






'''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  lowerCamelCase_  (     lowercase__					,  lowercase__					,  lowercase__):
 lowerCamelCase__	=  ConvBertConfig.from_json_file(lowercase__)
 lowerCamelCase__	=  ConvBertModel(lowercase__)

 lowerCamelCase__	=  load_tf_weights_in_convbert(lowercase__					,  lowercase__					,  lowercase__)
 model.save_pretrained(lowercase__)

 lowerCamelCase__	=  TFConvBertModel.from_pretrained(lowercase__					,  from_pt=lowercase__)
 tf_model.save_pretrained(lowercase__)


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(
     """--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				:				Optional[Any]					=		parser.parse_args()
 convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)









'''simple docstring'''





def  lowerCamelCase_  (     lowercase__					,  lowercase__):
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  (
     first_input_list_length
     if first_input_list_length > second_input_list_length
     else second_input_list_length
 )
 lowerCamelCase__	=  []
 for char_count in range(lowercase__):
  if char_count < first_input_list_length:
   output_result_list.append(first_input_list[char_count])
  if char_count < second_input_list_length:
   output_result_list.append(second_input_list[char_count])

 return output_result_list


if __name__ == "__main__":
 print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)

