





'''simple docstring'''


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel







def    _lowerCamelCase  (__lowerCamelCase  :     BertModel					,  __lowerCamelCase  :     str					,  __lowerCamelCase  :     str     )   ->    List[Any]:
			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  :     str     ):
						for patt, repl in iter(__lowerCamelCase     ):
									a__     = name.replace(__lowerCamelCase					,  __lowerCamelCase     )
						return f'''bert/{name}'''

			def create_tf_var(__lowerCamelCase  :     np.ndarray					,  __lowerCamelCase  :     str					,  __lowerCamelCase  :     tf.Session     ):
						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  :     List[Any]=None     )   ->    Optional[int]:
			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'''


import os
import time

import numpy as np
import onnxruntime as ort


lowerCAmelCase_   :   Optional[int]       =					"1"
lowerCAmelCase_   :   List[str]       =					"0"
lowerCAmelCase_   :   Tuple       =					"1"

lowerCAmelCase_   :   str       =					ort.SessionOptions()
lowerCAmelCase_   :   Tuple       =					ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
lowerCAmelCase_   :   Any       =					["TensorrtExecutionProvider", "CUDAExecutionProvider"]
lowerCAmelCase_   :   str       =					ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
lowerCAmelCase_   :   Tuple       =					ort.RunOptions()

lowerCAmelCase_   :   Optional[int]       =					128
lowerCAmelCase_   :   Union[str, Any]       =					1
lowerCAmelCase_   :   List[str]       =					np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_   :   Optional[Any]       =					np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_   :   str       =					np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
lowerCAmelCase_   :   Tuple       =					time.time()
lowerCAmelCase_   :   int       =					2000
lowerCAmelCase_   :   Optional[Any]       =					{}
for iter in range(max_iters):
		lowerCAmelCase_   :   List[str]       =					sess.run(
		    None,
		    {
		        sess.get_inputs()[0].name: input_ids,
		        sess.get_inputs()[1].name: attention_mask,
		        sess.get_inputs()[2].name: token_type_ids,
		    },
		    run_options=run_opt,
		)
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))



