






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def 	UpperCamelCase	(							snake_case__  : BertModel						,snake_case__  : str						,snake_case__  : str	):




     '''simple docstring'''






     __snake_case      :Any 							=    ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

     __snake_case      :Optional[int] 							=    (
         ("""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__	)

     __snake_case      :Union[str, Any] 							=    model.state_dict()

     def to_tf_var_name(snake_case__  : str	):
          for patt, repl in iter(snake_case__	):
               __snake_case      :List[str] 							=    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	):
          __snake_case      :Any 							=    tf.dtypes.as_dtype(tensor.dtype	)
          __snake_case      :Tuple 							=    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:
               __snake_case      :str 							=    to_tf_var_name(snake_case__	)
               __snake_case      :Union[str, Any] 							=    state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose	):
                    __snake_case      :Any 							=    torch_tensor.T
               __snake_case      :Dict 							=    create_tf_var(tensor=snake_case__						,name=snake_case__						,session=snake_case__	)
               tf.keras.backend.set_value(snake_case__						,snake_case__	)
               __snake_case      :List[Any] 							=    session.run(snake_case__	)
               print(f'''Successfully created {tf_name}: {np.allclose(snake_case__						,snake_case__	)}'''	)

          __snake_case      :str 							=    tf.train.Saver(tf.trainable_variables()	)
          saver.save(snake_case__						,os.path.join(snake_case__						,model_name.replace("""-"""						,"""_"""	) + """.ckpt"""	)	)






def 	UpperCamelCase	(							snake_case__  : List[str]=None	):




     '''simple docstring'''






     __snake_case      :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"""	)
     __snake_case      :Optional[int] 							=    parser.parse_args(snake_case__	)

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


if __name__ == "__main__":
     main()










import unittest

import numpy as np






def 	UpperCamelCase	(							snake_case__  : np.ndarray						,snake_case__  : np.ndarray						,snake_case__  : np.ndarray						,snake_case__  : np.ndarray | None = None						,):




     '''simple docstring'''






     __snake_case      :Any 							=    np.shape(snake_case__	)
     __snake_case      :List[str] 							=    np.shape(snake_case__	)
     __snake_case      :List[str] 							=    np.shape(snake_case__	)

     if shape_a[0] != shape_b[0]:
          __snake_case      :int 							=    (
              """Expected the same number of rows for A and B. """
              f'''Instead found A of size {shape_a} and B of size {shape_b}'''
          )
          raise ValueError(snake_case__	)

     if shape_b[1] != shape_c[1]:
          __snake_case      :List[str] 							=    (
              """Expected the same number of columns for B and C. """
              f'''Instead found B of size {shape_b} and C of size {shape_c}'''
          )
          raise ValueError(snake_case__	)

     __snake_case      :Any 							=    pseudo_inv
     if a_inv is None:
          try:
               __snake_case      :Optional[Any] 							=    np.linalg.inv(snake_case__	)
          except np.linalg.LinAlgError:
               raise ValueError(
                   """Input matrix A is not invertible. Cannot compute Schur complement."""	)

     return mat_c - mat_b.T @ a_inv @ mat_b




class       snake_case__ (				unittest.TestCase):

       '''simple docstring'''



       def        __lowercase    (				self     )			->   None:






            '''simple docstring'''





            __snake_case      :List[str] 							=    np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]     )
            __snake_case      :Union[str, Any] 							=    np.array([[0, 3], [3, 0], [2, 3]]     )
            __snake_case      :List[Any] 							=    np.array([[2, 1], [6, 3]]     )

            __snake_case      :int 							=    schur_complement(a__      ,    a__      ,    a__     )

            __snake_case      :Union[str, Any] 							=    np.block([[a, b], [b.T, c]]     )

            __snake_case      :Dict 							=    np.linalg.det(a__     )
            __snake_case      :Tuple 							=    np.linalg.det(a__     )
            __snake_case      :List[Any] 							=    np.linalg.det(a__     )

            self.assertAlmostEqual(a__      ,    det_a * det_s     )



       def        __lowercase    (				self     )			->   None:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]     )
            __snake_case      :Union[str, Any] 							=    np.array([[0, 3], [3, 0], [2, 3]]     )
            __snake_case      :Optional[int] 							=    np.array([[2, 1], [6, 3]]     )

            with self.assertRaises(a__     ):
                 schur_complement(a__      ,    a__      ,    a__     )



       def        __lowercase    (				self     )			->   None:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]     )
            __snake_case      :List[str] 							=    np.array([[0, 3], [3, 0], [2, 3]]     )
            __snake_case      :List[Any] 							=    np.array([[2, 1, 3], [6, 3, 5]]     )

            with self.assertRaises(a__     ):
                 schur_complement(a__      ,    a__      ,    a__     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     unittest.main()


