


'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			Dict:
       _a    : Dict     					=SwinConfig(image_size=192  )

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

       _a    : Optional[int]     					=window_size
       _a    : Union[str, Any]     					=embed_dim
       _a    : Dict     					=depths
       _a    : str     					=num_heads

       return config


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

       if name == "encoder.norm.weight":
              _a    : Any     					="""layernorm.weight"""
       if name == "encoder.norm.bias":
              _a    : Optional[Any]     					="""layernorm.bias"""

       if "decoder" in name:
              pass
       else:
              _a    : List[Any]     					="""swin.""" + name

       return name


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int    ,_UpperCAmelCase	:	Union[str, Any]  )  ->			Dict:
       for key in orig_state_dict.copy().keys():
              _a    : Tuple     					=orig_state_dict.pop(_UpperCAmelCase  )

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

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

       return orig_state_dict


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	Union[str, Any]    ,_UpperCAmelCase	:	int    ,_UpperCAmelCase	:	Dict  )  ->			List[Any]:
       _a    : List[Any]     					=torch.load(_UpperCAmelCase    ,map_location="""cpu"""  )["""model"""]

       _a    : Any     					=get_swin_config(_UpperCAmelCase  )
       _a    : Dict     					=SwinForMaskedImageModeling(_UpperCAmelCase  )
       model.eval()

       _a    : Union[str, Any]     					=convert_state_dict(_UpperCAmelCase    ,_UpperCAmelCase  )
       model.load_state_dict(_UpperCAmelCase  )

       _a    : Optional[int]     					="""http://images.cocodataset.org/val2017/000000039769.jpg"""

       _a    : Any     					=ViTImageProcessor(size={"""height""": 192, """width""": 192}  )
       _a    : Any     					=Image.open(requests.get(_UpperCAmelCase    ,stream=_UpperCAmelCase  ).raw  )
       _a    : Optional[Any]     					=image_processor(images=_UpperCAmelCase    ,return_tensors="""pt"""  )

       with torch.no_grad():
              _a    : Optional[int]     					=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__":
   A__:    Dict					    =       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.'''
   )

   A__:    Optional[int]					    =       parser.parse_args()
   convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''







class A__       :






      def __init__( self			:List[str]	)       ->					List[Any]:




             '''simple docstring'''
             _a    : Tuple     					=0
             _a    : Any     					=0
             _a    : int     					={}






      def __UpperCAmelCase       ( self			:Any     ,						SCREAMING_SNAKE_CASE			:List[str]	)       ->					Optional[int]:




             '''simple docstring'''
             if vertex not in self.adjacency:
                    _a    : Dict     					={}
                    self.num_vertices += 1






      def __UpperCAmelCase       ( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:Any	)       ->					List[str]:




             '''simple docstring'''
             self.add_vertex(SCREAMING_SNAKE_CASE	)
             self.add_vertex(SCREAMING_SNAKE_CASE	)

             if head == tail:
                    return

             _a    : Any     					=weight
             _a    : Tuple     					=weight






      def __UpperCAmelCase       ( self			:Dict	)       ->					Optional[int]:




             '''simple docstring'''
             _a    : Union[str, Any]     					=self.get_edges()
             for edge in edges:
                    _a					,    _a					,    _a    : List[str]     					=edge
                    edges.remove((tail, head, weight)	)
             for i in range(len(SCREAMING_SNAKE_CASE	)	):
                    _a    : str     					=list(edges[i]	)

             edges.sort(key=lambda SCREAMING_SNAKE_CASE	: e[2]	)
             for i in range(len(SCREAMING_SNAKE_CASE	) - 1	):
                    if edges[i][2] >= edges[i + 1][2]:
                           _a    : Union[str, Any]     					=edges[i][2] + 1
             for edge in edges:
                    _a					,    _a					,    _a    : Tuple     					=edge
                    _a    : Tuple     					=weight
                    _a    : List[Any]     					=weight






      def __str__( self			:int	)       ->					str:




             '''simple docstring'''
             _a    : int     					=""""""
             for tail in self.adjacency:
                    for head in self.adjacency[tail]:
                           _a    : str     					=self.adjacency[head][tail]
                           string += f"{head} -> {tail} == {weight}\n"
             return string.rstrip("""\n"""	)






      def __UpperCAmelCase       ( self			:Optional[int]	)       ->					Optional[Any]:




