
"""simple docstring"""






import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class       snake_case_       (    pl.LightningModule       ):



	"""simple docstring"""

	def __init__(      self  ,					__a						):


					"""simple docstring"""

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






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					pass





def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       LongformerModel.from_pretrained(lowerCamelCase__				)
				A__							 =       LightningModel(lowerCamelCase__				)

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

				# init longformer question answering model
				A__							 =       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__":
					SCREAMING_SNAKE_CASE						:						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.'''
					)
					SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       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 PIL import Image



def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__ ,			A__							 =       image.size
				A__							 =       0
				A__							 =       image.load()
				for i in range(lowerCAmelCase__				):
								for j in range(lowerCAmelCase__				):
												A__							 =       pixels[j, i]
												mean += pixel
				mean //= width * height

				for j in range(lowerCAmelCase__				):
								for i in range(lowerCAmelCase__				):
												A__							 =       255 if pixels[i, j] > mean else 0
				return image


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE						:						Dict					   =       mean_threshold(Image.open('''path_to_image''').convert('''L'''))
					image.save('''output_image_path''')


