
from pathlib import Path

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






def    lowerCamelCase__				(				__lowerCAmelCase  :  np.ndarray    ,							__lowerCAmelCase  :  np.ndarray    ,							__lowerCAmelCase  :  np.ndarray    ,							__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  int				):




			"""simple docstring"""


			lowerCAmelCase_						=	cva.getAffineTransform(__lowerCAmelCase    ,							__lowerCAmelCase				)
			return cva.warpAffine(__lowerCAmelCase    ,							__lowerCAmelCase    ,							(rows, cols)				)


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

			# set different points to rotate image
			_A   =     np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
			_A   =     np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
			_A   =     np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
			_A   =     np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

			# add all rotated images in a list
			_A   =     [
			    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
			_A   =     plt.figure(1)
			_A   =     ["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()





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				,				_UpperCamelCase     )				->      List[str]:
							super().__init__()
							lowerCAmelCase_						=	model
							lowerCAmelCase_						=	2
							lowerCAmelCase_						=	nn.Linear(self.model.config.hidden_size				,				self.num_labels     )




				def      __a						(					self     )				->      Tuple:
							pass






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	LongformerModel.from_pretrained(__lowerCAmelCase				)
			lowerCAmelCase_						=	LightningModel(__lowerCAmelCase				)

			lowerCAmelCase_						=	torch.load(__lowerCAmelCase    ,							map_location=torch.device("cpu"				)				)
			lightning_model.load_state_dict(ckpt["state_dict"]				)

			# init longformer question answering model
			lowerCAmelCase_						=	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   =     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
			)



