




import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, Any]  ):


      '''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     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]			,							lowercase			: Tuple  ):


      '''simple docstring'''



      for key in orig_state_dict.copy().keys():
            lowerCamelCase_	  =					orig_state_dict.pop(lowercase  )

            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     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]			,							lowercase			: str  ):


      '''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(lowercase  )

      lowerCamelCase_	  =					torch.hub.load_state_dict_from_url(lowercase			,							map_location='cpu'  )['model']

      lowerCamelCase_	  =					ViTMAEImageProcessor(size=config.image_size  )

      lowerCamelCase_	  =					convert_state_dict(lowercase			,							lowercase  )

      model.load_state_dict(lowercase  )
      model.eval()

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

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

      # forward pass
      torch.manual_seed(2  )
      lowerCamelCase_	  =					model(**lowercase  )
      lowerCamelCase_	  =					outputs.logits

      if "large" in checkpoint_url:
            lowerCamelCase_	  =					torch.tensor(
                [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]  )
      elif "huge" in checkpoint_url:
            lowerCamelCase_	  =					torch.tensor(
                [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]  )
      else:
            lowerCamelCase_	  =					torch.tensor(
                [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]  )

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









from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass







@dataclass
class 						A:



 '''simple docstring'''


 UpperCamelCase		     =		42
 UpperCamelCase		     =		None
 UpperCamelCase		     =		None

lowerCamelCase       :	str									=     namedtuple("CoinsDistribResult", "moves excess")

def     _SCREAMING_SNAKE_CASE    (							lowercase			: TreeNode | None  ):


      '''simple docstring'''



      if root is None:
            return 0

      # Validation
      def count_nodes(lowercase			: TreeNode | None  ) -> int:
            if node is None:
                  return 0

            return count_nodes(node.left  ) + count_nodes(node.right  ) + 1

      def count_coins(lowercase			: TreeNode | None  ) -> int:
            if node is None:
                  return 0

            return count_coins(node.left  ) + count_coins(node.right  ) + node.data

      if count_nodes(lowercase  ) != count_coins(lowercase  ):
            raise ValueError('The nodes number should be same as the number of coins'  )

      # Main calculation
      def get_distrib(lowercase			: TreeNode | None  ) -> CoinsDistribResult:

            if node is None:
                  return CoinsDistribResult(0			,							1  )

            lowerCamelCase_  ,       lowerCamelCase_	  =					get_distrib(node.left  )
            lowerCamelCase_  ,       lowerCamelCase_	  =					get_distrib(node.right  )

            lowerCamelCase_	  =					1 - left_distrib_excess
            lowerCamelCase_	  =					1 - right_distrib_excess

            lowerCamelCase_	  =					(
                left_distrib_moves
                + right_distrib_moves
                + abs(lowercase  )
                + abs(lowercase  )
            )
            lowerCamelCase_	  =					node.data - coins_to_left - coins_to_right

            return CoinsDistribResult(lowercase			,							lowercase  )

      return get_distrib(lowercase  )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()



