

import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()

def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   List[str]						):



      """simple docstring"""

      A_	     =							CanineConfig()
      A_	     =							CanineModel(__UpperCamelCase						)
      model.eval()

      print(f'''Building PyTorch model from configuration: {config}'''						)

      # Load weights from tf checkpoint
      load_tf_weights_in_canine(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)

      # Save pytorch-model (weights and configuration)
      print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
      model.save_pretrained(__UpperCamelCase						)

      # Save tokenizer files
      A_	     =							CanineTokenizer()
      print(f'''Save tokenizer files to {pytorch_dump_path}'''						)
      tokenizer.save_pretrained(__UpperCamelCase						)


if __name__ == "__main__":
      __a						:List[Any]            = argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--tf_checkpoint_path',
          default=None,
          type=str,
          required=True,
          help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
      )
      parser.add_argument(
          '--pytorch_dump_path',
          default=None,
          type=str,
          required=True,
          help='Path to a folder where the PyTorch model will be placed.',
      )
      __a						:Optional[Any]            = parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


import pytest

from datasets.utils.version import Version

@pytest.mark.parametrize(
    "other, expected_equality"							,[
        (Version("1.0.0"						), True),
        ("1.0.0", True),
        (Version("2.0.0"						), False),
        ("2.0.0", False),
        ("1", False),
        ("a", False),
        (1, False),
        (None, False),
    ]							,)
def 			__snake_case	(   __UpperCamelCase  :   Any							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							Version("1.0.0"						)
      assert (version == other) is expected_equality
      assert (version != other) is not expected_equality
      assert (hash(__UpperCamelCase						) == hash(__UpperCamelCase						)) is expected_equality