
"""simple docstring"""






import argparse

import torch

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


logging.set_verbosity_info()



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				# Initialise PyTorch model
				A__							 =       BertConfig.from_json_file(lowerCAmelCase__				)
				print(f'''Building PyTorch model from configuration: {config}'''				)
				A__							 =       BertForPreTraining(lowerCAmelCase__				)

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

				# Save pytorch-model
				print(f'''Save PyTorch model to {pytorch_dump_path}'''				)
				torch.save(model.state_dict()       ,lowerCAmelCase__				)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE						:						Union[str, 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.'''
					)
					SCREAMING_SNAKE_CASE						:						str					   =       parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










"""simple docstring"""






from PIL import Image



def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__ ,			A__							 =       image.size
				A__							 =       0
				A__							 =       image.load()
				for i in range(lowerCAmelCase__				):
								for j in range(lowerCAmelCase__				):
												A__							 =       pixels[j, i]
												mean += pixel
				mean //= width * height

				for j in range(lowerCAmelCase__				):
								for i in range(lowerCAmelCase__				):
												A__							 =       255 if pixels[i, j] > mean else 0
				return image


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE						:						Dict					   =       mean_threshold(Image.open('''path_to_image''').convert('''L'''))
					image.save('''output_image_path''')


