

'''simple docstring'''






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	int  ,  SCREAMING_SNAKE_CASE_     :	Dict  ,  SCREAMING_SNAKE_CASE_     :	Any    )							->   Any:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       TaConfig.from_json_file(SCREAMING_SNAKE_CASE_    )
      print(F"Building PyTorch model from configuration: {config}"    )
      SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       TaForConditionalGeneration(SCREAMING_SNAKE_CASE_    )

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )

      # Save pytorch-model
      print(F"Save PyTorch model to {pytorch_dump_path}"    )
      model.save_pretrained(SCREAMING_SNAKE_CASE_    )


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 T5 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.'
      )
      snake_case_       =       parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





'''simple docstring'''







from __future__ import annotations






def       lowerCAmelCase__       (				lowerCamelCase     :			list   ):
       _A     :	Union[str, Any]      =					sorted(lowerCamelCase   )
       _A     :	Tuple      =					len(lowerCamelCase   )
       _A     :	int      =					length >> 1
       return (
           (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
           if length % 2 == 0
           else sorted_list[mid_index]
       )






def       lowerCAmelCase__       (				):
       import doctest

       doctest.testmod()


if __name__ == "__main__":
   main()






