




def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	int:



       """simple docstring"""

       return 1 if digit in (0, 1) else (digit * factorial(digit - 1						))







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	bool:



       """simple docstring"""

       A__							=   0
       A__							=   number
       while duplicate > 0:
              A__			,							A__							=   divmod(lowercase_			,    10						)
              fact_sum += factorial(lowercase_						)
       return fact_sum == number


if __name__ == "__main__":
  print("""Program to check whether a number is a Krisnamurthy Number or not.""")
  _lowerCamelCase     :		List[str]								=       int(input("""Enter number: """).strip())
  print(
      F'''{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number.'''
  )








import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       if "cls_token" in name:
              A__							=   name.replace('''cls_token'''			,    '''vit.embeddings.cls_token'''						)
       if "mask_token" in name:
              A__							=   name.replace('''mask_token'''			,    '''decoder.mask_token'''						)
       if "decoder_pos_embed" in name:
              A__							=   name.replace('''decoder_pos_embed'''			,    '''decoder.decoder_pos_embed'''						)
       if "pos_embed" in name and "decoder" not in name:
              A__							=   name.replace('''pos_embed'''			,    '''vit.embeddings.position_embeddings'''						)
       if "patch_embed.proj" in name:
              A__							=   name.replace('''patch_embed.proj'''			,    '''vit.embeddings.patch_embeddings.projection'''						)
       if "patch_embed.norm" in name:
              A__							=   name.replace('''patch_embed.norm'''			,    '''vit.embeddings.norm'''						)
       if "decoder_blocks" in name:
              A__							=   name.replace('''decoder_blocks'''			,    '''decoder.decoder_layers'''						)
       if "blocks" in name:
              A__							=   name.replace('''blocks'''			,    '''vit.encoder.layer'''						)
       if "attn.proj" in name:
              A__							=   name.replace('''attn.proj'''			,    '''attention.output.dense'''						)
       if "attn" in name:
              A__							=   name.replace('''attn'''			,    '''attention.self'''						)
       if "norm1" in name:
              A__							=   name.replace('''norm1'''			,    '''layernorm_before'''						)
       if "norm2" in name:
              A__							=   name.replace('''norm2'''			,    '''layernorm_after'''						)
       if "mlp.fc1" in name:
              A__							=   name.replace('''mlp.fc1'''			,    '''intermediate.dense'''						)
       if "mlp.fc2" in name:
              A__							=   name.replace('''mlp.fc2'''			,    '''output.dense'''						)
       if "decoder_embed" in name:
              A__							=   name.replace('''decoder_embed'''			,    '''decoder.decoder_embed'''						)
       if "decoder_norm" in name:
              A__							=   name.replace('''decoder_norm'''			,    '''decoder.decoder_norm'''						)
       if "decoder_pred" in name:
              A__							=   name.replace('''decoder_pred'''			,    '''decoder.decoder_pred'''						)
       if "norm.weight" in name and "decoder" not in name:
              A__							=   name.replace('''norm.weight'''			,    '''vit.layernorm.weight'''						)
       if "norm.bias" in name and "decoder" not in name:
              A__							=   name.replace('''norm.bias'''			,    '''vit.layernorm.bias'''						)

       return name







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	str:



       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              A__							=   orig_state_dict.pop(lowercase_						)

              if "qkv" in key:
                     A__							=   key.split('''.'''						)
                     A__							=   int(key_split[1]						)
                     if "decoder_blocks" in key:
                            A__							=   config.decoder_hidden_size
                            A__							=   '''decoder.decoder_layers.'''
                            if "weight" in key:
                                   A__							=   val[:dim, :]
                                   A__							=   val[dim : dim * 2, :]
                                   A__							=   val[-dim:, :]
                            elif "bias" in key:
                                   A__							=   val[:dim]
                                   A__							=   val[dim : dim * 2]
                                   A__							=   val[-dim:]
                     else:
                            A__							=   config.hidden_size
                            A__							=   '''vit.encoder.layer.'''
                            if "weight" in key:
                                   A__							=   val[:dim, :]
                                   A__							=   val[dim : dim * 2, :]
                                   A__							=   val[-dim:, :]
                            elif "bias" in key:
                                   A__							=   val[:dim]
                                   A__							=   val[dim : dim * 2]
                                   A__							=   val[-dim:]

              else:
                     A__							=   val

       return orig_state_dict







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

       A__							=   ViTMAEConfig()
       if "large" in checkpoint_url:
              A__							=   1_024
              A__							=   4_096
              A__							=   24
              A__							=   16
       elif "huge" in checkpoint_url:
              A__							=   14
              A__							=   1_280
              A__							=   5_120
              A__							=   32
              A__							=   16

       A__							=   ViTMAEForPreTraining(lowercase_						)

       A__							=   torch.hub.load_state_dict_from_url(lowercase_			,    map_location='''cpu'''						)['''model''']

       A__							=   ViTMAEImageProcessor(size=config.image_size						)

       A__							=   convert_state_dict(lowercase_			,    lowercase_						)

       model.load_state_dict(lowercase_						)
       model.eval()

       A__							=   '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

       A__							=   Image.open(requests.get(lowercase_			,    stream=lowercase_						).raw						)
       A__							=   ViTMAEImageProcessor(size=config.image_size						)
       A__							=   image_processor(images=lowercase_			,    return_tensors='''pt'''						)

       # forward pass
       torch.manual_seed(2						)
       A__							=   model(**lowercase_						)
       A__							=   outputs.logits

       if "large" in checkpoint_url:
              A__							=   torch.tensor(
                  [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]						)
       elif "huge" in checkpoint_url:
              A__							=   torch.tensor(
                  [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]						)
       else:
              A__							=   torch.tensor(
                  [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]						)

       # verify logits
       assert torch.allclose(logits[0, :3, :3]			,    lowercase_			,    atol=1E-4						)

       print(f"""Saving model to {pytorch_dump_folder_path}"""						)
       model.save_pretrained(lowercase_						)

       print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
       image_processor.save_pretrained(lowercase_						)


if __name__ == "__main__":
  _lowerCamelCase     :		Any								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
      type=str,
      help="""URL of the checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  _lowerCamelCase     :		Optional[int]								=       parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


