
"""simple docstring"""






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

				A__							 =       (
				    ('layer.', 'layer_'),
				    ('word_embeddings.weight', 'word_embeddings'),
				    ('position_embeddings.weight', 'position_embeddings'),
				    ('token_type_embeddings.weight', 'token_type_embeddings'),
				    ('.', '/'),
				    ('LayerNorm/weight', 'LayerNorm/gamma'),
				    ('LayerNorm/bias', 'LayerNorm/beta'),
				    ('weight', 'kernel'),
				)

				if not os.path.isdir(lowerCAmelCase__				):
								os.makedirs(lowerCAmelCase__				)

				A__							 =       model.state_dict()

				def to_tf_var_name(lowerCAmelCase__				):
								for patt, repl in iter(lowerCAmelCase__				):
												A__							 =       name.replace(lowerCAmelCase__       ,lowerCAmelCase__				)
								return f'''bert/{name}'''

				def create_tf_var(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
								A__							 =       tf.dtypes.as_dtype(tensor.dtype				)
								A__							 =       tf.get_variable(dtype=lowerCAmelCase__       ,shape=tensor.shape       ,name=lowerCAmelCase__       ,initializer=tf.zeros_initializer()				)
								session.run(tf.variables_initializer([tf_var]				)				)
								session.run(lowerCAmelCase__				)
								return tf_var

				tf.reset_default_graph()
				with tf.Session() as session:
								for var_name in state_dict:
												A__							 =       to_tf_var_name(lowerCAmelCase__				)
												A__							 =       state_dict[var_name].numpy()
												if any(x in var_name for x in tensors_to_transpose				):
																A__							 =       torch_tensor.T
												A__							 =       create_tf_var(tensor=lowerCAmelCase__       ,name=lowerCAmelCase__       ,session=lowerCAmelCase__				)
												tf.keras.backend.set_value(lowerCAmelCase__       ,lowerCAmelCase__				)
												A__							 =       session.run(lowerCAmelCase__				)
												print(f'''Successfully created {tf_name}: {np.allclose(lowerCAmelCase__       ,lowerCAmelCase__				)}'''				)

								A__							 =       tf.train.Saver(tf.trainable_variables()				)
								saver.save(lowerCAmelCase__       ,os.path.join(lowerCAmelCase__       ,model_name.replace('-'       ,'_'				) + '.ckpt'				)				)



def  __lowerCamelCase					(					lowerCAmelCase__=None				):
				A__							 =       argparse.ArgumentParser()
				parser.add_argument('--model_name'       ,type=lowerCAmelCase__       ,required=lowerCAmelCase__       ,help='model name e.g. bert-base-uncased'				)
				parser.add_argument(
				    '--cache_dir'       ,type=lowerCAmelCase__       ,default=lowerCAmelCase__       ,required=lowerCAmelCase__       ,help='Directory containing pytorch model'				)
				parser.add_argument('--pytorch_model_path'       ,type=lowerCAmelCase__       ,required=lowerCAmelCase__       ,help='/path/to/<pytorch-model-name>.bin'				)
				parser.add_argument('--tf_cache_dir'       ,type=lowerCAmelCase__       ,required=lowerCAmelCase__       ,help='Directory in which to save tensorflow model'				)
				A__							 =       parser.parse_args(lowerCAmelCase__				)

				A__							 =       BertModel.from_pretrained(
				    pretrained_model_name_or_path=args.model_name       ,state_dict=torch.load(args.pytorch_model_path				)       ,cache_dir=args.cache_dir       ,)

				convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase__       ,ckpt_dir=args.tf_cache_dir       ,model_name=args.model_name				)


if __name__ == "__main__":
					main()










"""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''')


