




"""simple docstring"""

from __future__ import annotations




def    snake_case__						(							lowerCAmelCase :		List[str]		):







					if not list_of_ints:
										return []

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

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




def    snake_case__						(							lowerCAmelCase :		Union[str, Any]					,       lowerCAmelCase :		Union[str, Any]		):







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

					__snake_case        =						[]
					__snake_case        =						[]
					# 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
					__snake_case        =						_msd_radix_sort(__UpperCamelCase					,       bit_position - 1		)
					__snake_case        =						_msd_radix_sort(__UpperCamelCase					,       bit_position - 1		)

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

					return res




def    snake_case__						(							lowerCAmelCase :		List[str]		):
					__snake_case        =						len(__UpperCamelCase		)
					if not list_of_ints or length == 1:
										return

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

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




def    snake_case__						(							lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Tuple					,       lowerCAmelCase :		int					,       lowerCAmelCase :		str		):







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

					bit_position -= 1

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

										if changed:
															continue

										__snake_case   ,   __snake_case        =						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)




