



from __future__ import annotations





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					if not list_of_ints:
										return []

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

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





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






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

					lowercase						   =       []
					lowercase						   =       []
					# 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
					lowercase						   =       _msd_radix_sort(_UpperCAmelCase       ,			bit_position - 1				)
					lowercase						   =       _msd_radix_sort(_UpperCAmelCase       ,			bit_position - 1				)

					# recombine lists
					lowercase						   =       zeros
					res.extend(_UpperCAmelCase				)

					return res





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       len(_UpperCAmelCase				)
					if not list_of_ints or length == 1:
										return

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

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





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






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

					bit_position -= 1

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

										if changed:
															continue

										lowercase   ,      lowercase						   =       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()











import argparse

import torch

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


logging.set_verbosity_info()





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       RoFormerConfig.from_json_file(_UpperCAmelCase				)
					print(f"""Building PyTorch model from configuration: {config}"""				)
					lowercase						   =       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__":
		__magic_name__       =     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.'''
		)
		__magic_name__       =     parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






