


'''simple docstring'''

class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None		)       ->    List[Any]:
          _a						=		data
          _a						=		previous
          _a						=		next_node






     def __str__(			self		)       ->    str:
          return f"{self.data}"






     def  a_					(			self		)       ->    int:
          return self.data






     def  a_					(			self		)       ->    List[str]:
          return self.next






     def  a_					(			self		)       ->    Any:
          return self.previous







class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase		)       ->    Union[str, Any]:
          _a						=		head






     def __iter__(			self		)       ->    Optional[Any]:
          return self






     def  a_					(			self		)       ->    Optional[int]:
          if not self.current:
               raise StopIteration
          else:
               _a						=		self.current.get_data()
               _a						=		self.current.get_next()
               return value







class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self		)       ->    List[Any]:
          _a						=		None  # First node in list
          _a						=		None  # Last node in list






     def __str__(			self		)       ->    str:
          _a						=		self.head
          _a						=		[]
          while current is not None:
               nodes.append(current.get_data()		)
               _a						=		current.get_next()
          return " ".join(str(__UpperCamelCase		) for node in nodes		)






     def __contains__(			self     ,							__UpperCamelCase		)       ->    Tuple:
          _a						=		self.head
          while current:
               if current.get_data() == value:
                    return True
               _a						=		current.get_next()
          return False






     def __iter__(			self		)       ->    Optional[int]:
          return LinkedListIterator(self.head		)






     def  a_					(			self		)       ->    Optional[Any]:
          if self.head:
               return self.head.get_data()
          return None






     def  a_					(			self		)       ->    int:
          if self.tail:
               return self.tail.get_data()
          return None






     def  a_					(			self     ,							__UpperCamelCase		)       ->    None:
          if self.head is None:
               _a						=		node
               _a						=		node
          else:
               self.insert_before_node(self.head     ,							__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    None:
          if self.head is None:
               self.set_head(__UpperCamelCase		)
          else:
               self.insert_after_node(self.tail     ,							__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    None:
          _a						=		Node(__UpperCamelCase		)
          if self.head is None:
               self.set_head(__UpperCamelCase		)
          else:
               self.set_tail(__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    None:
          _a						=		node
          _a						=		node.previous

          if node.get_previous() is None:
               _a						=		node_to_insert
          else:
               _a						=		node_to_insert

          _a						=		node_to_insert






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    None:
          _a						=		node
          _a						=		node.next

          if node.get_next() is None:
               _a						=		node_to_insert
          else:
               _a						=		node_to_insert

          _a						=		node_to_insert






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    None:
          _a						=		1
          _a						=		Node(__UpperCamelCase		)
          _a						=		self.head
          while node:
               if current_position == position:
                    self.insert_before_node(__UpperCamelCase     ,							__UpperCamelCase		)
                    return
               current_position += 1
               _a						=		node.next
          self.insert_after_node(self.tail     ,							__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Node:
          _a						=		self.head
          while node:
               if node.get_data() == item:
                    return node
               _a						=		node.get_next()
          raise Exception("Node not found"		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Optional[int]:
          if (node := self.get_node(__UpperCamelCase		)) is not None:
               if node == self.head:
                    _a						=		self.head.get_next()

               if node == self.tail:
                    _a						=		self.tail.get_previous()

               self.remove_node_pointers(__UpperCamelCase		)






     @staticmethod
     def  a_					(			__UpperCamelCase		)       ->    None:
          if node.get_next():
               _a						=		node.previous

          if node.get_previous():
               _a						=		node.next

          _a						=		None
          _a						=		None






     def  a_					(			self		)       ->    str:
          return self.head is None







def __UpperCamelCase   (			)      ->     None:







     '''simple docstring'''








if __name__ == "__main__":
   import doctest

   doctest.testmod()





'''simple docstring'''

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def __UpperCamelCase   (			__lowerCamelCase		: BertModel      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str				)      ->     List[str]:







     '''simple docstring'''






     _a						=		("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

     _a						=		(
         ("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						=		model.state_dict()

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

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

          _a						=		tf.train.Saver(tf.trainable_variables()				)
          saver.save(__lowerCamelCase      ,  os.path.join(__lowerCamelCase      ,  model_name.replace("-"      ,  "_"				) + ".ckpt"				)				)






def __UpperCamelCase   (			__lowerCamelCase		: str=None				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		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						=		parser.parse_args(__lowerCamelCase				)

     _a						=		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()

