



"""simple docstring"""






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Optional[int]:
   __lowerCAmelCase   :						str				= ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

   __lowerCAmelCase   :						List[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(__snake_case			):
      os.makedirs(__snake_case			)

   __lowerCAmelCase   :						int				= model.state_dict()

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

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

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


def     _lowercase       (						__snake_case=None			)		->     Union[str, Any]:
   __lowerCAmelCase   :						str				= 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"			)
   __lowerCAmelCase   :						Dict				= parser.parse_args(__snake_case			)

   __lowerCAmelCase   :						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=__snake_case      ,ckpt_dir=args.tf_cache_dir      ,model_name=args.model_name			)


if __name__ == "__main__":
 main()




"""simple docstring"""






from ..utils import DummyObject, requires_backends



class     A__     (						metaclass=__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			['torch', 'scipy']

	def __init__(  self:	Tuple ,    *_SCREAMING_SNAKE_CASE:	Dict ,    **_SCREAMING_SNAKE_CASE:	str)       -> Union[str, Any]:

				"""simple docstring"""

				requires_backends(self ,    ["torch", "scipy"])

	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	Optional[Any] ,    *_SCREAMING_SNAKE_CASE:	Tuple ,    **_SCREAMING_SNAKE_CASE:	Optional[Any])       -> Optional[Any]:

				"""simple docstring"""

				requires_backends(cls ,    ["torch", "scipy"])







	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	int ,    *_SCREAMING_SNAKE_CASE:	List[Any] ,    **_SCREAMING_SNAKE_CASE:	Dict)       -> Union[str, Any]:

				"""simple docstring"""

				requires_backends(cls ,    ["torch", "scipy"])