


from pathlib import Path

import numpy as np
from PIL import Image




def 					lowercase__				(					__snake_case   :							np.ndarray  ):


					'''simple docstring'''


					UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
					return 0.2989 * r + 0.5870 * g + 0.1140 * b




def 					lowercase__				(					__snake_case   :							np.ndarray  ):


					'''simple docstring'''


					return (gray > 127) & (gray <= 255)




def 					lowercase__				(					__snake_case   :							np.ndarray					,	__snake_case   :							np.ndarray  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						np.zeros_like(__snake_case  )
					UpperCAmelCase_   :				List[Any]						=						np.zeros(
					    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)  )

					# Copy image to padded image
					UpperCAmelCase_   :				Union[str, Any]						=						image

					# Iterate over image & apply kernel
					for x in range(image.shape[1]  ):
										for y in range(image.shape[0]  ):
															UpperCAmelCase_   :				Optional[Any]						=						(
															    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
															).sum()
															UpperCAmelCase_   :				Union[str, Any]						=						int(summation > 0  )
					return output


if __name__ == "__main__":
		# read original image
		__UpperCAmelCase     							=						Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
		__UpperCAmelCase     							=						np.array(Image.open(lena_path))
		# kernel to be applied
		__UpperCAmelCase     							=						np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
		__UpperCAmelCase     							=						dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
		# Save the output image
		__UpperCAmelCase     							=						Image.fromarray(output).convert('RGB')
		pil_img.save('result_dilation.png')














import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()




def 					lowercase__				(					__snake_case   :							Optional[Any]					,	__snake_case   :							Any					,	__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						RemBertConfig.from_json_file(__snake_case  )
					print('Building PyTorch model from configuration: {}'.format(str(__snake_case  )  )  )
					UpperCAmelCase_   :				Dict						=						RemBertModel(__snake_case  )

					# Load weights from tf checkpoint
					load_tf_weights_in_rembert(__snake_case					,	__snake_case					,	__snake_case  )

					# Save pytorch-model
					print('Save PyTorch model to {}'.format(__snake_case  )  )
					torch.save(model.state_dict()					,	__snake_case  )


if __name__ == "__main__":
		__UpperCAmelCase     							=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--rembert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained RemBERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__UpperCAmelCase     							=						parser.parse_args()
		convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)






