



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()
a      :  Dict			     =			logging.get_logger(__name__)







def  lowerCAmelCase_	(lowerCAmelCase__:		str		,						lowerCAmelCase__:		str  ):





			"""simple docstring"""


			UpperCAmelCase_:		int							      =     RobertaPreLayerNormConfig.from_pretrained(
			    a__		,						architectures=["""RobertaPreLayerNormForMaskedLM"""]  )

			# convert state_dict
			UpperCAmelCase_:		List[str]							      =     torch.load(hf_hub_download(repo_id=a__		,						filename="""pytorch_model.bin"""  )  )
			UpperCAmelCase_:		Union[str, 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."""  ):
									UpperCAmelCase_:		Dict							      =     """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

						UpperCAmelCase_:		List[Any]							      =     tensor_value

			UpperCAmelCase_:		Dict							      =     RobertaPreLayerNormForMaskedLM.from_pretrained(
			    pretrained_model_name_or_path=a__		,						config=a__		,						state_dict=a__  )
			model.save_pretrained(a__  )

			# convert tokenizer
			UpperCAmelCase_:		Dict							      =     AutoTokenizer.from_pretrained(a__  )
			tokenizer.save_pretrained(a__  )


if __name__ == "__main__":
	a      :  Union[str, Any]			     =			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.'
	)
	a      :  Union[str, Any]			     =			parser.parse_args()
	convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)













# Copyright 2023 The HuggingFace Inc. 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 re

from ..utils import cached_file


# docstyle-ignore
UpperCamelCase__           =			"\nHuman: <<task>>\n\nAssistant: "


UpperCamelCase__           =			"huggingface-tools/default-prompts"
UpperCamelCase__           =			{"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}





def      _UpperCamelCase  (a__ :List[Any] ,      a__ :Optional[Any] ,      a__ :List[str]="run"							):






							"""simple docstring"""





							if prompt_or_repo_id is None:
														UpperCamelCase__					  =      DEFAULT_PROMPTS_REPO

							# prompt is considered a repo ID when it does not contain any kind of space
							if re.search("""\\s""" ,      a__							) is not None:
														return prompt_or_repo_id

							UpperCamelCase__					  =      cached_file(
							    a__ ,      PROMPT_FILES[mode] ,      repo_type="""dataset""" ,      user_agent={"""agent""": agent_name}							)
							with open(a__ ,      """r""" ,      encoding="""utf-8"""							) as f:
														return f.read()




