





"""simple docstring"""





import argparse

import torch

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


logging.set_verbosity_info()


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


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

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

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


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(
						    '''--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.'''
						)
						lowerCAmelCase__     			=			parser.parse_args()
						convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)













"""simple docstring"""





from diffusers.utils.testing_utils import require_onnxruntime





@require_onnxruntime
class        _lowerCamelCase      :
						pass






