





from pathlib import Path

import numpy as np
from PIL import Image







def        __lowerCamelCase (							__a       :np.ndarray   )		->    np.ndarray:






		"""simple docstring"""

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







def        __lowerCamelCase (							__a       :np.ndarray   )		->    np.ndarray:






		"""simple docstring"""

		return (gray > 1_2_7) & (gray <= 2_5_5)







def        __lowerCamelCase (							__a       :np.ndarray   ,					__a       :np.ndarray   )		->    np.ndarray:






		"""simple docstring"""

		A__  				=       np.zeros_like(__a   )
		A__  				=       np.zeros(
		    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)   )

		# Copy image to padded image
		A__  				=       image

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


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










import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
					A   : List[str]			=				argparse.ArgumentParser(
					    description=(
					        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
					        ''' Distillation'''
					    )
					)
					parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
					parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
					parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
					parser.add_argument('''--vocab_transform''', action='''store_true''')
					A   : Tuple			=				parser.parse_args()

					if args.model_type == "bert":
										A   : Dict			=				BertForMaskedLM.from_pretrained(args.model_name)
										A   : List[str]			=				'''bert'''
					else:
										raise ValueError('''args.model_type should be "bert".''')

					A   : Optional[Any]			=				model.state_dict()
					A   : int			=				{}

					for w in ["word_embeddings", "position_embeddings"]:
										A   : str			=				state_dict[F'''{prefix}.embeddings.{w}.weight''']
					for w in ["weight", "bias"]:
										A   : Any			=				state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

					A   : Tuple			=				0
					for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
										for w in ["weight", "bias"]:
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
															]

															A   : int			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
															]
															A   : List[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
															]

															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
															]
															A   : Union[str, Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
															]
															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
															]
										std_idx += 1

					A   : int			=				state_dict['''cls.predictions.decoder.weight''']
					A   : str			=				state_dict['''cls.predictions.bias''']
					if args.vocab_transform:
										for w in ["weight", "bias"]:
															A   : List[Any]			=				state_dict[F'''cls.predictions.transform.dense.{w}''']
															A   : List[str]			=				state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

					print(F'''N layers selected for distillation: {std_idx}''')
					print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

					print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
					torch.save(compressed_sd, args.dump_checkpoint)



