





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def   a_   (			lowerCAmelCase_				:      BertModel,     lowerCAmelCase_				:      str,     lowerCAmelCase_				:      str     ):
   __lowerCAmelCase							     =     ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

   __lowerCAmelCase							     =     (
       ('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_     )

   __lowerCAmelCase							     =     model.state_dict()

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

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

      __lowerCAmelCase							     =     tf.train.Saver(tf.trainable_variables()     )
      saver.save(lowerCAmelCase_,     os.path.join(lowerCAmelCase_,     model_name.replace('-',     '_'     ) + '.ckpt'     )     )

def   a_   (			lowerCAmelCase_				:      Optional[int]=None     ):
   __lowerCAmelCase							     =     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'     )
   __lowerCAmelCase							     =     parser.parse_args(lowerCAmelCase_     )

   __lowerCAmelCase							     =     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()








from math import factorial



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		str					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     real
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     [1] * rank
     else:
        __lowerCAmelCase							     =     rank






  def __repr__(    self			:		Optional[Any]					)							->						Tuple:
     return (
         f"""{self.real}+"""
         f"""{"+".join(str(lowerCAmelCase_					)+"E"+str(n+1					)for n,dual in enumerate(self.duals					)					)}"""
     )






  def     lowercase		(    self			:		str					)							->						Dict:
     __lowerCAmelCase							     =     self.duals.copy()
     while cur[-1] == 0:
        cur.pop(-1					)
     return Dual(self.real ,  lowerCAmelCase_					)






  def __add__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Tuple					)							->						Optional[Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        return Dual(self.real + other ,  self.duals					)
     __lowerCAmelCase							     =     self.duals.copy()
     __lowerCAmelCase							     =     other.duals.copy()
     if len(lowerCAmelCase_					) > len(lowerCAmelCase_					):
        o_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     elif len(lowerCAmelCase_					) < len(lowerCAmelCase_					):
        s_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     __lowerCAmelCase							     =     []
     for i in range(len(lowerCAmelCase_					)					):
        new_duals.append(s_dual[i] + o_dual[i]					)
     return Dual(self.real + other.real ,  lowerCAmelCase_					)

  a_      =			__add__






  def __sub__(    self			:		int ,  lowerCAmelCase_			:		Dict					)							->						Optional[Any]:
     return self + other * -1






  def __mul__(    self			:		int ,  lowerCAmelCase_			:		Optional[int]					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i * other					)
        return Dual(self.real * other ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     [0] * (len(self.duals					) + len(other.duals					) + 1)
     for i, item in enumerate(self.duals					):
        for j, jtem in enumerate(other.duals					):
           new_duals[i + j + 1] += item * jtem
     for k in range(len(self.duals					)					):
        new_duals[k] += self.duals[k] * other.real
     for index in range(len(other.duals					)					):
        new_duals[index] += other.duals[index] * self.real
     return Dual(self.real * other.real ,  lowerCAmelCase_					)

  a_      =			__mul__






  def __truediv__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		str					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i / other					)
        return Dual(self.real / other ,  lowerCAmelCase_					)
     raise ValueError






  def __floordiv__(    self			:		str ,  lowerCAmelCase_			:		List[Any]					)							->						Union[str, Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i // other					)
        return Dual(self.real // other ,  lowerCAmelCase_					)
     raise ValueError






  def __pow__(    self			:		Tuple ,  lowerCAmelCase_			:		Dict					)							->						List[str]:
     if n < 0 or isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        raise ValueError('power must be a positive integer'					)
     if n == 0:
        return 1
     if n == 1:
        return self
     __lowerCAmelCase							     =     self
     for _ in range(n - 1					):
        x *= self
     return x





def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   if not callable(lowerCAmelCase_     ):
      raise ValueError('differentiate() requires a function as input for func'     )
   if not isinstance(lowerCAmelCase_,     (float, int)     ):
      raise ValueError('differentiate() requires a float as input for position'     )
   if not isinstance(lowerCAmelCase_,     lowerCAmelCase_     ):
      raise ValueError('differentiate() requires an int as input for order'     )
   __lowerCAmelCase							     =     Dual(lowerCAmelCase_,     1     )
   __lowerCAmelCase							     =     func(lowerCAmelCase_     )
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(lowerCAmelCase_     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     def   a_   (			lowerCAmelCase_				:      int     ):
        return y**2 * y**4




     print(differentiate(f, 9, 2))

