


'''simple docstring'''
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel




def 	_A  (       A							,A							,A							)  ->				List[Any]:
	lowercase						:		str   					=		("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(A							):
		os.makedirs(A							)

	lowercase						:		Optional[Any]   					=		model.state_dict()

	def to_tf_var_name(A							):
		for patt, repl in iter(A							):
			lowercase						:		Optional[int]   					=		name.replace(A							,A							)
		return F'''bert/{name}'''

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

	tf.reset_default_graph()
	with tf.Session() as session:
		for var_name in state_dict:
			lowercase						:		List[Any]   					=		to_tf_var_name(A							)
			lowercase						:		Optional[int]   					=		state_dict[var_name].numpy()
			if any(x in var_name for x in tensors_to_transpose							):
				lowercase						:		Optional[int]   					=		torch_tensor.T
			lowercase						:		Optional[int]   					=		create_tf_var(tensor=A							,name=A							,session=A							)
			tf.keras.backend.set_value(A							,A							)
			lowercase						:		Tuple   					=		session.run(A							)
			print(F'''Successfully created {tf_name}: {np.allclose(A							,A							)}'''							)

		lowercase						:		Optional[int]   					=		tf.train.Saver(tf.trainable_variables()							)
		saver.save(A							,os.path.join(A							,model_name.replace("-"							,"_"							) + ".ckpt"							)							)




def 	_A  (       A=None							)  ->				Optional[int]:
	lowercase						:		Optional[int]   					=		argparse.ArgumentParser()
	parser.add_argument("--model_name"							,type=A							,required=A							,help="model name e.g. bert-base-uncased"							)
	parser.add_argument(
	    "--cache_dir"							,type=A							,default=A							,required=A							,help="Directory containing pytorch model"							)
	parser.add_argument("--pytorch_model_path"							,type=A							,required=A							,help="/path/to/<pytorch-model-name>.bin"							)
	parser.add_argument("--tf_cache_dir"							,type=A							,required=A							,help="Directory in which to save tensorflow model"							)
	lowercase						:		Any   					=		parser.parse_args(A							)

	lowercase						:		List[Any]   					=		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=A							,ckpt_dir=args.tf_cache_dir							,model_name=args.model_name							)


if __name__ == "__main__":
					main()






'''simple docstring'''
import baseaa




def 	_A  (       A							)  ->				bytes:
	return baseaa.aaaencode(string.encode("utf-8"							)							)




def 	_A  (       A							)  ->				str:
	return baseaa.aaadecode(A							).decode("utf-8"							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


