

'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor


def    __lowerCamelCase     ( SCREAMING_SNAKE_CASE_	:      Union[str, Any]       )				->  Tuple:
      """simple docstring"""

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

      return name


def    __lowerCamelCase     ( SCREAMING_SNAKE_CASE_	:      Tuple    ,    SCREAMING_SNAKE_CASE_	:      Union[str, Any]       )				->  Any:
      """simple docstring"""

      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			orig_state_dict.pop(__SCREAMING_SNAKE_CASE       )

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

            else:
                  SCREAMING_SNAKE_CASE_ :     List[str]	=			val

      return orig_state_dict


def    __lowerCamelCase     ( SCREAMING_SNAKE_CASE_	:      Tuple    ,    SCREAMING_SNAKE_CASE_	:      Tuple       )				->  Dict:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			ViTMAEConfig()
      if "large" in checkpoint_url:
            SCREAMING_SNAKE_CASE_ :     int	=			1_0_2_4
            SCREAMING_SNAKE_CASE_ :     str	=			4_0_9_6
            SCREAMING_SNAKE_CASE_ :     Tuple	=			2_4
            SCREAMING_SNAKE_CASE_ :     Tuple	=			1_6
      elif "huge" in checkpoint_url:
            SCREAMING_SNAKE_CASE_ :     Optional[int]	=			1_4
            SCREAMING_SNAKE_CASE_ :     Optional[int]	=			1_2_8_0
            SCREAMING_SNAKE_CASE_ :     List[Any]	=			5_1_2_0
            SCREAMING_SNAKE_CASE_ :     List[str]	=			3_2
            SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			1_6

      SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			ViTMAEForPreTraining(__SCREAMING_SNAKE_CASE       )

      SCREAMING_SNAKE_CASE_ :     Tuple	=			torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE    ,    map_location="cpu"       )['model']

      SCREAMING_SNAKE_CASE_ :     Any	=			ViTMAEImageProcessor(size=config.image_size       )

      SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			convert_state_dict(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE       )

      model.load_state_dict(__SCREAMING_SNAKE_CASE       )
      model.eval()

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

      SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			Image.open(requests.get(__SCREAMING_SNAKE_CASE    ,    stream=__SCREAMING_SNAKE_CASE       ).raw       )
      SCREAMING_SNAKE_CASE_ :     int	=			ViTMAEImageProcessor(size=config.image_size       )
      SCREAMING_SNAKE_CASE_ :     List[str]	=			image_processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors="pt"       )

      # forward pass
      torch.manual_seed(2       )
      SCREAMING_SNAKE_CASE_ :     Optional[int]	=			model(**__SCREAMING_SNAKE_CASE       )
      SCREAMING_SNAKE_CASE_ :     List[str]	=			outputs.logits

      if "large" in checkpoint_url:
            SCREAMING_SNAKE_CASE_ :     Any	=			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:
            SCREAMING_SNAKE_CASE_ :     Any	=			torch.tensor(
                [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]       )
      else:
            SCREAMING_SNAKE_CASE_ :     Tuple	=			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]    ,    __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__":
      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.'
      )

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







'''simple docstring'''






from math import pi






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   float:
      return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
      print(arc_length(90, 10))
