
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						List[str]:
				UpperCamelCase_:				Optional[Any]						=    ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

				UpperCamelCase_:				Union[str, Any]						=    (
				    ("""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__    )

				UpperCamelCase_:				int						=    model.state_dict()

				def to_tf_var_name(lowerCamelCase    ):
								for patt, repl in iter(lowerCamelCase__    ):
												UpperCamelCase_:				Optional[Any]						=    name.replace(lowerCamelCase__  ,     lowerCamelCase__    )
								return F'''bert/{name}'''

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

								UpperCamelCase_:				Tuple						=    tf.train.Saver(tf.trainable_variables()    )
								saver.save(lowerCamelCase__  ,     os.path.join(lowerCamelCase__  ,     model_name.replace("""-"""  ,     """_"""    ) + """.ckpt"""    )    )







def 							A__       (    lowerCamelCase=None    )	->						List[str]:
				UpperCamelCase_:				str						=    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"""    )
				UpperCamelCase_:				Any						=    parser.parse_args(lowerCamelCase__    )

				UpperCamelCase_:				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()








import re





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]"      ,					str_       )]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     split_input(str_       )
				return "".join(
				    ["".join([char.capitalize() for char in sub_str]       ) for sub_str in string_split]       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     split_input(lowerCamelCase__       )
								if upper:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.upper() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								else:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.lower() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								return res_str
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return to_simple_case(lowerCamelCase__       )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     to_simple_case(lowerCamelCase__       )
								return res_str[0].lower() + res_str[1:]
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"_"       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"-"       )


if __name__ == "__main__":
			__import__('''doctest''').testmod()


