


"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       list,  _lowerCAmelCase  :       int,  _lowerCAmelCase  :       int							):






     """simple docstring"""

     if right <= left:
          return
     _a     						=					_a     						=					left
     _a     						=					right
     _a     						=					sorting[left]
     while i <= b:
          if sorting[i] < pivot:
               _a						,   _a     						=					sorting[i], sorting[a]
               a += 1
               i += 1
          elif sorting[i] > pivot:
               _a						,   _a     						=					sorting[i], sorting[b]
               b -= 1
          else:
               i += 1
     quick_sort_apartition(_lowerCAmelCase,  _lowerCAmelCase,  a - 1							)
     quick_sort_apartition(_lowerCAmelCase,  b + 1,  _lowerCAmelCase							)
def    A_ ( _lowerCAmelCase  :       list,  _lowerCAmelCase  :       int,  _lowerCAmelCase  :       int							):






     """simple docstring"""

     if left < right:
          _a     						=					lomuto_partition(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)
          quick_sort_lomuto_partition(_lowerCAmelCase,  _lowerCAmelCase,  pivot_index - 1							)
          quick_sort_lomuto_partition(_lowerCAmelCase,  pivot_index + 1,  _lowerCAmelCase							)
def    A_ ( _lowerCAmelCase  :       list,  _lowerCAmelCase  :       int,  _lowerCAmelCase  :       int							):






     """simple docstring"""

     _a     						=					sorting[right]
     _a     						=					left
     for i in range(_lowerCAmelCase,  _lowerCAmelCase							):
          if sorting[i] < pivot:
               _a						,   _a     						=					sorting[i], sorting[store_index]
               store_index += 1
     _a						,   _a     						=					sorting[store_index], sorting[right]
     return store_index
def    A_ ( _lowerCAmelCase  :       list							):






     """simple docstring"""

     if len(_lowerCAmelCase							) <= 1:
          return sorting
     return (
         three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
         + [i for i in sorting if i == sorting[0]]
         + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
     )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

   __snake_case												=							input('''Enter numbers separated by a comma:\n''').strip()
   __snake_case												=							[int(item) for item in user_input.split(''',''')]
   quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
   print(unsorted)



"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    A_ ( _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					RoFormerConfig.from_json_file(_lowerCAmelCase							)
     print(f'Building PyTorch model from configuration: {config}'							)
     _a     						=					RoFormerForMaskedLM(_lowerCAmelCase							)

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)

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


if __name__ == "__main__":
   __snake_case												=							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.'''
   )
   __snake_case												=							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)