

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel


class  A						(							pl.LightningModule ):







    """simple docstring"""





    def __init__(      self :       Dict,lowercase_ :       Dict		)->    Dict:





        '''simple docstring'''







        super().__init__()
        A__          =   model
        A__          =   2
        A__          =   nn.Linear(self.model.config.hidden_size,self.num_labels		)


    def 						snake_case__     (      self :       str		)->    Optional[Any]:





        '''simple docstring'''







        pass




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str      )							->      Any:





    '''simple docstring'''






    A__          =   LongformerModel.from_pretrained(SCREAMING_SNAKE_CASE__      )
    A__          =   LightningModel(SCREAMING_SNAKE_CASE__      )

    A__          =   torch.load(SCREAMING_SNAKE_CASE__    ,			map_location=torch.device('cpu'      )      )
    lightning_model.load_state_dict(ckpt['state_dict']      )

    # init longformer question answering model
    A__          =   LongformerForQuestionAnswering.from_pretrained(SCREAMING_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(SCREAMING_SNAKE_CASE__      )

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


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




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}


class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'resnet'
    lowerCamelCase			=							['basic', 'bottleneck']


    def __init__(      self :       Optional[Any],lowercase_ :       int=3,lowercase_ :       List[str]=6_4,lowercase_ :       int=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8],lowercase_ :       Tuple=[3, 4, 6, 3],lowercase_ :       Union[str, Any]="bottleneck",lowercase_ :       List[str]="relu",lowercase_ :       Tuple=False,lowercase_ :       List[str]=None,lowercase_ :       List[Any]=None,**lowercase_ :       str,)->    Optional[Any]:





        '''simple docstring'''







        super().__init__(**lowercase_		)
        if layer_type not in self.layer_types:
            raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
        A__          =   num_channels
        A__          =   embedding_size
        A__          =   hidden_sizes
        A__          =   depths
        A__          =   layer_type
        A__          =   hidden_act
        A__          =   downsample_in_first_stage
        A__          =   ['stem'] + [F'stage{idx}' for idx in range(1,len(lowercase_		) + 1		)]
        A__      ,						A__          =   get_aligned_output_features_output_indices(
            out_features=lowercase_,out_indices=lowercase_,stage_names=self.stage_names		)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							version.parse('1.11' )


    @property
    def 						snake_case__     (      self :       List[Any]		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ]		)


    @property
    def 						snake_case__     (      self :       Any		)->    float:





        '''simple docstring'''







        return 1E-3

