


'''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 _SCREAMING_SNAKE_CASE (A			)			->			str:






       """simple docstring"""







       lowercase__      =					SwinConfig()
       lowercase__      =					swin_name.split('''_'''			)

       lowercase__      =					name_split[1]
       lowercase__      =					int(name_split[4]			)
       lowercase__      =					int(name_split[3][-1]			)

       if model_size == "tiny":
              lowercase__      =					96
              lowercase__      =					(2, 2, 6, 2)
              lowercase__      =					(3, 6, 12, 24)
       elif model_size == "small":
              lowercase__      =					96
              lowercase__      =					(2, 2, 18, 2)
              lowercase__      =					(3, 6, 12, 24)
       elif model_size == "base":
              lowercase__      =					128
              lowercase__      =					(2, 2, 18, 2)
              lowercase__      =					(4, 8, 16, 32)
       else:
              lowercase__      =					192
              lowercase__      =					(2, 2, 18, 2)
              lowercase__      =					(6, 12, 24, 48)

       if "in22k" in swin_name:
              lowercase__      =					21_841
       else:
              lowercase__      =					1_000
              lowercase__      =					'''huggingface/label-files'''
              lowercase__      =					'''imagenet-1k-id2label.json'''
              lowercase__      =					json.load(open(hf_hub_download(A    ,					A    ,					repo_type='''dataset'''			)    ,					'''r'''			)			)
              lowercase__      =					{int(A			): v for k, v in idalabel.items()}
              lowercase__      =					idalabel
              lowercase__      =					{v: k for k, v in idalabel.items()}

       lowercase__      =					img_size
       lowercase__      =					num_classes
       lowercase__      =					embed_dim
       lowercase__      =					depths
       lowercase__      =					num_heads
       lowercase__      =					window_size

       return config

def _SCREAMING_SNAKE_CASE (A			)			->			str:






       """simple docstring"""







       if "patch_embed.proj" in name:
              lowercase__      =					name.replace('''patch_embed.proj'''    ,					'''embeddings.patch_embeddings.projection'''			)
       if "patch_embed.norm" in name:
              lowercase__      =					name.replace('''patch_embed.norm'''    ,					'''embeddings.norm'''			)
       if "layers" in name:
              lowercase__      =					'''encoder.''' + name
       if "attn.proj" in name:
              lowercase__      =					name.replace('''attn.proj'''    ,					'''attention.output.dense'''			)
       if "attn" in name:
              lowercase__      =					name.replace('''attn'''    ,					'''attention.self'''			)
       if "norm1" in name:
              lowercase__      =					name.replace('''norm1'''    ,					'''layernorm_before'''			)
       if "norm2" in name:
              lowercase__      =					name.replace('''norm2'''    ,					'''layernorm_after'''			)
       if "mlp.fc1" in name:
              lowercase__      =					name.replace('''mlp.fc1'''    ,					'''intermediate.dense'''			)
       if "mlp.fc2" in name:
              lowercase__      =					name.replace('''mlp.fc2'''    ,					'''output.dense'''			)

       if name == "norm.weight":
              lowercase__      =					'''layernorm.weight'''
       if name == "norm.bias":
              lowercase__      =					'''layernorm.bias'''

       if "head" in name:
              lowercase__      =					name.replace('''head'''    ,					'''classifier'''			)
       else:
              lowercase__      =					'''swin.''' + name

       return name

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			str:






       """simple docstring"""







       for key in orig_state_dict.copy().keys():
              lowercase__      =					orig_state_dict.pop(A			)

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

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

       return orig_state_dict

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			Dict:






       """simple docstring"""







       lowercase__      =					timm.create_model(A    ,					pretrained=A			)
       timm_model.eval()

       lowercase__      =					get_swin_config(A			)
       lowercase__      =					SwinForImageClassification(A			)
       model.eval()

       lowercase__      =					convert_state_dict(timm_model.state_dict()    ,					A			)
       model.load_state_dict(A			)

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

       lowercase__      =					AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''    ,					'''-'''			)			)			)
       lowercase__      =					Image.open(requests.get(A    ,					stream=A			).raw			)
       lowercase__      =					image_processor(images=A    ,					return_tensors='''pt'''			)

       lowercase__      =					timm_model(inputs['''pixel_values''']			)
       lowercase__      =					model(**A			).logits

       assert torch.allclose(A    ,					A    ,					atol=1E-3			)

       print(f"Saving model {swin_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 __name__ == "__main__":
 lowerCamelCase				:  Optional[int]    		=      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.'
 )

 lowerCamelCase				:  int    		=      parser.parse_args()
 convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)








'''simple docstring'''

from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class     __lowerCAmelCase		:





      '''simple docstring'''






      lowerCAmelCase__   :	int
      lowerCAmelCase__   :	TreeNode | None       =				None
      lowerCAmelCase__   :	TreeNode | None       =				None






lowerCamelCase				:  Optional[Any]    		=      namedtuple('CoinsDistribResult', 'moves excess')

def _SCREAMING_SNAKE_CASE (A			)			->			int:






       """simple docstring"""







       if root is None:
              return 0

       # Validation
       def count_nodes(A			) -> int:
              if node is None:
                     return 0

              return count_nodes(node.left			) + count_nodes(node.right			) + 1

       def count_coins(A			) -> int:
              if node is None:
                     return 0

              return count_coins(node.left			) + count_coins(node.right			) + node.data

       if count_nodes(A			) != count_coins(A			):
              raise ValueError('''The nodes number should be same as the number of coins'''			)

       # Main calculation
       def get_distrib(A			) -> CoinsDistribResult:

              if node is None:
                     return CoinsDistribResult(0    ,					1			)

              lowercase__     ,lowercase__      =					get_distrib(node.left			)
              lowercase__     ,lowercase__      =					get_distrib(node.right			)

              lowercase__      =					1 - left_distrib_excess
              lowercase__      =					1 - right_distrib_excess

              lowercase__      =					(
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(A			)
                  + abs(A			)
              )
              lowercase__      =					node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(A    ,					A			)

       return get_distrib(A			)[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




