




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def    A						(__A   :    Dict       )   ->      List[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							SwinConfig(image_size=192       )

     if "base" in model_name:
          UpperCAmelCase_          =							6
          UpperCAmelCase_          =							128
          UpperCAmelCase_          =							(2, 2, 18, 2)
          UpperCAmelCase_          =							(4, 8, 16, 32)
     elif "large" in model_name:
          UpperCAmelCase_          =							12
          UpperCAmelCase_          =							192
          UpperCAmelCase_          =							(2, 2, 18, 2)
          UpperCAmelCase_          =							(6, 12, 24, 48)
     else:
          raise ValueError('''Model not supported, only supports base and large variants'''       )

     UpperCAmelCase_          =							window_size
     UpperCAmelCase_          =							embed_dim
     UpperCAmelCase_          =							depths
     UpperCAmelCase_          =							num_heads

     return config


def    A						(__A   :    Dict       )   ->      str:







     """simple docstring"""


     if "encoder.mask_token" in name:
          UpperCAmelCase_          =							name.replace('''encoder.mask_token'''   ,				'''embeddings.mask_token'''       )
     if "encoder.patch_embed.proj" in name:
          UpperCAmelCase_          =							name.replace('''encoder.patch_embed.proj'''   ,				'''embeddings.patch_embeddings.projection'''       )
     if "encoder.patch_embed.norm" in name:
          UpperCAmelCase_          =							name.replace('''encoder.patch_embed.norm'''   ,				'''embeddings.norm'''       )
     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 name == "encoder.norm.weight":
          UpperCAmelCase_          =							'''layernorm.weight'''
     if name == "encoder.norm.bias":
          UpperCAmelCase_          =							'''layernorm.bias'''

     if "decoder" in name:
          pass
     else:
          UpperCAmelCase_          =							'''swin.''' + name

     return name


def    A						(__A   :    Dict   ,				__A   :    List[Any]       )   ->      Any:







     """simple docstring"""


     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_          =							orig_state_dict.pop(__A       )

          if "attn_mask" in key:
               pass
          elif "qkv" in key:
               UpperCAmelCase_          =							key.split('''.'''       )
               UpperCAmelCase_          =							int(key_split[2]       )
               UpperCAmelCase_          =							int(key_split[4]       )
               UpperCAmelCase_          =							model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    UpperCAmelCase_          =							val[:dim, :]
                    UpperCAmelCase_          =							val[
                        dim : dim * 2, :
                    ]
                    UpperCAmelCase_          =							val[-dim:, :]
               else:
                    UpperCAmelCase_          =							val[
                        :dim
                    ]
                    UpperCAmelCase_          =							val[
                        dim : dim * 2
                    ]
                    UpperCAmelCase_          =							val[
                        -dim:
                    ]
          else:
               UpperCAmelCase_          =							val

     return orig_state_dict


def    A						(__A   :    str   ,				__A   :    Tuple   ,				__A   :    List[str]   ,				__A   :    List[str]       )   ->      Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_          =							torch.load(__A   ,				map_location='''cpu'''       )['''model''']

     UpperCAmelCase_          =							get_swin_config(__A       )
     UpperCAmelCase_          =							SwinForMaskedImageModeling(__A       )
     model.eval()

     UpperCAmelCase_          =							convert_state_dict(__A   ,				__A       )
     model.load_state_dict(__A       )

     UpperCAmelCase_          =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''

     UpperCAmelCase_          =							ViTImageProcessor(size={'''height''': 192, '''width''': 192}       )
     UpperCAmelCase_          =							Image.open(requests.get(__A   ,				stream=__A       ).raw       )
     UpperCAmelCase_          =							image_processor(images=__A   ,				return_tensors='''pt'''       )

     with torch.no_grad():
          UpperCAmelCase_          =							model(**__A       ).logits

     print(outputs.keys()       )
     print('''Looks ok!'''       )

     if pytorch_dump_folder_path is not None:
          print(F"""Saving model {model_name} 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 push_to_hub:
          print(F"""Pushing model and image processor for {model_name} to hub"""       )
          model.push_to_hub(F"""microsoft/{model_name}"""       )
          image_processor.push_to_hub(F"""microsoft/{model_name}"""       )


if __name__ == "__main__":
    snake_case_ : Any    		=       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--model_name",
        default="swin-base-simmim-window6-192",
        type=str,
        choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
        help="Name of the Swin SimMIM model you'd like to convert.",
    )
    parser.add_argument(
        "--checkpoint_path",
        default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
        type=str,
        help="Path to the original PyTorch checkpoint (.pth file).",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
    )

    snake_case_ : int    		=       parser.parse_args()
    convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)













'''simple docstring'''

from typing import List

from .keymap import KEYMAP, get_character





def lowerCamelCase						(     __lowerCamelCase :    str    )						->Optional[int]:

    def decorator(__lowerCamelCase :    int    ):
        _SCREAMING_SNAKE_CASE									=			getattr(__lowerCamelCase					,       """handle_key"""					,       []    )
        handle += [key]
        setattr(__lowerCamelCase					,       """handle_key"""					,       __lowerCamelCase    )
        return func

    return decorator





def lowerCamelCase						(     *__lowerCamelCase :    List[str]    )						->Dict:

    def decorator(__lowerCamelCase :    int    ):
        _SCREAMING_SNAKE_CASE									=			getattr(__lowerCamelCase					,       """handle_key"""					,       []    )
        handle += keys
        setattr(__lowerCamelCase					,       """handle_key"""					,       __lowerCamelCase    )
        return func

    return decorator


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __new__(		cls ,		A ,		A ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			super().__new__(cls ,		A ,		A ,		A       )
        if not hasattr(A ,		"""key_handler"""       ):
            setattr(A ,		"""key_handler""" ,		{}       )
        setattr(A ,		"""handle_input""" ,		KeyHandler.handle_input       )

        for value in attrs.values():
            _SCREAMING_SNAKE_CASE									=			getattr(A ,		"""handle_key""" ,		[]       )
            for key in handled_keys:
                _SCREAMING_SNAKE_CASE									=			value
        return new_cls





    @staticmethod
    def 			snake_case_(		cls       )		->   str:
        _SCREAMING_SNAKE_CASE									=			get_character()
        if char != KEYMAP["undefined"]:
            _SCREAMING_SNAKE_CASE									=			ord(A       )
        _SCREAMING_SNAKE_CASE									=			cls.key_handler.get(A       )
        if handler:
            _SCREAMING_SNAKE_CASE									=			char
            return handler(cls       )
        else:
            return None





def lowerCamelCase						(     cls :    Any    )						->Dict:
    return KeyHandler(cls.__name__					,       cls.__bases__					,       cls.__dict__.copy()    )



