






import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
	_UpperCamelCase			:	Optional[int]								=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')
	_UpperCamelCase			:	str								=parser.parse_args()

	if args.model_type == "bert":
		_UpperCamelCase			:	Optional[Any]								=BertForMaskedLM.from_pretrained(args.model_name)
		_UpperCamelCase			:	Any								='bert'
	else:
		raise ValueError('args.model_type should be "bert".')

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

	for w in ["word_embeddings", "position_embeddings"]:
		_UpperCamelCase			:	Optional[Any]								=state_dict[f'''{prefix}.embeddings.{w}.weight''']
	for w in ["weight", "bias"]:
		_UpperCamelCase			:	Optional[int]								=state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

	_UpperCamelCase			:	Any								=0
	for teacher_idx in [0, 2, 4, 7, 9, 11]:
		for w in ["weight", "bias"]:
			_UpperCamelCase			:	List[str]								=state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
			]
			_UpperCamelCase			:	List[str]								=state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
			]
			_UpperCamelCase			:	Tuple								=state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
			]

			_UpperCamelCase			:	Union[str, Any]								=state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
			]
			_UpperCamelCase			:	Union[str, Any]								=state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
			]

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

	_UpperCamelCase			:	List[Any]								=state_dict['cls.predictions.decoder.weight']
	_UpperCamelCase			:	Any								=state_dict['cls.predictions.bias']
	if args.vocab_transform:
		for w in ["weight", "bias"]:
			_UpperCamelCase			:	List[Any]								=state_dict[f'''cls.predictions.transform.dense.{w}''']
			_UpperCamelCase			:	Any								=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)











from PIL import Image



def  a__ (__lowercase							:Image					,  __lowercase							:float   )		-> Image:

				def brightness(__lowercase							:int   ) -> float:
								return 128 + level + (c - 128)

				if not -255.0 <= level <= 255.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''   )
				return img.point(__lowercase   )


if __name__ == "__main__":
	# Load image
	with Image.open('image_data/lena.jpg') as img:
		# Change brightness to 100
		_UpperCamelCase			:	Any								=change_brightness(img, 100)
		brigt_img.save('image_data/lena_brightness.png', format='png')



