





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  __magic_name__  (				lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     SwinConfig()
							lowercase_	:   Any  				=     swin_name.split("""_"""				)

							lowercase_	:   List[Any]  				=     name_split[1]
							lowercase_	:   List[Any]  				=     int(name_split[4]				)
							lowercase_	:   Dict  				=     int(name_split[3][-1]				)

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

							if "in22k" in swin_name:
														lowercase_	:   Optional[int]  				=     21841
							else:
														lowercase_	:   Any  				=     1000
														lowercase_	:   Any  				=     """huggingface/label-files"""
														lowercase_	:   Optional[int]  				=     """imagenet-1k-id2label.json"""
														lowercase_	:   Optional[Any]  				=     json.load(open(hf_hub_download(lowercase				,       lowercase				,       repo_type="""dataset"""				)				,       """r"""				)				)
														lowercase_	:   List[str]  				=     {int(lowercase				): v for k, v in idalabel.items()}
														lowercase_	:   List[str]  				=     idalabel
														lowercase_	:   Optional[Any]  				=     {v: k for k, v in idalabel.items()}

							lowercase_	:   str  				=     img_size
							lowercase_	:   str  				=     num_classes
							lowercase_	:   Any  				=     embed_dim
							lowercase_	:   Union[str, Any]  				=     depths
							lowercase_	:   Tuple  				=     num_heads
							lowercase_	:   Any  				=     window_size

							return config





def  __magic_name__  (				lowercase				)				-> str:

							"""simple docstring"""



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

							if name == "norm.weight":
														lowercase_	:   Dict  				=     """layernorm.weight"""
							if name == "norm.bias":
														lowercase_	:   List[str]  				=     """layernorm.bias"""

							if "head" in name:
														lowercase_	:   Optional[Any]  				=     name.replace("""head"""				,       """classifier"""				)
							else:
														lowercase_	:   Optional[Any]  				=     """swin.""" + name

							return name





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							for key in orig_state_dict.copy().keys():
														lowercase_	:   str  				=     orig_state_dict.pop(lowercase				)

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

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

							return orig_state_dict





def  __magic_name__  (				lowercase				,       lowercase				)				-> Dict:

							"""simple docstring"""



							lowercase_	:   List[Any]  				=     timm.create_model(lowercase				,       pretrained=lowercase				)
							timm_model.eval()

							lowercase_	:   Dict  				=     get_swin_config(lowercase				)
							lowercase_	:   Optional[Any]  				=     SwinForImageClassification(lowercase				)
							model.eval()

							lowercase_	:   List[str]  				=     convert_state_dict(timm_model.state_dict()				,       lowercase				)
							model.load_state_dict(lowercase				)

							lowercase_	:   List[str]  				=     """http://images.cocodataset.org/val2017/000000039769.jpg"""

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

							lowercase_	:   List[str]  				=     timm_model(inputs["""pixel_values"""]				)
							lowercase_	:   List[Any]  				=     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_							=				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_							=				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()




