




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  List[Any]		       =     SwinConfig(image_size=192      )

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

  lowerCAmelCase__				:  List[str]		       =     window_size
  lowerCAmelCase__				:  Union[str, Any]		       =     embed_dim
  lowerCAmelCase__				:  Union[str, Any]		       =     depths
  lowerCAmelCase__				:  List[str]		       =     num_heads

  return config



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

  if name == "encoder.norm.weight":
    lowerCAmelCase__				:  Dict		       =     '''layernorm.weight'''
  if name == "encoder.norm.bias":
    lowerCAmelCase__				:  int		       =     '''layernorm.bias'''

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

  return name



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	Dict:
  for key in orig_state_dict.copy().keys():
    lowerCAmelCase__				:  Optional[int]		       =     orig_state_dict.pop(UpperCamelCase      )

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

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

  return orig_state_dict



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Tuple:
  lowerCAmelCase__				:  Dict		       =     torch.load(UpperCamelCase	,      map_location='''cpu'''      )['''model''']

  lowerCAmelCase__				:  Union[str, Any]		       =     get_swin_config(UpperCamelCase      )
  lowerCAmelCase__				:  List[Any]		       =     SwinForMaskedImageModeling(UpperCamelCase      )
  model.eval()

  lowerCAmelCase__				:  List[str]		       =     convert_state_dict(UpperCamelCase	,      UpperCamelCase      )
  model.load_state_dict(UpperCamelCase      )

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

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

  with torch.no_grad():
    lowerCAmelCase__				:  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__":
     lowerCAmelCase_					    = 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."""
     )

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







from collections.abc import Callable






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase = None		):
        # Stores actual heap items.
        lowerCAmelCase__				:  list		       =     []
        # Stores indexes of each item for supporting updates and deletion.
        lowerCAmelCase__				:  dict		       =     {}
        # Stores current size of heap.
        lowerCAmelCase__				:  Any		       =     0
        # Stores function used to evaluate the score of an item on which basis ordering
        # will be done.
        lowerCAmelCase__				:  Union[str, Any]		       =     key or (lambda __UpperCAmelCase		: x)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        return int((i - 1) / 2		) if i > 0 else None

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     int(2 * i + 1		)
        return left if 0 < left < self.size else None

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[str]		       =     int(2 * i + 2		)
        return right if 0 < right < self.size else None

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  List[str]		       =     (
            self.pos_map[self.arr[j][0]],
            self.pos_map[self.arr[i][0]],
        )
        # Then swap the items in the list.
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Any		       =     self.arr[j], self.arr[i]

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        return self.arr[i][1] < self.arr[j][1]

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[int]		       =     self._left(__UpperCAmelCase		)
        lowerCAmelCase__				:  Any		       =     self._right(__UpperCAmelCase		)
        lowerCAmelCase__				:  str		       =     i

        if left is not None and not self._cmp(__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  Dict		       =     left
        if right is not None and not self._cmp(__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  List[str]		       =     right

        return valid_parent

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Tuple		       =     self._parent(__UpperCAmelCase		)
        while parent is not None and not self._cmp(__UpperCAmelCase      ,				__UpperCAmelCase		):
          self._swap(__UpperCAmelCase      ,				__UpperCAmelCase		)
          lowerCAmelCase__       ,			lowerCAmelCase__				:  Dict		       =     parent, self._parent(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  str		       =     self._get_valid_parent(__UpperCAmelCase		)
        while valid_parent != index:
          self._swap(__UpperCAmelCase      ,				__UpperCAmelCase		)
          lowerCAmelCase__       ,			lowerCAmelCase__				:  List[Any]		       =     valid_parent, self._get_valid_parent(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        if item not in self.pos_map:
          return
        lowerCAmelCase__				:  Tuple		       =     self.pos_map[item]
        lowerCAmelCase__				:  Any		       =     [item, self.key(__UpperCAmelCase		)]
        # Make sure heap is right in both up and down direction.
        # Ideally only one of them will make any change.
        self._heapify_up(__UpperCAmelCase		)
        self._heapify_down(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        if item not in self.pos_map:
          return
        lowerCAmelCase__				:  Tuple		       =     self.pos_map[item]
        del self.pos_map[item]
        lowerCAmelCase__				:  Tuple		       =     self.arr[self.size - 1]
        lowerCAmelCase__				:  int		       =     index
        self.size -= 1
        # Make sure heap is right in both up and down direction. Ideally only one
        # of them will make any change- so no performance loss in calling both.
        if self.size > index:
          self._heapify_up(__UpperCAmelCase		)
          self._heapify_down(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[int]		       =     len(self.arr		)
        if arr_len == self.size:
          self.arr.append([item, self.key(__UpperCAmelCase		)]		)
        else:
          lowerCAmelCase__				:  str		       =     [item, self.key(__UpperCAmelCase		)]
        lowerCAmelCase__				:  Optional[Any]		       =     self.size
        self.size += 1
        self._heapify_up(self.size - 1		)

      def 	__magic_name__(   self		):
        return self.arr[0] if self.size else None







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  int		       =     self.get_top()
        if top_item_tuple:
          self.delete_item(top_item_tuple[0]		)
        return top_item_tuple



def      __lowerCAmelCase			(					)     ->	None:
  pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()

