


"""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 _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     RemBertConfig.from_json_file(_UpperCamelCase )
			print("Building PyTorch model from configuration: {}".format(str(_UpperCamelCase ) ) )
			__lowerCAmelCase							       =     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__":
	A	:  Optional[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."
	)
	A	:  List[Any]            =     parser.parse_args()
	convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)








"""simple docstring"""


from __future__ import annotations
class     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a=None      ):
										__lowerCAmelCase							       =     data
										__lowerCAmelCase							       =     None



							def __repr__(	self      ):
										__lowerCAmelCase							       =     []
										__lowerCAmelCase							       =     self
										while temp:
													string_rep.append(f"{temp.data}"      )
													__lowerCAmelCase							       =     temp.next
										return "->".join(__a      )


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if not elements_list:
						raise Exception("The Elements List is empty" )

			__lowerCAmelCase							       =     __lowerCAmelCase							       =     Node(elements_list[0] )
			for i in range(1       ,					len(_UpperCamelCase ) ):
						__lowerCAmelCase							       =     Node(elements_list[i] )
						__lowerCAmelCase							       =     current.next
			return head


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if head_node is not None and isinstance(_UpperCamelCase       ,					_UpperCamelCase ):
						print_reverse(head_node.next )
						print(head_node.data )


def _lowerCamelCase      (						):

			'''simple docstring'''




			from doctest import testmod

			testmod()

			__lowerCAmelCase							       =     make_linked_list([14, 52, 14, 12, 43] )
			print("Linked List:" )
			print(_UpperCamelCase )
			print("Elements in Reverse:" )
			print_reverse(_UpperCamelCase )


if __name__ == "__main__":
	main()




