lowercase						:		int   					=		nn.Linear(self.model.config.hidden_size							,      self.num_labels							)


				def 	a__		(     self							)    ->  List[Any]:
					pass




def 	_A  (       A							,A							,A							)  ->				List[str]:
	# load longformer model from model identifier
	lowercase						:		Optional[int]   					=		LongformerModel.from_pretrained(A							)
	lowercase						:		Optional[Any]   					=		LightningModel(A							)

	lowercase						:		List[Any]   					=		torch.load(A							,map_location=torch.device("cpu"							)							)
	lightning_model.load_state_dict(ckpt["state_dict"]							)

	# init longformer question answering model
	lowercase						:		Dict   					=		LongformerForQuestionAnswering.from_pretrained(A							)

	# 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(A							)

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


if __name__ == "__main__":
					lowerCAmelCase   : 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."""
					)
					lowerCAmelCase   : int             =				parser.parse_args()
					convert_longformer_qa_checkpoint_to_pytorch(
					    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
					)






'''simple docstring'''
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


