import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def A__  (			lowercase:     Any,		lowercase:     Optional[Any],		lowercase:     str					)				->		Union[str, Any]:
       A						:     Any  =('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

       A						:     Dict  =(
           ('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(lowercase					):
              os.makedirs(lowercase					)

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

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

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

       tf.reset_default_graph()
       with tf.Session() as session:
              for var_name in state_dict:
                     A						:     Union[str, Any]  =to_tf_var_name(lowercase					)
                     A						:     List[Any]  =state_dict[var_name].numpy()
                     if any(x in var_name for x in tensors_to_transpose					):
                            A						:     Union[str, Any]  =torch_tensor.T
                     A						:     str  =create_tf_var(tensor=lowercase,		name=lowercase,		session=lowercase					)
                     tf.keras.backend.set_value(lowercase,		lowercase					)
                     A						:     Optional[Any]  =session.run(lowercase					)
                     print(F'Successfully created {tf_name}: {np.allclose(lowercase,		lowercase					)}'					)

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



def A__  (			lowercase:     Dict=None					)				->		Tuple:
       A						:     int  =argparse.ArgumentParser()
       parser.add_argument('--model_name',		type=lowercase,		required=lowercase,		help='model name e.g. bert-base-uncased'					)
       parser.add_argument(
           '--cache_dir',		type=lowercase,		default=lowercase,		required=lowercase,		help='Directory containing pytorch model'					)
       parser.add_argument('--pytorch_model_path',		type=lowercase,		required=lowercase,		help='/path/to/<pytorch-model-name>.bin'					)
       parser.add_argument('--tf_cache_dir',		type=lowercase,		required=lowercase,		help='Directory in which to save tensorflow model'					)
       A						:     Any  =parser.parse_args(lowercase					)

       A						:     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=lowercase,		ckpt_dir=args.tf_cache_dir,		model_name=args.model_name					)


if __name__ == "__main__":
    main()







from typing import List

from .keymap import KEYMAP, get_character



def A__  (			lowercase:     str					)				->		List[str]:

       def decorator(lowercase:     int					):
              A						:     Tuple         =getattr(lowercase,		'handle_key',		[]					)
              handle += [key]
              setattr(lowercase,		'handle_key',		lowercase					)
              return func

       return decorator



def A__  (			*lowercase:     List[str]					)				->		Dict:

       def decorator(lowercase:     Union[str, Any]					):
              A						:     Optional[int]         =getattr(lowercase,		'handle_key',		[]					)
              handle += keys
              setattr(lowercase,		'handle_key',		lowercase					)
              return func

       return decorator



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  def __new__(				cls    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				Any:
         A						:     Dict         =super().__new__(cls      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         if not hasattr(SCREAMING_SNAKE_CASE__      ,	'key_handler'     ):
                setattr(SCREAMING_SNAKE_CASE__      ,	'key_handler'      ,	{}     )
         setattr(SCREAMING_SNAKE_CASE__      ,	'handle_input'      ,	KeyHandler.handle_input     )

         for value in attrs.values():
                A						:     Optional[Any]         =getattr(SCREAMING_SNAKE_CASE__      ,	'handle_key'      ,	[]     )
                for key in handled_keys:
                       A						:     str         =value
         return new_cls







  @staticmethod
  def 		SCREAMING_SNAKE_CASE_		(				cls    :					str     )    ->				Any:
         A						:     str         =get_character()
         if char != KEYMAP["undefined"]:
                A						:     List[str]         =ord(SCREAMING_SNAKE_CASE__     )
         A						:     List[str]         =cls.key_handler.get(SCREAMING_SNAKE_CASE__     )
         if handler:
                A						:     List[str]         =char
                return handler(cls     )
         else:
                return None







def A__  (			cls:     Optional[int]					)				->		str:
       return KeyHandler(cls.__name__,		cls.__bases__,		cls.__dict__.copy()					)






