




'''simple docstring'''

import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   List[Any]:

					'''simple docstring'''

					snake_case_		     =							MobileBertConfig.from_json_file(__UpperCAmelCase      )
					print(F"Building PyTorch model from configuration: {config}"      )
					snake_case_		     =							MobileBertForPreTraining(__UpperCAmelCase      )
					# Load weights from tf checkpoint
					snake_case_		     =							load_tf_weights_in_mobilebert(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
					# Save pytorch-model
					print(F"Save PyTorch model to {pytorch_dump_path}"      )
					torch.save(model.state_dict(), __UpperCAmelCase      )


if __name__ == "__main__":
							a    :							Dict										=    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(
							    '--mobilebert_config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained MobileBERT 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    :							int										=    parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)







'''simple docstring'''

from PIL import Image


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Image:

					'''simple docstring'''

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

					if not -2_5_5.0 <= level <= 2_5_5.0:
										raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''      )
					return img.point(__UpperCAmelCase      )


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

