




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__       (						A__       :					str  ,			A__       :					List[str]  ,			A__       :					Tuple	):





       '''simple docstring'''



       __lowerCamelCase   =						RemBertConfig.from_json_file(A__	)
       print("""Building PyTorch model from configuration: {}""".format(str(A__	)	)	)
       __lowerCamelCase   =						RemBertModel(A__	)

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

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


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(
           '--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.'
       )
       UpperCAmelCase_									=	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__:    str					    =       {}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Tuple					    =       ['''GPTSw3Tokenizer''']


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

else:
   import sys

   A__:    str					    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


