


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel
class  SCREAMING_SNAKE_CASE__  (				pl.LightningModule			):
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Dict:
             super().__init__()
             a_					:		Union[str, Any]							=  model
             a_					:		int							=  2
             a_					:		Optional[int]							=  nn.Linear(self.model.config.hidden_size     ,							self.num_labels     )

      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Dict:
             pass







def        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			str  ,    __A :			str				)	->		int:

       """simple docstring"""

       a_					:		Optional[Any]							=  LongformerModel.from_pretrained(__A				)
       a_					:		Optional[int]							=  LightningModel(__A				)

       a_					:		Union[str, Any]							=  torch.load(__A  ,    map_location=torch.device('cpu'				)				)
       lightning_model.load_state_dict(ckpt['state_dict']				)

       # init longformer question answering model
       a_					:		Optional[Any]							=  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__":
   UpperCAmelCase_      : Optional[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.'
   )
   UpperCAmelCase_      : Optional[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 pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.ndarray:
		_a     :      Union[str, Any]        =							cva.getAffineTransform(lowerCAmelCase_						,			lowerCAmelCase_  )
		return cva.warpAffine(lowerCAmelCase_						,			lowerCAmelCase_						,			(rows, cols)  )


if __name__ == "__main__":
							# read original image
							__lowerCAmelCase									=   cva.imread(
							    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
							)
							# turn image in gray scale value
							__lowerCAmelCase									=   cva.cvtColor(image, cva.COLOR_BGR2GRAY)
							# get image shape
							__lowerCAmelCase			,					__lowerCAmelCase									=   gray_img.shape

							# set different points to rotate image
							__lowerCAmelCase									=   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
							__lowerCAmelCase									=   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

							# add all rotated images in a list
							__lowerCAmelCase									=   [
							    gray_img,
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							]

							# plot different image rotations
							__lowerCAmelCase									=   plt.figure(1)
							__lowerCAmelCase									=   ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
							for i, image in enumerate(images):
														plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
														plt.title(titles[i])
														plt.axis('''off''')
														plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
							plt.show()




