



import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





       '''simple docstring'''




       _lowerCAmelCase  :							Any				     =					FunnelConfig.from_json_file(_lowerCamelCase						)
       print(F"Building PyTorch model from configuration: {config}"						)
       _lowerCAmelCase  :							List[str]				     =					FunnelBaseModel(_lowerCamelCase						) if base_model else FunnelModel(_lowerCamelCase						)

       # Load weights from tf checkpoint
       load_tf_weights_in_funnel(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)

       # Save pytorch-model
       print(F"Save PyTorch model to {pytorch_dump_path}"						)
       torch.save(model.state_dict()       ,				_lowerCamelCase						)


if __name__ == "__main__":
      _snake_case	=						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(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
      )
      _snake_case	=						parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
      )





def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							if length <= 0 or not isinstance(_lowerCamelCase       ,				_lowerCamelCase						):
														raise ValueError("Length must be a positive integer."						)
							return [n * (2 * n - 1) for n in range(_lowerCamelCase						)]


if __name__ == "__main__":
						print(hexagonal_numbers(length=5))
						print(hexagonal_numbers(length=10))
