code: 
  
  
  
  
  """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)
  
  
style_context: 
  
  
  
  
  """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))
  
  