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]	,					__snake_case     :    Any		):
										super().__init__()
										lowerCamelCase						:Dict						  =     model
										lowerCamelCase						:Union[str, Any]						  =     2
										lowerCamelCase						:Any						  =     nn.Linear(self.model.config.hidden_size	,					self.num_labels		)







							def 				snake_case  (				self     :    Dict		):
										pass





def  _lowerCamelCase					(   a_				: str						,			a_				: str						,			a_				: str):
			lowerCamelCase						:List[Any]						  =     LongformerModel.from_pretrained(__UpperCamelCase)
			lowerCamelCase						:Optional[int]						  =     LightningModel(__UpperCamelCase)

			lowerCamelCase						:List[Any]						  =     torch.load(__UpperCamelCase						,			map_location=torch.device('''cpu'''))
			lightning_model.load_state_dict(ckpt['''state_dict'''])

			# init longformer question answering model
			lowerCamelCase						:Dict						  =     LongformerForQuestionAnswering.from_pretrained(__UpperCamelCase)

			# 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(__UpperCamelCase)

			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
			)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__           =							{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							[
			    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """NllbMoeForConditionalGeneration""",
			    """NllbMoeModel""",
			    """NllbMoePreTrainedModel""",
			    """NllbMoeTop2Router""",
			    """NllbMoeSparseMLP""",
			]


if TYPE_CHECKING:
			from .configuration_nllb_moe import (
			    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    NllbMoeConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_nllb_moe import (
						    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    NllbMoeForConditionalGeneration,
						    NllbMoeModel,
						    NllbMoePreTrainedModel,
						    NllbMoeSparseMLP,
						    NllbMoeTopaRouter,
						)


else:
			import sys

			A__           =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



