



'''simple docstring'''

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> List[Any]:
		_a	: Tuple								=				('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

		_a	: Tuple								=				(
		    ('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	: Any								=				model.state_dict()

		def to_tf_var_name(lowerCAmelCase_    ):
				for patt, repl in iter(lowerCAmelCase_    ):
						_a	: List[str]								=				name.replace(lowerCAmelCase_		,     lowerCAmelCase_    )
				return f"""bert/{name}"""

		def create_tf_var(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    ):
				_a	: Dict								=				tf.dtypes.as_dtype(tensor.dtype    )
				_a	: str								=				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	: Union[str, Any]								=				to_tf_var_name(lowerCAmelCase_    )
						_a	: List[str]								=				state_dict[var_name].numpy()
						if any(x in var_name for x in tensors_to_transpose    ):
								_a	: int								=				torch_tensor.T
						_a	: Any								=				create_tf_var(tensor=lowerCAmelCase_		,     name=lowerCAmelCase_		,     session=lowerCAmelCase_    )
						tf.keras.backend.set_value(lowerCAmelCase_		,     lowerCAmelCase_    )
						_a	: Dict								=				session.run(lowerCAmelCase_    )
						print(f"""Successfully created {tf_name}: {np.allclose(lowerCAmelCase_		,     lowerCAmelCase_    )}"""    )

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



def 	__lowerCamelCase	( lowerCAmelCase_=None    )     -> List[str]:
		_a	: Optional[Any]								=				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	: Optional[int]								=				parser.parse_args(lowerCAmelCase_    )

		_a	: Dict								=				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'''

from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




