






"""simple docstring"""







import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






def    _lowerCAmelCase							(   lowerCAmelCase		):







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

       return name






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''
       for key in orig_state_dict.copy().keys():
              UpperCAmelCase						     =      orig_state_dict.pop(A__		)

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

              else:
                     UpperCAmelCase						     =      val

       return orig_state_dict






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      ViTMAEConfig()
       if "large" in checkpoint_url:
              UpperCAmelCase						     =      1024
              UpperCAmelCase						     =      4096
              UpperCAmelCase						     =      24
              UpperCAmelCase						     =      16
       elif "huge" in checkpoint_url:
              UpperCAmelCase						     =      14
              UpperCAmelCase						     =      1280
              UpperCAmelCase						     =      5120
              UpperCAmelCase						     =      32
              UpperCAmelCase						     =      16

       UpperCAmelCase						     =      ViTMAEForPreTraining(A__		)

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

       UpperCAmelCase						     =      ViTMAEImageProcessor(size=config.image_size		)

       UpperCAmelCase						     =      convert_state_dict(A__						,   A__		)

       model.load_state_dict(A__		)
       model.eval()

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

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

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

       if "large" in checkpoint_url:
              UpperCAmelCase						     =      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:
              UpperCAmelCase						     =      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:
              UpperCAmelCase						     =      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]						,   A__						,   atol=1e-4		)

       print(F'''Saving model to {pytorch_dump_folder_path}'''		)
       model.save_pretrained(A__		)

       print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
       image_processor.save_pretrained(A__		)


if __name__ == "__main__":
 lowerCAmelCase_						:     str							  =   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_						:     str							  =   parser.parse_args()
 convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




from . import (
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)





