"""simple docstring"""
from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

SCREAMING_SNAKE_CASE__   :      str							=typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
SCREAMING_SNAKE_CASE__   :      int							=typing.Union[np.floataa, int, float]  # noqa: UP007



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->List[Any]:
    return np.sqrt(np.sum((np.asarray(_A ) - np.asarray(_A )) ** 2 ) )



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->Optional[int]:
    return sum((va - va) ** 2 for va, va in zip(_A						,	_A ) ) ** (1 / 2)


if __name__ == "__main__":



  def 					UpperCamelCase					(       )      ->List[Any]:
      from timeit import timeit

      print('''Without Numpy''' )
      print(
          timeit(
              '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'''						,	number=1_0000						,	globals=globals()						,	) )
      print('''With Numpy''' )
      print(
          timeit(
              '''euclidean_distance([1, 2, 3], [4, 5, 6])'''						,	number=1_0000						,	globals=globals()						,	) )


  benchmark()






'''simple docstring'''







import argparse

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


logging.set_verbosity_info()






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							TaConfig.from_json_file(_A       )
       print(f'Building PyTorch model from configuration: {config}'       )
       _lowerCAmelCase     :				Union[str, Any]       =							TaForConditionalGeneration(_A       )

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

       # Save pytorch-model
       print(f'Save PyTorch model to {pytorch_dump_path}'       )
       model.save_pretrained(_A       )


if __name__ == "__main__":
       lowerCAmelCase     :  str											=    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."""
       )
       lowerCAmelCase     :  int											=    parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

