




"""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 				__lowerCAmelCase					(					__lowerCAmelCase			:		Union[str, Any]  ,	__lowerCAmelCase			:		Union[str, Any]						)    ->       Optional[Any]:
 # Initialize PyTorch model
 _UpperCamelCase      :	Any					      =  CanineConfig()
 _UpperCamelCase      :	Optional[int]					      =  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
 _UpperCamelCase      :	int					      =  CanineTokenizer()
 print(f"Save tokenizer files to {pytorch_dump_path}"						)
 tokenizer.save_pretrained(__lowerCAmelCase						)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE						 =     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.""",
    )
    _SCREAMING_SNAKE_CASE						 =     parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)









"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_SCREAMING_SNAKE_CASE						 =     logging.get_logger(__name__)







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 def __init__(self					,     *lowerCAmelCase__					,     **lowerCAmelCase__   ):

  '''simple docstring'''







  warnings.warn(
      "The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
      " Please use LayoutLMv3ImageProcessor instead."					,     lowerCAmelCase__					,     )
  super().__init__(*lowerCAmelCase__					,     **lowerCAmelCase__   )



