

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()





def 	__a     (      A__			:  List[str]			,  A__			:  Optional[Any]			,  A__			:  Optional[int]       ):
    # Initialise PyTorch model
    SCREAMING_SNAKE_CASE										=   BertConfig.from_json_file(A__       )
    print(F"Building PyTorch model from configuration: {config}"       )
    SCREAMING_SNAKE_CASE										=   BertForPreTraining(A__       )

    # Load weights from tf checkpoint
    load_tf_weights_in_bert(A__			,  A__			,  A__       )

    # Save pytorch-model
    print(F"Save PyTorch model to {pytorch_dump_path}"       )
    torch.save(model.state_dict()			,  A__       )


if __name__ == "__main__":
    __A			:					Optional[Any]						=			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(
        '--bert_config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained BERT 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.'
    )
    __A			:					Optional[Any]						=			parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







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')



