




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			)			->							str:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

     lowercase :       Optional[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(__magic_name__			):
          os.makedirs(__magic_name__			)

     lowercase :       Optional[Any]       =			model.state_dict()

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

     def create_tf_var(__magic_name__			,		__magic_name__			,		__magic_name__			):
          lowercase :       int       =			tf.dtypes.as_dtype(tensor.dtype			)
          lowercase :       int       =			tf.get_variable(dtype=__magic_name__			,		shape=tensor.shape			,		name=__magic_name__			,		initializer=tf.zeros_initializer()			)
          session.run(tf.variables_initializer([tf_var]			)			)
          session.run(__magic_name__			)
          return tf_var

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

          lowercase :       str       =			tf.train.Saver(tf.trainable_variables()			)
          saver.save(__magic_name__			,		os.path.join(__magic_name__			,		model_name.replace('''-'''			,		'''_'''			) + '''.ckpt'''			)			)






def  snake_case(    __magic_name__=None			)			->							List[Any]:





     '''simple docstring'''


     lowercase :       str       =			argparse.ArgumentParser()
     parser.add_argument('''--model_name'''			,		type=__magic_name__			,		required=__magic_name__			,		help='''model name e.g. bert-base-uncased'''			)
     parser.add_argument(
         '''--cache_dir'''			,		type=__magic_name__			,		default=__magic_name__			,		required=__magic_name__			,		help='''Directory containing pytorch model'''			)
     parser.add_argument('''--pytorch_model_path'''			,		type=__magic_name__			,		required=__magic_name__			,		help='''/path/to/<pytorch-model-name>.bin'''			)
     parser.add_argument('''--tf_cache_dir'''			,		type=__magic_name__			,		required=__magic_name__			,		help='''Directory in which to save tensorflow model'''			)
     lowercase :       int       =			parser.parse_args(__magic_name__			)

     lowercase :       Union[str, Any]       =			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=__magic_name__			,		ckpt_dir=args.tf_cache_dir			,		model_name=args.model_name			)


if __name__ == "__main__":
 main()





def  snake_case(    __magic_name__			,		__magic_name__   )			->							bool:





     '''simple docstring'''


     lowercase :       List[Any]       =			len(__magic_name__   )
     lowercase :       str       =			[[False] * (required_sum + 1) for _ in range(arr_len + 1   )]

     # for each arr value, a sum of zero(0) can be formed by not taking any element
     # hence True/1
     for i in range(arr_len + 1   ):
          lowercase :       List[str]       =			True

     # sum is not zero and set is empty then false
     for i in range(1			,		required_sum + 1   ):
          lowercase :       str       =			False

     for i in range(1			,		arr_len + 1   ):
          for j in range(1			,		required_sum + 1   ):
               if arr[i - 1] > j:
                    lowercase :       Optional[Any]       =			subset[i - 1][j]
               if arr[i - 1] <= j:
                    lowercase :       Dict       =			subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

     return subset[arr_len][required_sum]


if __name__ == "__main__":
 import doctest

 doctest.testmod()