


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		List[Any]:
    _lowercase :							Optional[Any]   	=	('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

    _lowercase :							int   	=	(
        ('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_				)

    _lowercase :							Dict   	=	model.state_dict()

    def to_tf_var_name(lowerCamelCase_				):
        for patt, repl in iter(lowerCamelCase_				):
            _lowercase :							List[Any]   	=	name.replace(lowerCamelCase_							,				lowerCamelCase_				)
        return F'''bert/{name}'''

    def create_tf_var(lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				):
        _lowercase :							Optional[Any]   	=	tf.dtypes.as_dtype(tensor.dtype				)
        _lowercase :							Optional[Any]   	=	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:
            _lowercase :							Any   	=	to_tf_var_name(lowerCamelCase_				)
            _lowercase :							List[Any]   	=	state_dict[var_name].numpy()
            if any(x in var_name for x in tensors_to_transpose				):
                _lowercase :							Union[str, Any]   	=	torch_tensor.T
            _lowercase :							Dict   	=	create_tf_var(tensor=lowerCamelCase_							,				name=lowerCamelCase_							,				session=lowerCamelCase_				)
            tf.keras.backend.set_value(lowerCamelCase_							,				lowerCamelCase_				)
            _lowercase :							List[Any]   	=	session.run(lowerCamelCase_				)
            print(F'''Successfully created {tf_name}: {np.allclose(lowerCamelCase_							,				lowerCamelCase_				)}'''				)

        _lowercase :							List[Any]   	=	tf.train.Saver(tf.trainable_variables()				)
        saver.save(lowerCamelCase_							,				os.path.join(lowerCamelCase_							,				model_name.replace('-'							,				'_'				) + '.ckpt'				)				)



def        UpperCamelCase_(   lowerCamelCase_=None				)					->		Union[str, Any]:
    _lowercase :							int   	=	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'				)
    _lowercase :							Optional[Any]   	=	parser.parse_args(lowerCamelCase_				)

    _lowercase :							List[str]   	=	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()






def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		bool:
    return numa ^ numa < 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()


