





'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

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

   return name
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]     ,__lowerCamelCase : Dict			):
   for key in orig_state_dict.copy().keys():
      lowercase_      :Any         =			orig_state_dict.pop(__lowerCamelCase			)

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

      else:
         lowercase_      :Tuple         =			val

   return orig_state_dict
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : Union[str, Any]			):
   lowercase_      :Union[str, Any]         =			ViTMAEConfig()
   if "large" in checkpoint_url:
      lowercase_      :List[str]         =			10_24
      lowercase_      :Optional[int]         =			40_96
      lowercase_      :List[Any]         =			24
      lowercase_      :Union[str, Any]         =			16
   elif "huge" in checkpoint_url:
      lowercase_      :str         =			14
      lowercase_      :List[Any]         =			12_80
      lowercase_      :int         =			51_20
      lowercase_      :int         =			32
      lowercase_      :Optional[int]         =			16

   lowercase_      :Union[str, Any]         =			ViTMAEForPreTraining(__lowerCamelCase			)

   lowercase_      :Optional[Any]         =			torch.hub.load_state_dict_from_url(__lowerCamelCase     ,map_location="cpu"			)["model"]

   lowercase_      :Optional[Any]         =			ViTMAEImageProcessor(size=config.image_size			)

   lowercase_      :int         =			convert_state_dict(__lowerCamelCase     ,__lowerCamelCase			)

   model.load_state_dict(__lowerCamelCase			)
   model.eval()

   lowercase_      :Optional[int]         =			"https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"

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

   # forward pass
   torch.manual_seed(2			)
   lowercase_      :List[Any]         =			model(**__lowerCamelCase			)
   lowercase_      :Optional[int]         =			outputs.logits

   if "large" in checkpoint_url:
      lowercase_      :List[Any]         =			torch.tensor(
          [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]			)
   elif "huge" in checkpoint_url:
      lowercase_      :Tuple         =			torch.tensor(
          [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]			)
   else:
      lowercase_      :Optional[int]         =			torch.tensor(
          [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]			)

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

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

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


if __name__ == "__main__":
 lowerCAmelCase :					Tuple 							=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 :					Tuple 							=parser.parse_args()
 convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









'''simple docstring'''


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase :					Any 							={
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase :					int 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      "maskformer"
     __A								=      {"hidden_size": "mask_feature_size"}
     __A								=      ["resnet", "swin"]
     __A								=      ["detr"]





     def __init__(			self		: List[Any]     ,				lowercase		: int = 256     ,				lowercase		: int = 256     ,				lowercase		: float = 0.1     ,				lowercase		: bool = False     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: float = 0.02     ,				lowercase		: float = 1.0     ,				lowercase		: float = 1.0     ,				lowercase		: float = 1.0     ,				lowercase		: float = 20.0     ,				lowercase		: Optional[bool] = None     ,				**lowercase		: Any     ,				):





        """simple docstring"""




        if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           lowercase_      :Any         =			SwinConfig(
               image_size=384     ,				in_channels=3     ,				patch_size=4     ,				embed_dim=128     ,				depths=[2, 2, 18, 2]     ,				num_heads=[4, 8, 16, 32]     ,				window_size=12     ,				drop_path_rate=0.3     ,				out_features=["stage1", "stage2", "stage3", "stage4"]     ,				)

        if isinstance(lowercase     ,				lowercase						):
           lowercase_      :Optional[int]         =			backbone_config.pop("model_type"						)
           lowercase_      :Optional[int]         =			CONFIG_MAPPING[backbone_model_type]
           lowercase_      :int         =			config_class.from_dict(lowercase						)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
           logger.warning_once(
               F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
               F'Supported model types: {",".join(self.backbones_supported						)}'						)

        if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           lowercase_      :Optional[Any]         =			DetrConfig()
        else:
           # verify that the decoder is supported
           lowercase_      :Tuple         =			(
               decoder_config.pop("model_type"						) if isinstance(lowercase     ,				lowercase						) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
              raise ValueError(
                  F'Transformer Decoder {decoder_type} not supported, please use one of'
                  F' {",".join(self.decoders_supported						)}'						)
           if isinstance(lowercase     ,				lowercase						):
              lowercase_      :str         =			CONFIG_MAPPING[decoder_type]
              lowercase_      :List[str]         =			config_class.from_dict(lowercase						)

        lowercase_      :str         =			backbone_config
        lowercase_      :Union[str, Any]         =			decoder_config
        # main feature dimension for the model
        lowercase_      :Any         =			fpn_feature_size
        lowercase_      :Optional[int]         =			mask_feature_size
        # initializer
        lowercase_      :List[Any]         =			init_std
        lowercase_      :Union[str, Any]         =			init_xavier_std
        # Hungarian matcher && loss
        lowercase_      :List[str]         =			cross_entropy_weight
        lowercase_      :int         =			dice_weight
        lowercase_      :List[str]         =			mask_weight
        lowercase_      :Optional[Any]         =			use_auxiliary_loss
        lowercase_      :str         =			no_object_weight
        lowercase_      :int         =			output_auxiliary_logits

        lowercase_      :Optional[Any]         =			self.decoder_config.encoder_attention_heads
        lowercase_      :int         =			self.decoder_config.num_hidden_layers
        super().__init__(**lowercase						)





     @classmethod
     def 							lowercase__				(			cls		: Tuple     ,				lowercase		: PretrainedConfig     ,				lowercase		: PretrainedConfig     ,				**lowercase		: Union[str, Any]						):





        """simple docstring"""




        return cls(
            backbone_config=lowercase     ,				decoder_config=lowercase     ,				**lowercase     ,				)







     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :str         =			copy.deepcopy(self.__dict__						)
        lowercase_      :int         =			self.backbone_config.to_dict()
        lowercase_      :List[Any]         =			self.decoder_config.to_dict()
        lowercase_      :Optional[Any]         =			self.__class__.model_type
        return output


