
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()
lowercase__	:int										=					logging.get_logger(__name__)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		RobertaPreLayerNormConfig.from_pretrained(
					    lowerCAmelCase__				,    architectures=['''RobertaPreLayerNormForMaskedLM''']	)

					# convert state_dict
					lowercase					=		torch.load(hf_hub_download(repo_id=lowerCAmelCase__				,    filename='''pytorch_model.bin'''	)	)
					lowercase					=		{}
					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.'''	):
															lowercase					=		'''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

										lowercase					=		tensor_value

					lowercase					=		RobertaPreLayerNormForMaskedLM.from_pretrained(
					    pretrained_model_name_or_path=lowerCAmelCase__				,    config=lowerCAmelCase__				,    state_dict=lowerCAmelCase__	)
					model.save_pretrained(lowerCAmelCase__	)

					# convert tokenizer
					lowercase					=		AutoTokenizer.from_pretrained(lowerCAmelCase__	)
					tokenizer.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
							lowercase__	: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."
							)
							lowercase__	:Any										=					parser.parse_args()
							convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)






import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    "The `inpainting.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionInpaintPipeline` instead."
)




