





import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()





def 						lowerCamelCase__     (     UpperCamelCase__			:       Dict       ,    UpperCamelCase__			:       Any       ,    UpperCamelCase__			:       Union[str, Any]						)			-> Optional[Any]:



					'''simple docstring'''




					_snake_case									=				RemBertConfig.from_json_file(UpperCamelCase__						)
					print('Building PyTorch model from configuration: {}'.format(str(UpperCamelCase__						)						)						)
					_snake_case									=				RemBertModel(UpperCamelCase__						)

					# Load weights from tf checkpoint
					load_tf_weights_in_rembert(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__						)

					# Save pytorch-model
					print('Save PyTorch model to {}'.format(UpperCamelCase__						)						)
					torch.save(model.state_dict()       ,    UpperCamelCase__						)


if __name__ == "__main__":
					UpperCAmelCase_      							=     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(
					    """--rembert_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained RemBERT 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."""
					)
					UpperCAmelCase_      							=     parser.parse_args()
					convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)










def 						lowerCamelCase__     (     UpperCamelCase__			:       list						)			-> list:



					'''simple docstring'''




					_snake_case									=				len(UpperCamelCase__						)
					for i in range(1       ,    UpperCamelCase__						):
										_snake_case									=				collection[i]
										_snake_case									=				0
										_snake_case									=				i - 1

										while low <= high:
															_snake_case									=				(low + high) // 2
															if val < collection[mid]:
																				_snake_case									=				mid - 1
															else:
																				_snake_case									=				mid + 1
										for j in range(UpperCamelCase__       ,    UpperCamelCase__       ,    -1						):
															_snake_case									=				collection[j - 1]
										_snake_case									=				val
					return collection


if __name__ == "__main__":
					UpperCAmelCase_      							=     input("""Enter numbers separated by a comma:\n""").strip()
					UpperCAmelCase_      							=     [int(item) for item in user_input.split(""",""")]
					print(binary_insertion_sort(unsorted))



