'''simple docstring'''




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel






class 		__A       (pl.LightningModule   ):






       def __init__(      self				,     UpperCamelCase_      ):
              super().__init__()
              __UpperCAmelCase  :				Dict       				=		model
              __UpperCAmelCase  :				str       				=		2
              __UpperCAmelCase  :				Any       				=		nn.Linear(self.model.config.hidden_size				,     self.num_labels      )







       def 			_snake_case						(      self      ):
              pass





def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Optional[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				List[Any]       				=		LongformerModel.from_pretrained(lowerCamelCase__						)
       __UpperCAmelCase  :				Dict       				=		LightningModel(lowerCamelCase__						)

       __UpperCAmelCase  :				str       				=		torch.load(lowerCamelCase__							,    map_location=torch.device("cpu"						)						)
       lightning_model.load_state_dict(ckpt["state_dict"]						)

       # init longformer question answering model
       __UpperCAmelCase  :				List[Any]       				=		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
   )







'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a   :	List[str] 							=   logging.get_logger(__name__)

_a   :	Any 							=   {
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}






class 		__A       (__magic_name__   ):
       snake_case       :Union[str, Any]				    =					"ibert"






       def __init__(      self				,     UpperCamelCase_=3_05_22				,     UpperCamelCase_=7_68				,     UpperCamelCase_=12				,     UpperCamelCase_=12				,     UpperCamelCase_=30_72				,     UpperCamelCase_="gelu"				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.1				,     UpperCamelCase_=5_12				,     UpperCamelCase_=2				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=1E-12				,     UpperCamelCase_=1				,     UpperCamelCase_=0				,     UpperCamelCase_=2				,     UpperCamelCase_="absolute"				,     UpperCamelCase_=False				,     UpperCamelCase_="none"				,     **UpperCamelCase_				,     ):
              super().__init__(pad_token_id=UpperCamelCase_				,     bos_token_id=UpperCamelCase_				,     eos_token_id=UpperCamelCase_				,     **UpperCamelCase_      )

              __UpperCAmelCase  :				List[Any]       				=		vocab_size
              __UpperCAmelCase  :				Optional[Any]       				=		hidden_size
              __UpperCAmelCase  :				List[Any]       				=		num_hidden_layers
              __UpperCAmelCase  :				Any       				=		num_attention_heads
              __UpperCAmelCase  :				List[str]       				=		hidden_act
              __UpperCAmelCase  :				List[str]       				=		intermediate_size
              __UpperCAmelCase  :				Optional[int]       				=		hidden_dropout_prob
              __UpperCAmelCase  :				Union[str, Any]       				=		attention_probs_dropout_prob
              __UpperCAmelCase  :				str       				=		max_position_embeddings
              __UpperCAmelCase  :				List[str]       				=		type_vocab_size
              __UpperCAmelCase  :				Dict       				=		initializer_range
              __UpperCAmelCase  :				Optional[int]       				=		layer_norm_eps
              __UpperCAmelCase  :				Any       				=		position_embedding_type
              __UpperCAmelCase  :				Tuple       				=		quant_mode
              __UpperCAmelCase  :				Union[str, Any]       				=		force_dequant






class 		__A       (__magic_name__   ):






       @property
       def 			_snake_case						(      self      ):
              if self.task == "multiple-choice":
                     __UpperCAmelCase  :				Optional[int]       				=		{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     __UpperCAmelCase  :				Optional[int]       				=		{0: "batch", 1: "sequence"}
              return OrderedDict(
                  [
                      ("input_ids", dynamic_axis),
                      ("attention_mask", dynamic_axis),
                  ]      )






