
'''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 				_snake_case (		lowercase			,    lowercase						)       ->       Any:
				# Initialize PyTorch model
				__a       :     Union[str, Any]								=	CanineConfig()
				__a       :     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
				__a       :     Optional[Any]								=	CanineTokenizer()
				print(F"""Save tokenizer files to {pytorch_dump_path}"""						)
				tokenizer.save_pretrained(__UpperCAmelCase						)


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




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase:  Union[str, Any]			     =				{"""tokenization_phobert""": ["""PhobertTokenizer"""]}


if TYPE_CHECKING:
		from .tokenization_phobert import PhobertTokenizer

else:
		import sys

		UpperCAmelCase:  Optional[int]			     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