             '''simple docstring'''
             _a    : Union[str, Any]     					=[]
             for tail in self.adjacency:
                    for head in self.adjacency[tail]:
                           output.append((tail, head, self.adjacency[head][tail])	)
             return output






      def __UpperCAmelCase       ( self			:List[Any]	)       ->					List[Any]:




             '''simple docstring'''
             return self.adjacency.keys()






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:Dict=None     ,						SCREAMING_SNAKE_CASE			:List[Any]=None	)       ->					Optional[int]:




             '''simple docstring'''
             _a    : str     					=Graph()
             if vertices is None:
                    _a    : Union[str, Any]     					=[]
             if edges is None:
                    _a    : List[Any]     					=[]
             for vertex in vertices:
                    g.add_vertex(SCREAMING_SNAKE_CASE	)
             for edge in edges:
                    g.add_edge(*SCREAMING_SNAKE_CASE	)
             return g







      class A__       :






            def __init__( self			:Optional[int]	)       ->					Union[str, Any]:




                   '''simple docstring'''
                   _a    : Optional[int]     					={}
                   _a    : List[str]     					={}






            def __len__( self			:List[Any]	)       ->					List[Any]:




                   '''simple docstring'''
                   return len(self.parent	)






            def __UpperCAmelCase       ( self			:Tuple     ,						SCREAMING_SNAKE_CASE			:Tuple	)       ->					Dict:




                   '''simple docstring'''
                   if item in self.parent:
                          return self.find(SCREAMING_SNAKE_CASE	)

                   _a    : Optional[Any]     					=item
                   _a    : List[str]     					=0
                   return item






            def __UpperCAmelCase       ( self			:int     ,						SCREAMING_SNAKE_CASE			:Dict	)       ->					List[str]:




                   '''simple docstring'''
                   if item not in self.parent:
                          return self.make_set(SCREAMING_SNAKE_CASE	)
                   if item != self.parent[item]:
                          _a    : str     					=self.find(self.parent[item]	)
                   return self.parent[item]






            def __UpperCAmelCase       ( self			:Optional[int]     ,						SCREAMING_SNAKE_CASE			:Tuple     ,						SCREAMING_SNAKE_CASE			:List[Any]	)       ->					Optional[Any]:




                   '''simple docstring'''
                   _a    : Optional[int]     					=self.find(SCREAMING_SNAKE_CASE	)
                   _a    : Dict     					=self.find(SCREAMING_SNAKE_CASE	)

                   if roota == roota:
                          return roota

                   if self.rank[roota] > self.rank[roota]:
                          _a    : Any     					=roota
                          return roota

                   if self.rank[roota] < self.rank[roota]:
                          _a    : List[str]     					=roota
                          return roota

                   if self.rank[roota] == self.rank[roota]:
                          self.rank[roota] += 1
                          _a    : List[Any]     					=roota
                          return roota
                   return None






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:Dict	)       ->					Union[str, Any]:




             '''simple docstring'''
             _a    : Any     					=graph.num_vertices

             _a    : Union[str, Any]     					=Graph.UnionFind()
             _a    : Optional[int]     					=[]
             while num_components > 1:
                    _a    : str     					={}
                    for vertex in graph.get_vertices():
                           _a    : List[str]     					=-1

                    _a    : Any     					=graph.get_edges()
                    for edge in edges:
                           _a					,    _a					,    _a    : Tuple     					=edge
                           edges.remove((tail, head, weight)	)
                    for edge in edges:
                           _a					,    _a					,    _a    : Any     					=edge
                           _a    : Any     					=union_find.find(SCREAMING_SNAKE_CASE	)
                           _a    : List[Any]     					=union_find.find(SCREAMING_SNAKE_CASE	)
                           if seta != seta:
                                  if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                                         _a    : Optional[int]     					=[head, tail, weight]

                                  if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                                         _a    : List[Any]     					=[head, tail, weight]
                    for vertex in cheap_edge:
                           if cheap_edge[vertex] != -1:
                                  _a					,    _a					,    _a    : Optional[Any]     					=cheap_edge[vertex]
                                  if union_find.find(SCREAMING_SNAKE_CASE	) != union_find.find(SCREAMING_SNAKE_CASE	):
                                         union_find.union(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                                         mst_edges.append(cheap_edge[vertex]	)
                                         _a    : str     					=num_components - 1
             _a    : str     					=Graph.build(edges=SCREAMING_SNAKE_CASE	)
             return mst


