






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      :       Optional[Any]    =						logging.get_logger(__name__)





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       RobertaPreLayerNormConfig.from_pretrained(
         A__       ,			architectures=["RobertaPreLayerNormForMaskedLM"]							)

     # convert state_dict
     __lowerCAmelCase             =       torch.load(hf_hub_download(repo_id=A__       ,			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=A__       ,			config=A__       ,			state_dict=A__							)
     model.save_pretrained(A__							)

     # convert tokenizer
     __lowerCAmelCase             =       AutoTokenizer.from_pretrained(A__							)
     tokenizer.save_pretrained(A__							)


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










import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					argparse.ArgumentParser()

      parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

      parser.add_argument(
          '--txt2img_unclip',
          default='kakaobrain/karlo-v1-alpha',
          type=str,
          required=False,
          help='The pretrained txt2img unclip.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

      snake_case__				:	Optional[int]					       =					CLIPImageProcessor()
      snake_case__				:	Optional[Any]					       =					CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

      snake_case__				:	Dict					       =					UnCLIPImageVariationPipeline(
          decoder=txtaimg.decoder,
          text_encoder=txtaimg.text_encoder,
          tokenizer=txtaimg.tokenizer,
          text_proj=txtaimg.text_proj,
          feature_extractor=feature_extractor,
          image_encoder=image_encoder,
          super_res_first=txtaimg.super_res_first,
          super_res_last=txtaimg.super_res_last,
          decoder_scheduler=txtaimg.decoder_scheduler,
          super_res_scheduler=txtaimg.super_res_scheduler,
      )

      imgaimg.save_pretrained(args.dump_path)





