
'''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       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Dict   ,   __SCREAMING_SNAKE_CASE      :				Dict   ,   __SCREAMING_SNAKE_CASE      :				List[str]							):



						"""simple docstring"""





						lowercase_			:  Optional[int]									= ConvBertConfig.from_json_file(__SCREAMING_SNAKE_CASE							)
						lowercase_			:  Tuple									= ConvBertModel(__SCREAMING_SNAKE_CASE							)

						lowercase_			:  str									= load_tf_weights_in_convbert(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)
						model.save_pretrained(__SCREAMING_SNAKE_CASE							)

						lowercase_			:  Tuple									= TFConvBertModel.from_pretrained(__SCREAMING_SNAKE_CASE   ,   from_pt=__SCREAMING_SNAKE_CASE							)
						tf_model.save_pretrained(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							_lowercase				:						Dict												= 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."
							)
							_lowercase				:						str												= parser.parse_args()
							convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)





'''simple docstring'''







def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int = 1000							):



						"""simple docstring"""





						lowercase_			:  List[Any]									= 0
						for i in range(__SCREAMING_SNAKE_CASE							):
												if i % 3 == 0 or i % 5 == 0:
																		result += i
						return result


if __name__ == "__main__":
							print(f"""{solution() = }""")



