






'''simple docstring'''

import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def     __lowercase  (_lowercase     )   ->     Any:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				SwinConfig()
     __lowerCamelCase						:       Any				   =				swin_name.split("""_"""     )

     __lowerCamelCase						:       List[Any]				   =				name_split[1]
     __lowerCamelCase						:       str				   =				int(name_split[4]     )
     __lowerCamelCase						:       Tuple				   =				int(name_split[3][-1]     )

     if model_size == "tiny":
          __lowerCamelCase						:       str				   =				96
          __lowerCamelCase						:       List[Any]				   =				(2, 2, 6, 2)
          __lowerCamelCase						:       List[str]				   =				(3, 6, 12, 24)
     elif model_size == "small":
          __lowerCamelCase						:       Tuple				   =				96
          __lowerCamelCase						:       Union[str, Any]				   =				(2, 2, 18, 2)
          __lowerCamelCase						:       List[Any]				   =				(3, 6, 12, 24)
     elif model_size == "base":
          __lowerCamelCase						:       Any				   =				128
          __lowerCamelCase						:       Any				   =				(2, 2, 18, 2)
          __lowerCamelCase						:       Any				   =				(4, 8, 16, 32)
     else:
          __lowerCamelCase						:       Union[str, Any]				   =				192
          __lowerCamelCase						:       Dict				   =				(2, 2, 18, 2)
          __lowerCamelCase						:       List[Any]				   =				(6, 12, 24, 48)

     if "in22k" in swin_name:
          __lowerCamelCase						:       List[str]				   =				21_841
     else:
          __lowerCamelCase						:       List[Any]				   =				1_000
          __lowerCamelCase						:       Tuple				   =				"""huggingface/label-files"""
          __lowerCamelCase						:       Tuple				   =				"""imagenet-1k-id2label.json"""
          __lowerCamelCase						:       Union[str, Any]				   =				json.load(open(hf_hub_download(_lowercase,						_lowercase,						repo_type="""dataset"""     ),						"""r"""     )     )
          __lowerCamelCase						:       int				   =				{int(_lowercase     ): v for k, v in idalabel.items()}
          __lowerCamelCase						:       Any				   =				idalabel
          __lowerCamelCase						:       Optional[int]				   =				{v: k for k, v in idalabel.items()}

     __lowerCamelCase						:       Any				   =				img_size
     __lowerCamelCase						:       Any				   =				num_classes
     __lowerCamelCase						:       List[str]				   =				embed_dim
     __lowerCamelCase						:       List[Any]				   =				depths
     __lowerCamelCase						:       Dict				   =				num_heads
     __lowerCamelCase						:       Optional[int]				   =				window_size

     return config
def     __lowercase  (_lowercase     )   ->     Any:


     """simple docstring"""
     if "patch_embed.proj" in name:
          __lowerCamelCase						:       Optional[Any]				   =				name.replace("""patch_embed.proj""",						"""embeddings.patch_embeddings.projection"""     )
     if "patch_embed.norm" in name:
          __lowerCamelCase						:       Dict				   =				name.replace("""patch_embed.norm""",						"""embeddings.norm"""     )
     if "layers" in name:
          __lowerCamelCase						:       Tuple				   =				"""encoder.""" + name
     if "attn.proj" in name:
          __lowerCamelCase						:       str				   =				name.replace("""attn.proj""",						"""attention.output.dense"""     )
     if "attn" in name:
          __lowerCamelCase						:       Optional[int]				   =				name.replace("""attn""",						"""attention.self"""     )
     if "norm1" in name:
          __lowerCamelCase						:       Tuple				   =				name.replace("""norm1""",						"""layernorm_before"""     )
     if "norm2" in name:
          __lowerCamelCase						:       Optional[Any]				   =				name.replace("""norm2""",						"""layernorm_after"""     )
     if "mlp.fc1" in name:
          __lowerCamelCase						:       Union[str, Any]				   =				name.replace("""mlp.fc1""",						"""intermediate.dense"""     )
     if "mlp.fc2" in name:
          __lowerCamelCase						:       Optional[int]				   =				name.replace("""mlp.fc2""",						"""output.dense"""     )

     if name == "norm.weight":
          __lowerCamelCase						:       Any				   =				"""layernorm.weight"""
     if name == "norm.bias":
          __lowerCamelCase						:       Any				   =				"""layernorm.bias"""

     if "head" in name:
          __lowerCamelCase						:       str				   =				name.replace("""head""",						"""classifier"""     )
     else:
          __lowerCamelCase						:       List[Any]				   =				"""swin.""" + name

     return name
def     __lowercase  (_lowercase,						_lowercase     )   ->     List[str]:


