import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[str]		, _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				int			):
						# Initialise PyTorch model
						UpperCamelCase_		:							List[str]  =		RemBertConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
						print("""Building PyTorch model from configuration: {}""".format(str(_SCREAMING_SNAKE_CASE			)			)			)
						UpperCamelCase_		:							Optional[int]  =		RemBertModel(_SCREAMING_SNAKE_CASE			)

						# Load weights from tf checkpoint
						load_tf_weights_in_rembert(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

						# Save pytorch-model
						print("""Save PyTorch model to {}""".format(_SCREAMING_SNAKE_CASE			)			)
						torch.save(model.state_dict()		, _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
	SCREAMING_SNAKE_CASE		:     List[Any]         =	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(
	    "--rembert_config_file",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        "The config json file corresponding to the pre-trained RemBERT 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."
	)
	SCREAMING_SNAKE_CASE		:     int         =	parser.parse_args()
	convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_A							=  {}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  ["""BartphoTokenizer"""]

if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_bartpho import BartphoTokenizer

else:
    import sys

    _A							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

