

'''simple docstring'''



import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 						__lowerCamelCase	(       __snake_case					:		BertModel,		__snake_case					:		str,		__snake_case					:		str	)	->							int:






						"""simple docstring"""





						A__					:       str     =("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

						A__					:       List[str]     =(
						    ("""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(__snake_case	):
												os.makedirs(__snake_case	)

						A__					:       str     =model.state_dict()

						def to_tf_var_name(__snake_case					:		str	):
												for patt, repl in iter(__snake_case	):
																		A__					:       Tuple     =name.replace(__snake_case,		__snake_case	)
												return f"bert/{name}"

						def create_tf_var(__snake_case					:		np.ndarray,		__snake_case					:		str,		__snake_case					:		tf.Session	):
												A__					:       Any     =tf.dtypes.as_dtype(tensor.dtype	)
												A__					:       int     =tf.get_variable(dtype=__snake_case,		shape=tensor.shape,		name=__snake_case,		initializer=tf.zeros_initializer()	)
												session.run(tf.variables_initializer([tf_var]	)	)
												session.run(__snake_case	)
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		A__					:       Optional[Any]     =to_tf_var_name(__snake_case	)
																		A__					:       List[str]     =state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose	):
																								A__					:       Optional[Any]     =torch_tensor.T
																		A__					:       Optional[int]     =create_tf_var(tensor=__snake_case,		name=__snake_case,		session=__snake_case	)
																		tf.keras.backend.set_value(__snake_case,		__snake_case	)
																		A__					:       List[str]     =session.run(__snake_case	)
																		print(f"Successfully created {tf_name}: {np.allclose(__snake_case,		__snake_case	)}"	)

												A__					:       str     =tf.train.Saver(tf.trainable_variables()	)
												saver.save(__snake_case,		os.path.join(__snake_case,		model_name.replace("""-""",		"""_"""	) + """.ckpt"""	)	)
def 						__lowerCamelCase	(       __snake_case					:		int=None	)	->							Any:






						"""simple docstring"""





						A__					:       Tuple     =argparse.ArgumentParser()
						parser.add_argument("""--model_name""",		type=__snake_case,		required=__snake_case,		help="""model name e.g. bert-base-uncased"""	)
						parser.add_argument(
						    """--cache_dir""",		type=__snake_case,		default=__snake_case,		required=__snake_case,		help="""Directory containing pytorch model"""	)
						parser.add_argument("""--pytorch_model_path""",		type=__snake_case,		required=__snake_case,		help="""/path/to/<pytorch-model-name>.bin"""	)
						parser.add_argument("""--tf_cache_dir""",		type=__snake_case,		required=__snake_case,		help="""Directory in which to save tensorflow model"""	)
						A__					:       List[Any]     =parser.parse_args(__snake_case	)

						A__					:       Tuple     =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=__snake_case,		ckpt_dir=args.tf_cache_dir,		model_name=args.model_name	)


if __name__ == "__main__":
		main()




'''simple docstring'''



from __future__ import annotations
def 						__lowerCamelCase	(       __snake_case					:		list[int]	)	->							bool:






						"""simple docstring"""





						return len(set(__snake_case	)	) == len(__snake_case	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

