
def lowerCamelCase_       (			A			:      str		,					A			:      Optional[int]						):




      """simple docstring"""
      sequence.append(__snake_case						)

      lowerCAmelCase_						=      0
      while sequence[index] != target:
            index += 1

      sequence.pop()

      if index == len(__snake_case						):
            return None

      return index


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

 _snake_case		  =   input("Enter a single number to be found in the list:\n")
 _snake_case		  =   int(target_input)
 _snake_case		  =   sentinel_linear_search(sequence, target)
 if result is not None:
  print(f'''{target} found at positions: {result}''')
 else:
  print("Not found")





import argparse

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


logging.set_verbosity_info()
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		List[str]    ,__snake_case				:		List[Any]					)       ->   str:
       __snake_case						       =   ConvBertConfig.from_json_file(__snake_case					)
       __snake_case						       =   ConvBertModel(__snake_case					)

       __snake_case						       =   load_tf_weights_in_convbert(__snake_case    ,__snake_case    ,__snake_case					)
       model.save_pretrained(__snake_case					)

       __snake_case						       =   TFConvBertModel.from_pretrained(__snake_case    ,from_pt=__snake_case					)
       tf_model.save_pretrained(__snake_case					)


if __name__ == "__main__":
 lowerCamelCase_				:							List[Any]       =    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."
 )
 lowerCamelCase_				:							Union[str, Any]       =    parser.parse_args()
 convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
