





'''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()
_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)




def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					str			):
   __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__":
 _SCREAMING_SNAKE_CASE   =      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.'''
 )
 _SCREAMING_SNAKE_CASE   =      parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)












'''simple docstring'''



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Union[List[PIL.Image.Image], np.ndarray]
	a      :			Optional[List[bool]]


if is_transformers_available() and is_torch_available():
	from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





