





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[int]      )      ->	List[Any]:
    UpperCamelCase__							:				Any          =      SwinConfig(image_size=192      )

    if "base" in model_name:
        UpperCamelCase__							:				Optional[int]          =      6
        UpperCamelCase__							:				Optional[Any]          =      128
        UpperCamelCase__							:				List[str]          =      (2, 2, 18, 2)
        UpperCamelCase__							:				str          =      (4, 8, 16, 32)
    elif "large" in model_name:
        UpperCamelCase__							:				Dict          =      12
        UpperCamelCase__							:				int          =      192
        UpperCamelCase__							:				Any          =      (2, 2, 18, 2)
        UpperCamelCase__							:				Optional[int]          =      (6, 12, 24, 48)
    else:
        raise ValueError('''Model not supported, only supports base and large variants'''      )

    UpperCamelCase__							:				List[Any]          =      window_size
    UpperCamelCase__							:				Dict          =      embed_dim
    UpperCamelCase__							:				Tuple          =      depths
    UpperCamelCase__							:				Any          =      num_heads

    return config




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Any      )      ->	Dict:
    if "encoder.mask_token" in name:
        UpperCamelCase__							:				List[Any]          =      name.replace('''encoder.mask_token'''		,   '''embeddings.mask_token'''      )
    if "encoder.patch_embed.proj" in name:
        UpperCamelCase__							:				Union[str, Any]          =      name.replace('''encoder.patch_embed.proj'''		,   '''embeddings.patch_embeddings.projection'''      )
    if "encoder.patch_embed.norm" in name:
        UpperCamelCase__							:				Dict          =      name.replace('''encoder.patch_embed.norm'''		,   '''embeddings.norm'''      )
    if "attn.proj" in name:
        UpperCamelCase__							:				Dict          =      name.replace('''attn.proj'''		,   '''attention.output.dense'''      )
    if "attn" in name:
        UpperCamelCase__							:				Tuple          =      name.replace('''attn'''		,   '''attention.self'''      )
    if "norm1" in name:
        UpperCamelCase__							:				Any          =      name.replace('''norm1'''		,   '''layernorm_before'''      )
    if "norm2" in name:
        UpperCamelCase__							:				Tuple          =      name.replace('''norm2'''		,   '''layernorm_after'''      )
    if "mlp.fc1" in name:
        UpperCamelCase__							:				List[str]          =      name.replace('''mlp.fc1'''		,   '''intermediate.dense'''      )
    if "mlp.fc2" in name:
        UpperCamelCase__							:				Any          =      name.replace('''mlp.fc2'''		,   '''output.dense'''      )

    if name == "encoder.norm.weight":
        UpperCamelCase__							:				Union[str, Any]          =      '''layernorm.weight'''
    if name == "encoder.norm.bias":
        UpperCamelCase__							:				Dict          =      '''layernorm.bias'''

    if "decoder" in name:
        pass
    else:
        UpperCamelCase__							:				List[str]          =      '''swin.''' + name

    return name




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Any      )      ->	str:
    for key in orig_state_dict.copy().keys():
        UpperCamelCase__							:				List[str]          =      orig_state_dict.pop(__UpperCAmelCase      )

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

            if "weight" in key:
                UpperCamelCase__							:				Optional[int]          =      val[:dim, :]
                UpperCamelCase__							:				List[str]          =      val[
                    dim : dim * 2, :
                ]
                UpperCamelCase__							:				Tuple          =      val[-dim:, :]
            else:
                UpperCamelCase__							:				Union[str, Any]          =      val[
                    :dim
                ]
                UpperCamelCase__							:				int          =      val[
                    dim : dim * 2
                ]
                UpperCamelCase__							:				str          =      val[
                    -dim:
                ]
        else:
            UpperCamelCase__							:				Optional[int]          =      val

    return orig_state_dict




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Dict		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			Dict      )      ->	Union[str, Any]:
    UpperCamelCase__							:				Optional[Any]          =      torch.load(__UpperCAmelCase		,   map_location='''cpu'''      )['''model''']

    UpperCamelCase__							:				Optional[Any]          =      get_swin_config(__UpperCAmelCase      )
    UpperCamelCase__							:				Union[str, Any]          =      SwinForMaskedImageModeling(__UpperCAmelCase      )
    model.eval()

    UpperCamelCase__							:				Optional[Any]          =      convert_state_dict(__UpperCAmelCase		,   __UpperCAmelCase      )
    model.load_state_dict(__UpperCAmelCase      )

    UpperCamelCase__							:				List[Any]          =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

    UpperCamelCase__							:				Dict          =      ViTImageProcessor(size={'''height''': 192, '''width''': 192}      )
    UpperCamelCase__							:				Optional[Any]          =      Image.open(requests.get(__UpperCAmelCase		,   stream=__UpperCAmelCase      ).raw      )
    UpperCamelCase__							:				List[Any]          =      image_processor(images=__UpperCAmelCase		,   return_tensors='''pt'''      )

    with torch.no_grad():
        UpperCamelCase__							:				Optional[Any]          =      model(**__UpperCAmelCase      ).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(__UpperCAmelCase      )

        print(f"Saving image processor to {pytorch_dump_folder_path}"      )
        image_processor.save_pretrained(__UpperCAmelCase      )

    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__":
  UpperCAmelCase_     							= 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.'
  )

  UpperCAmelCase_     							= parser.parse_args()
  convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''




class 		UpperCAmelCase_      :





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      list[int]    )				->  None:
        lowerCAmelCase     =			len(UpperCAmelCase__    )
        lowerCAmelCase     =			[0] * len_array

        if len_array > 0:
           lowerCAmelCase     =			array[0]

        for i in range(1    ,      UpperCAmelCase__    ):
           lowerCAmelCase     =			self.prefix_sum[i - 1] + array[i]





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  int:
        if start == 0:
           return self.prefix_sum[end]

        return self.prefix_sum[end] - self.prefix_sum[start - 1]





     def 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      int    )				->  bool:
        lowerCAmelCase     =			{0}
        for sum_item in self.prefix_sum:
           if sum_item - target_sum in sums:
              return True

           sums.add(UpperCAmelCase__    )

        return False


if __name__ == "__main__":
       import doctest

       doctest.testmod()





