





"""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 					__lowerCamelCase       (							a_					:		List[str]							)      ->				int:
      __SCREAMING_SNAKE_CASE       :List[str]		    =  SwinConfig()
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  swin_name.split('''_'''							)

      __SCREAMING_SNAKE_CASE       :Any		    =  name_split[1]
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  int(name_split[4]							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  int(name_split[3][-1]							)

      if model_size == "tiny":
            __SCREAMING_SNAKE_CASE       :List[str]		    =  96
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  (2, 2, 6, 2)
            __SCREAMING_SNAKE_CASE       :Tuple		    =  (3, 6, 12, 24)
      elif model_size == "small":
            __SCREAMING_SNAKE_CASE       :List[str]		    =  96
            __SCREAMING_SNAKE_CASE       :Any		    =  (2, 2, 18, 2)
            __SCREAMING_SNAKE_CASE       :Tuple		    =  (3, 6, 12, 24)
      elif model_size == "base":
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  1_28
            __SCREAMING_SNAKE_CASE       :Tuple		    =  (2, 2, 18, 2)
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  (4, 8, 16, 32)
      else:
            __SCREAMING_SNAKE_CASE       :int		    =  1_92
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  (2, 2, 18, 2)
            __SCREAMING_SNAKE_CASE       :str		    =  (6, 12, 24, 48)

      if "in22k" in swin_name:
            __SCREAMING_SNAKE_CASE       :Tuple		    =  2_18_41
      else:
            __SCREAMING_SNAKE_CASE       :List[str]		    =  10_00
            __SCREAMING_SNAKE_CASE       :str		    =  '''huggingface/label-files'''
            __SCREAMING_SNAKE_CASE       :List[str]		    =  '''imagenet-1k-id2label.json'''
            __SCREAMING_SNAKE_CASE       :Tuple		    =  json.load(open(hf_hub_download(a_			,       a_			,       repo_type='''dataset'''							)			,       '''r'''							)							)
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  {int(a_							): v for k, v in idalabel.items()}
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  idalabel
            __SCREAMING_SNAKE_CASE       :Dict		    =  {v: k for k, v in idalabel.items()}

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  img_size
      __SCREAMING_SNAKE_CASE       :Tuple		    =  num_classes
      __SCREAMING_SNAKE_CASE       :List[str]		    =  embed_dim
      __SCREAMING_SNAKE_CASE       :int		    =  depths
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  num_heads
      __SCREAMING_SNAKE_CASE       :str		    =  window_size

      return config



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

      if name == "norm.weight":
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  '''layernorm.weight'''
      if name == "norm.bias":
            __SCREAMING_SNAKE_CASE       :int		    =  '''layernorm.bias'''

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

      return name



def 					__lowerCamelCase       (							a_					:		Dict			,       a_					:		int							)      ->				Tuple:
      for key in orig_state_dict.copy().keys():
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  orig_state_dict.pop(a_							)

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

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

      return orig_state_dict



def 					__lowerCamelCase       (							a_					:		List[str]			,       a_					:		Any							)      ->				List[Any]:
      __SCREAMING_SNAKE_CASE       :Tuple		    =  timm.create_model(a_			,       pretrained=a_							)
      timm_model.eval()

      __SCREAMING_SNAKE_CASE       :int		    =  get_swin_config(a_							)
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  SwinForImageClassification(a_							)
      model.eval()

      __SCREAMING_SNAKE_CASE       :Any		    =  convert_state_dict(timm_model.state_dict()			,       a_							)
      model.load_state_dict(a_							)

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

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

      __SCREAMING_SNAKE_CASE       :str		    =  timm_model(inputs['''pixel_values''']							)
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  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_       =		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_       =		parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		int = 10			,       a_					:		int = 22							)      ->				int:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  range(1			,       a_							)
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  range(1			,       a_							)
      return sum(
          1 for power in powers for base in bases if len(str(base**power							)							) == power							)


if __name__ == "__main__":
  print(f'{solution(1_0, 2_2) = }')