




"""simple docstring"""

import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()



def   UpperCamelCase__     (		lowercase__  :     Tuple       ,     lowercase__  :     str       ,     lowercase__  :     Union[str, Any]   ):
		snake_case		:						Tuple								=				ConvBertConfig.from_json_file(lowercase__   )
		snake_case		:						Dict								=				ConvBertModel(lowercase__   )

		snake_case		:						Dict								=				load_tf_weights_in_convbert(lowercase__       ,     lowercase__       ,     lowercase__   )
		model.save_pretrained(lowercase__   )

		snake_case		:						Dict								=				TFConvBertModel.from_pretrained(lowercase__       ,     from_pt=lowercase__   )
		tf_model.save_pretrained(lowercase__   )


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







"""simple docstring"""

import random



def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]   ):

		def is_sorted(lowercase__  :     int   ):
				for i in range(len(lowercase__   ) - 1   ):
						if collection[i] > collection[i + 1]:
								return False
				return True

		while not is_sorted(lowercase__   ):
				random.shuffle(lowercase__   )
		return collection


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