     """simple docstring"""
     for key in orig_state_dict.copy().keys():
          __lowerCamelCase						:       List[Any]				   =				orig_state_dict.pop(_lowercase     )

          if "mask" in key:
               continue
          elif "qkv" in key:
               __lowerCamelCase						:       Optional[int]				   =				key.split("""."""     )
               __lowerCamelCase						:       Any				   =				int(key_split[1]     )
               __lowerCamelCase						:       int				   =				int(key_split[3]     )
               __lowerCamelCase						:       Optional[Any]				   =				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

     return orig_state_dict
def     __lowercase  (_lowercase,						_lowercase     )   ->     List[Any]:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				timm.create_model(_lowercase,						pretrained=_lowercase     )
     timm_model.eval()

     __lowerCamelCase						:       int				   =				get_swin_config(_lowercase     )
     __lowerCamelCase						:       List[Any]				   =				SwinForImageClassification(_lowercase     )
     model.eval()

     __lowerCamelCase						:       Optional[int]				   =				convert_state_dict(timm_model.state_dict(),						_lowercase     )
     model.load_state_dict(_lowercase     )

     __lowerCamelCase						:       int				   =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""

     __lowerCamelCase						:       Tuple				   =				AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""",						"""-"""     )     )     )
     __lowerCamelCase						:       Optional[Any]				   =				Image.open(requests.get(_lowercase,						stream=_lowercase     ).raw     )
     __lowerCamelCase						:       List[Any]				   =				image_processor(images=_lowercase,						return_tensors="""pt"""     )

     __lowerCamelCase						:       int				   =				timm_model(inputs["""pixel_values"""]     )
     __lowerCamelCase						:       Dict				   =				model(**_lowercase     ).logits

     assert torch.allclose(_lowercase,						_lowercase,						atol=1e-3     )

     print(f"Saving model {swin_name} to {pytorch_dump_folder_path}"     )
     model.save_pretrained(_lowercase     )

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


if __name__ == "__main__":
   UpperCAmelCase__						:List[str]          =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--swin_name""",
       default="""swin_tiny_patch4_window7_224""",
       type=str,
       help="""Name of the Swin timm model you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )

   UpperCAmelCase__						:List[Any]          =			parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)











'''simple docstring'''

def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     Union[str, Any]:


     """simple docstring"""
     # Return True if there is node that has not iterated.
     __lowerCamelCase						:       Optional[Any]				   =				[False] * len(_lowercase     )
     __lowerCamelCase						:       Dict				   =				[]
     queue.append(_lowercase     )
     __lowerCamelCase						:       Optional[int]				   =				True

     while queue:
          __lowerCamelCase						:       Optional[Any]				   =				queue.pop(0     )
          for ind in range(len(graph[u]     )     ):
               if visited[ind] is False and graph[u][ind] > 0:
                    queue.append(_lowercase     )
                    __lowerCamelCase						:       List[str]				   =				True
                    __lowerCamelCase						:       str				   =				u

     return visited[t]
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     # This array is filled by BFS and to store path
     __lowerCamelCase						:       Any				   =				[-1] * (len(_lowercase     ))
     __lowerCamelCase						:       Optional[int]				   =				0
     while bfs(_lowercase,						_lowercase,						_lowercase,						_lowercase     ):
          __lowerCamelCase						:       List[Any]				   =				float("""Inf"""     )
          __lowerCamelCase						:       Union[str, Any]				   =				sink

          while s != source:
               # Find the minimum value in select path
               __lowerCamelCase						:       Optional[int]				   =				min(_lowercase,						graph[parent[s]][s]     )
               __lowerCamelCase						:       Union[str, Any]				   =				parent[s]

          max_flow += path_flow
          __lowerCamelCase						:       str				   =				sink

          while v != source:
               __lowerCamelCase						:       List[Any]				   =				parent[v]
               graph[u][v] -= path_flow
               graph[v][u] += path_flow
               __lowerCamelCase						:       Dict				   =				parent[v]
     return max_flow


UpperCAmelCase__						:str          =			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

UpperCAmelCase__       ,				UpperCAmelCase__						:Optional[Any]          =			0, 5
print(ford_fulkerson(graph, source, sink))



