





"""simple docstring"""





import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						int			):







      '''simple docstring'''




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

      return name


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[Any]							,						SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      for key in orig_state_dict.copy().keys():
            lowerCAmelCase  						=       orig_state_dict.pop(SCREAMING_SNAKE_CASE			)

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

            else:
                  lowerCAmelCase  						=       val

      return orig_state_dict


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						Tuple			):







      '''simple docstring'''




      lowerCAmelCase  						=       ViTMAEConfig()
      if "large" in checkpoint_url:
            lowerCAmelCase  						=       10_24
            lowerCAmelCase  						=       40_96
            lowerCAmelCase  						=       24
            lowerCAmelCase  						=       16
      elif "huge" in checkpoint_url:
            lowerCAmelCase  						=       14
            lowerCAmelCase  						=       12_80
            lowerCAmelCase  						=       51_20
            lowerCAmelCase  						=       32
            lowerCAmelCase  						=       16

      lowerCAmelCase  						=       ViTMAEForPreTraining(SCREAMING_SNAKE_CASE			)

      lowerCAmelCase  						=       torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE							,						map_location="""cpu"""			)["""model"""]

      lowerCAmelCase  						=       ViTMAEImageProcessor(size=config.image_size			)

      lowerCAmelCase  						=       convert_state_dict(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

      model.load_state_dict(SCREAMING_SNAKE_CASE			)
      model.eval()

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

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

      # forward pass
      torch.manual_seed(2			)
      lowerCAmelCase  						=       model(**SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       outputs.logits

      if "large" in checkpoint_url:
            lowerCAmelCase  						=       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:
            lowerCAmelCase  						=       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:
            lowerCAmelCase  						=       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]							,						SCREAMING_SNAKE_CASE							,						atol=1e-4			)

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

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__					=   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."
       )

       SCREAMING_SNAKE_CASE__					=   parser.parse_args()
       convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)













"""simple docstring"""





# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


SCREAMING_SNAKE_CASE__					=   open  # noqa: we just need to have a builtin inside this module to test it properly






