code: 
  
  
  
  
  
  '''simple docstring'''
  
  import argparse
  
  from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Tuple		, _lowerCamelCase      :      Union[str, Any] ):
      # Initialize PyTorch model
      A__       =    CanineConfig()
      A__       =    CanineModel(_lowerCamelCase )
      model.eval()
  
      print(F"Building PyTorch model from configuration: {config}" )
  
      # Load weights from tf checkpoint
      load_tf_weights_in_canine(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase )
  
      # Save pytorch-model (weights and configuration)
      print(F"Save PyTorch model to {pytorch_dump_path}" )
      model.save_pretrained(_lowerCamelCase )
  
      # Save tokenizer files
      A__       =    CanineTokenizer()
      print(F"Save tokenizer files to {pytorch_dump_path}" )
      tokenizer.save_pretrained(_lowerCamelCase )
  
  
  if __name__ == "__main__":
    __lowerCAmelCase							:			List[str]				=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.",
    )
    __lowerCAmelCase							:			int				=parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  __lowerCAmelCase							:			Dict				=logging.get_logger(__name__)
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :List[str]		, *lowercase_   :Dict		, **lowercase_   :int							)->							None:
       warnings.warn(
           "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
           " use ViltImageProcessor instead."		, lowercase_		, )
       super().__init__(*lowercase_		, **lowercase_							)
  
  
  
  