




"""simple docstring"""

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def 						_snake_case					( _snake_case							: BertModel    ,  _snake_case							: str    ,  _snake_case							: str							)   -> Dict:






      '''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(_snake_case							):
            os.makedirs(_snake_case							)

      _A        =       model.state_dict()

      def to_tf_var_name(_snake_case							: str							):
            for patt, repl in iter(_snake_case							):
                  _A        =       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							):
            _A        =       tf.dtypes.as_dtype(tensor.dtype							)
            _A        =       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:
                  _A        =       to_tf_var_name(_snake_case							)
                  _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=_snake_case    ,  name=_snake_case    ,  session=_snake_case							)
                  tf.keras.backend.set_value(_snake_case    ,  _snake_case							)
                  _A        =       session.run(_snake_case							)
                  print(F'''Successfully created {tf_name}: {np.allclose(_snake_case    ,  _snake_case							)}'''							)

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






def 						_snake_case					( _snake_case							: int=None							)   -> List[str]:






      '''simple docstring'''


      _A        =       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'							)
      _A        =       parser.parse_args(_snake_case							)

      _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=_snake_case    ,  ckpt_dir=args.tf_cache_dir    ,  model_name=args.model_name							)


if __name__ == "__main__":
       main()







"""simple docstring"""

def 						_snake_case					( _snake_case							: int							)   -> list:






      '''simple docstring'''


      _A        =       int(_snake_case							)
      if n_element < 1:
            _A        =       ValueError('a should be a positive number'							)
            raise my_error

      _A        =       [1]
      _A       ,   _A       ,   _A        =       (0, 0, 0)
      _A        =       1
      while index < n_element:
            while hamming_list[i] * 2 <= hamming_list[-1]:
                  i += 1
            while hamming_list[j] * 3 <= hamming_list[-1]:
                  j += 1
            while hamming_list[k] * 5 <= hamming_list[-1]:
                  k += 1
            hamming_list.append(
                min(hamming_list[i] * 2    ,  hamming_list[j] * 3    ,  hamming_list[k] * 5							)							)
            index += 1
      return hamming_list


if __name__ == "__main__":
       a         =      input('''Enter the last number (nth term) of the Hamming Number Series: ''')
       print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
       a         =      hamming(int(n))
       print('''-----------------------------------------------------''')
       print(F'''The list with nth numbers is: {hamming_numbers}''')
       print('''-----------------------------------------------------''')

