

'''simple docstring'''






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()


def 						_A	(       A__	,							A__	,							A__	,							A__ ):






 """simple docstring"""



 __lowercase				    =					FunnelConfig.from_json_file(A__ )
 print(F"Building PyTorch model from configuration: {config}" )
 __lowercase				    =					FunnelBaseModel(A__ ) if base_model else FunnelModel(A__ )

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

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


if __name__ == "__main__":
 lowerCAmelCase__   		=      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(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 parser.add_argument(
     '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
 )
 lowerCAmelCase__   		=      parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
 )








"""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											=						["""MLukeTokenizer"""]

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


else:
  import sys

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