




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       :	str									=     logging.get_logger(__name__)

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: str  ):


						'''simple docstring'''



						lowerCamelCase_	  =					RobertaPreLayerNormConfig.from_pretrained(
						    lowerCamelCase_			,							architectures=['RobertaPreLayerNormForMaskedLM']  )

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

												lowerCamelCase_	  =					tensor_value

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

						# convert tokenizer
						lowerCamelCase_	  =					AutoTokenizer.from_pretrained(lowerCamelCase_  )
						tokenizer.save_pretrained(lowerCamelCase_  )


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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase       :	Dict									=     {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Optional[int]									=     ["ViTFeatureExtractor"]
 lowerCamelCase       :	Dict									=     ["ViTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	int									=     [
     "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTForImageClassification",
     "ViTForMaskedImageModeling",
     "ViTModel",
     "ViTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Tuple									=     [
     "TFViTForImageClassification",
     "TFViTModel",
     "TFViTPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Dict									=     [
     "FlaxViTForImageClassification",
     "FlaxViTModel",
     "FlaxViTPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

 lowerCamelCase       :	Any									=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



