




'''simple docstring'''




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__   :				List[Any]      =   logging.get_logger(__name__)






def 	_lowerCAmelCase      (  __snake_case  :			Optional[Any]			,							__snake_case  :			Optional[Any]      )					->					Optional[int]:
     __A						:			str								=	RobertaPreLayerNormConfig.from_pretrained(
         A_			,							architectures=['RobertaPreLayerNormForMaskedLM']      )

     # convert state_dict
     __A						:			Dict								=	torch.load(hf_hub_download(repo_id=A_			,							filename='pytorch_model.bin'      )      )
     __A						:			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.'      ):
               __A						:			int								=	'''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

          __A						:			Any								=	tensor_value

     __A						:			List[Any]								=	RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=A_			,							config=A_			,							state_dict=A_      )
     model.save_pretrained(A_      )

     # convert tokenizer
     __A						:			Optional[Any]								=	AutoTokenizer.from_pretrained(A_      )
     tokenizer.save_pretrained(A_      )


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






'''simple docstring'''




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
  from .camera import create_pan_cameras
  from .pipeline_shap_e import ShapEPipeline
  from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
  from .renderer import (
      BoundingBoxVolume,
      ImportanceRaySampler,
      MLPNeRFModelOutput,
      MLPNeRSTFModel,
      ShapEParamsProjModel,
      ShapERenderer,
      StratifiedRaySampler,
      VoidNeRFModel,
  )