




def 					lowerCamelCase__       (						A__       :					list	):





       '''simple docstring'''



       __lowerCamelCase   =						len(A__	)
       for _ in range(A__	):
              for i in range(_ % 2  ,			arr_size - 1  ,			2	):
                     if arr[i + 1] < arr[i]:
                            __lowerCamelCase,   __lowerCamelCase   =						arr[i + 1], arr[i]

       return arr


if __name__ == "__main__":
       UpperCAmelCase_									=	list(range(10, 0, -1))
       print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")


import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()

def 			lowercase_				(			_lowerCamelCase							:       str		,	_lowerCamelCase							:       List[Any]		,	_lowerCamelCase							:       Dict):
    # Initialise PyTorch model
    lowercase__ :   List[str]    					=				BertConfig.from_json_file(_lowerCamelCase)
    print(f'''Building PyTorch model from configuration: {config}''')
    lowercase__ :   Optional[Any]    					=				BertForPreTraining(_lowerCamelCase)

    # Load weights from tf checkpoint
    load_tf_weights_in_bert(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase)

    # Save pytorch-model
    print(f'''Save PyTorch model to {pytorch_dump_path}''')
    torch.save(model.state_dict()		,	_lowerCamelCase)


if __name__ == "__main__":
      UpperCamelCase  							=						argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--bert_config_file''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained BERT model. \n'''
              '''This specifies the model architecture.'''
          ),
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      UpperCamelCase  							=						parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



