






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def      _lowerCamelCase	(						__A    :							Optional[int] )      ->					List[str]:






    def wrapper(*__A    :							List[str]		,		**__A    :							Dict ):
        _UpperCAmelCase				:      int			  =				timeit.default_timer()
        _UpperCAmelCase				:      Any			  =				func(*a_		,		**a_ )
        _UpperCAmelCase				:      str			  =				timeit.default_timer() - starttime
        return delta

    _UpperCAmelCase				:      int			  =				func.__name__

    return wrapper

def      _lowerCamelCase	(						__A    :							dict		,		__A    :							str=100		,		__A    :							Optional[int]=None )      ->					Tuple:
    _UpperCAmelCase				:      Union[str, Any]			  =				[]
    _UpperCAmelCase				:      Optional[int]			  =				seq_shapes or {}
    for i in range(a_ ):
        _UpperCAmelCase				:      List[Any]			  =				{}
        for col_id, (k, v) in enumerate(features.items() ):
            if isinstance(a_		,		_ArrayXD ):
                _UpperCAmelCase				:      Optional[Any]			  =				np.random.rand(*v.shape ).astype(v.dtype )
            elif isinstance(a_		,		datasets.Value ):
                if v.dtype == "string":
                    _UpperCAmelCase				:      str			  =				'''The small grey turtle was surprisingly fast when challenged.'''
                else:
                    _UpperCAmelCase				:      Union[str, Any]			  =				np.random.randint(10		,		size=1 ).astype(v.dtype ).item()
            elif isinstance(a_		,		datasets.Sequence ):
                while isinstance(a_		,		datasets.Sequence ):
                    _UpperCAmelCase				:      Optional[int]			  =				v.feature
                _UpperCAmelCase				:      List[Any]			  =				seq_shapes[k]
                _UpperCAmelCase				:      Any			  =				np.random.rand(*a_ ).astype(v.dtype )
            _UpperCAmelCase				:      Tuple			  =				data

        dummy_data.append((i, example) )

    return dummy_data


def      _lowerCamelCase	(						__A    :							List[Any]		,		__A    :							Dict		,		__A    :							List[str]=100		,		__A    :							Union[str, Any]=None )      ->					str:
    _UpperCAmelCase				:      Optional[Any]			  =				generate_examples(a_		,		num_examples=a_		,		seq_shapes=a_ )

    with ArrowWriter(features=a_		,		path=a_ ) as writer:
        for key, record in dummy_data:
            _UpperCAmelCase				:      int			  =				features.encode_example(a_ )
            writer.write(a_ )

        _UpperCAmelCase				:      Any			  =				writer.finalize()

    if not num_final_examples == num_examples:
        raise ValueError(
            f'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.''' )

    _UpperCAmelCase				:      Union[str, Any]			  =				datasets.Dataset.from_file(filename=a_		,		info=datasets.DatasetInfo(features=a_ ) )

    return dataset












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							,  _A) ->		List[str]:

     """simple docstring"""



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


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

     """simple docstring"""



     pass



def      _lowerCamelCase	(						__A    :							str		,		__A    :							str		,		__A    :							str )      ->					List[str]:
    # load longformer model from model identifier
    _UpperCAmelCase				:      Optional[Any]			  =				LongformerModel.from_pretrained(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				LightningModel(__A )

    _UpperCAmelCase				:      Optional[int]			  =				torch.load(__A		,		map_location=torch.device('''cpu''' ) )
    lightning_model.load_state_dict(ckpt['''state_dict'''] )

    # init longformer question answering model
    _UpperCAmelCase				:      int			  =				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__":
  SCREAMING_SNAKE_CASE 							=						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.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )




