






"""simple docstring"""


from __future__ import annotations



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  if not list_of_ints:
    return []

  if min(__UpperCamelCase					) < 0:
    raise ValueError('''All numbers must be positive'''					)

  __A		     =					max(len(bin(__UpperCamelCase					)[2:]					) for x in list_of_ints					)
  return _msd_radix_sort(__UpperCamelCase						,			__UpperCamelCase					)



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  if bit_position == 0 or len(__UpperCamelCase					) in [0, 1]:
    return list_of_ints

  __A		     =					[]
  __A		     =					[]
  # Split numbers based on bit at bit_position from the right
  for number in list_of_ints:
    if (number >> (bit_position - 1)) & 1:
      # number has a one at bit bit_position
      ones.append(__UpperCamelCase					)
    else:
      # number has a zero at bit bit_position
      zeros.append(__UpperCamelCase					)

    # recursively split both lists further
  __A		     =					_msd_radix_sort(__UpperCamelCase						,			bit_position - 1					)
  __A		     =					_msd_radix_sort(__UpperCamelCase						,			bit_position - 1					)

  # recombine lists
  __A		     =					zeros
  res.extend(__UpperCamelCase					)

  return res



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A		     =					len(__UpperCamelCase					)
  if not list_of_ints or length == 1:
    return

  if min(__UpperCamelCase					) < 0:
    raise ValueError('''All numbers must be positive'''					)

  __A		     =					max(len(bin(__UpperCamelCase					)[2:]					) for x in list_of_ints					)
  _msd_radix_sort_inplace(__UpperCamelCase						,			__UpperCamelCase						,			0						,			__UpperCamelCase					)



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  if bit_position == 0 or end_index - begin_index <= 1:
    return

  bit_position -= 1

  __A		     =					begin_index
  __A		     =					end_index - 1
  while i <= j:
    __A		     =					False
    if not (list_of_ints[i] >> bit_position) & 1:
      # found zero at the beginning
      i += 1
      __A		     =					True
    if (list_of_ints[j] >> bit_position) & 1:
      # found one at the end
      j -= 1
      __A		     =					True

    if changed:
      continue

    __A     ,       __A		     =					list_of_ints[j], list_of_ints[i]
    j -= 1
    if j != i:
      i += 1

  _msd_radix_sort_inplace(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)
  _msd_radix_sort_inplace(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()












"""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    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







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

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

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


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




