
"""simple docstring"""




def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      1.3
					SCREAMING_SNAKE_CASE           =      len(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      False

					while not completed:
										# Update the gap value for a next comb
										SCREAMING_SNAKE_CASE           =      int(gap / shrink_factor     )
										if gap <= 1:
															SCREAMING_SNAKE_CASE           =      True

										SCREAMING_SNAKE_CASE           =      0
										while index + gap < len(SCREAMING_SNAKE_CASE_     ):
															if data[index] > data[index + gap]:
																				# Swap values
																				SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      data[index + gap], data[index]
																				SCREAMING_SNAKE_CASE           =      False
															index += 1

					return data


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	snake_case					  =						input('Enter numbers separated by a comma:\n').strip()
	snake_case					  =						[int(item) for item in user_input.split(',')]
	print(comb_sort(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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      # Initialise PyTorch model
      __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__":
 a__		: Tuple  							=							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."
 )
 a__		: Union[str, Any]  							=							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






