



"""simple docstring"""
import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel
class 				lowerCamelCase  (			pl.LightningModule				):






					'''simple docstring'''





					def __init__(       self:							str						,     snake_case:							List[str]						)   ->	Tuple:
											super().__init__()
											snake_case_ :Any						=			model
											snake_case_ :List[Any]						=			2
											snake_case_ :str						=			nn.Linear(self.model.config.hidden_size						,     self.num_labels						)






					def    lowerCAmelCase_				(       self:							str						)   ->	Dict:
											pass

def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Optional[Any]						=			LongformerModel.from_pretrained(_lowercase	)
						snake_case_ :Dict						=			LightningModel(_lowercase	)

						snake_case_ :int						=			torch.load(_lowercase,					map_location=torch.device("""cpu"""	)	)
						lightning_model.load_state_dict(ckpt["""state_dict"""]	)

						# init longformer question answering model
						snake_case_ :List[str]						=			LongformerForQuestionAnswering.from_pretrained(_lowercase	)

						# 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(_lowercase	)

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


if __name__ == "__main__":
						__a      =				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      =				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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__a      =				{
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["Mask2FormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "Mask2FormerForUniversalSegmentation",
						    "Mask2FormerModel",
						    "Mask2FormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_maskaformer import MaskaFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_maskaformer import (
												    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MaskaFormerForUniversalSegmentation,
												    MaskaFormerModel,
												    MaskaFormerPreTrainedModel,
												)


else:
						import sys

						__a      =				_LazyModule(__name__, globals()["__file__"], _import_structure)
