



"""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  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		# Initialize PyTorch model
		_lowercase				: Union[str, Any]     =   CanineConfig()
		_lowercase				: Union[str, Any]     =   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
		_lowercase				: Optional[Any]     =   CanineTokenizer()
		print(F"""Save tokenizer files to {pytorch_dump_path}"""      )
		tokenizer.save_pretrained(__UpperCAmelCase      )


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





"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->Dict:

   """simple docstring"""



   a_						   =     RoFormerConfig.from_json_file(UpperCAmelCase     )
   print(F'''Building PyTorch model from configuration: {config}'''     )
   a_						   =     RoFormerForMaskedLM(UpperCAmelCase     )

   # Load weights from tf checkpoint
   load_tf_weights_in_roformer(UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )

   # Save pytorch-model
   print(F'''Save PyTorch model to {pytorch_dump_path}'''     )
   torch.save(model.state_dict() ,   UpperCAmelCase ,   _use_new_zipfile_serialization=UpperCAmelCase     )


if __name__ == "__main__":
      UpperCamelCase_						   =   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(
          '--bert_config_file',
          default=None,
          type=str,
          required=True,
          help=(
              'The config json file corresponding to the pre-trained BERT model. \n'
              'This specifies the model architecture.'
          ),
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      UpperCamelCase_						   =   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)