





import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase :Tuple     =    logging.get_logger(__name__)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Tuple				       =   RobertaPreLayerNormConfig.from_pretrained(
					    UpperCAmelCase  ,  architectures=["RobertaPreLayerNormForMaskedLM"]				)

					# convert state_dict
					_snake_case       :					Optional[Any]				       =   torch.load(hf_hub_download(repo_id=UpperCAmelCase  ,  filename="pytorch_model.bin"				)				)
					_snake_case       :					Optional[Any]				       =   {}
					for tensor_key, tensor_value in original_state_dict.items():
										# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
										if tensor_key.startswith("roberta."				):
															_snake_case       :					str				       =   "roberta_prelayernorm." + tensor_key[len("roberta."				) :]

										# The original implementation contains weights which are not used, remove them from the state_dict
										if tensor_key.endswith(".self.LayerNorm.weight"				) or tensor_key.endswith(".self.LayerNorm.bias"				):
															continue

										_snake_case       :					Union[str, Any]				       =   tensor_value

					_snake_case       :					Optional[Any]				       =   RobertaPreLayerNormForMaskedLM.from_pretrained(
					    pretrained_model_name_or_path=UpperCAmelCase  ,  config=UpperCAmelCase  ,  state_dict=UpperCAmelCase				)
					model.save_pretrained(UpperCAmelCase				)

					# convert tokenizer
					_snake_case       :					List[Any]				       =   AutoTokenizer.from_pretrained(UpperCAmelCase				)
					tokenizer.save_pretrained(UpperCAmelCase				)


if __name__ == "__main__":
							__lowerCAmelCase :List[str]     =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--checkpoint-repo',
							    default=None,
							    type=str,
							    required=True,
							    help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							__lowerCAmelCase :Optional[Any]     =    parser.parse_args()
							convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser







def    A				(							UpperCAmelCase=None				):
					_snake_case       :					Union[str, Any]				       =   argparse.ArgumentParser(add_help=UpperCAmelCase  ,  allow_abbrev=UpperCAmelCase				)
					# The main config parser
					_snake_case       :					Tuple				       =   config_command_parser(UpperCAmelCase				)
					# The subparser to add commands to
					_snake_case       :					Any				       =   config_parser.add_subparsers(title="subcommands"  ,  dest="subcommand"				)

					# Then add other parsers with the parent parser
					default_command_parser(UpperCAmelCase  ,  parents=[parent_parser]				)
					update_command_parser(UpperCAmelCase  ,  parents=[parent_parser]				)

					return config_parser







def    A				(							):
					_snake_case       :					str				       =   get_config_parser()
					_snake_case       :					Union[str, Any]				       =   config_parser.parse_args()

					if not hasattr(UpperCAmelCase  ,  "func"				):
										config_parser.print_help()
										exit(1				)

					# Run
					args.func(UpperCAmelCase				)


if __name__ == "__main__":
							main()