




"""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       (							lowercase_						)						->		Tuple:
 A__	=    SwinConfig()
 A__	=    swin_name.split("_"						)

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

 if model_size == "tiny":
  A__	=    96
  A__	=    (2, 2, 6, 2)
  A__	=    (3, 6, 12, 24)
 elif model_size == "small":
  A__	=    96
  A__	=    (2, 2, 18, 2)
  A__	=    (3, 6, 12, 24)
 elif model_size == "base":
  A__	=    1_28
  A__	=    (2, 2, 18, 2)
  A__	=    (4, 8, 16, 32)
 else:
  A__	=    1_92
  A__	=    (2, 2, 18, 2)
  A__	=    (6, 12, 24, 48)

 if "in22k" in swin_name:
  A__	=    2_18_41
 else:
  A__	=    10_00
  A__	=    "huggingface/label-files"
  A__	=    "imagenet-1k-id2label.json"
  A__	=    json.load(open(hf_hub_download(lowercase_      ,			lowercase_      ,			repo_type="dataset"						)      ,			"r"						)						)
  A__	=    {int(lowercase_						): v for k, v in idalabel.items()}
  A__	=    idalabel
  A__	=    {v: k for k, v in idalabel.items()}

 A__	=    img_size
 A__	=    num_classes
 A__	=    embed_dim
 A__	=    depths
 A__	=    num_heads
 A__	=    window_size

 return config







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		List[Any]:
 if "patch_embed.proj" in name:
  A__	=    name.replace("patch_embed.proj"      ,			"embeddings.patch_embeddings.projection"						)
 if "patch_embed.norm" in name:
  A__	=    name.replace("patch_embed.norm"      ,			"embeddings.norm"						)
 if "layers" in name:
  A__	=    "encoder." + name
 if "attn.proj" in name:
  A__	=    name.replace("attn.proj"      ,			"attention.output.dense"						)
 if "attn" in name:
  A__	=    name.replace("attn"      ,			"attention.self"						)
 if "norm1" in name:
  A__	=    name.replace("norm1"      ,			"layernorm_before"						)
 if "norm2" in name:
  A__	=    name.replace("norm2"      ,			"layernorm_after"						)
 if "mlp.fc1" in name:
  A__	=    name.replace("mlp.fc1"      ,			"intermediate.dense"						)
 if "mlp.fc2" in name:
  A__	=    name.replace("mlp.fc2"      ,			"output.dense"						)

 if name == "norm.weight":
  A__	=    "layernorm.weight"
 if name == "norm.bias":
  A__	=    "layernorm.bias"

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

 return name







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		List[str]:
 for key in orig_state_dict.copy().keys():
  A__	=    orig_state_dict.pop(lowercase_						)

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

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

 return orig_state_dict







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Any:
 A__	=    timm.create_model(lowercase_      ,			pretrained=lowercase_						)
 timm_model.eval()

 A__	=    get_swin_config(lowercase_						)
 A__	=    SwinForImageClassification(lowercase_						)
 model.eval()

 A__	=    convert_state_dict(timm_model.state_dict()      ,			lowercase_						)
 model.load_state_dict(lowercase_						)

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

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

 A__	=    timm_model(inputs["pixel_values"]						)
 A__	=    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__":
      SCREAMING_SNAKE_CASE    =	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."
      )

      SCREAMING_SNAKE_CASE    =	parser.parse_args()
      convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)











"""simple docstring"""



import baseaa







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		bytes:
 return baseaa.aaaencode(string.encode("utf-8"						)						)







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		str:
 return baseaa.aaadecode(lowercase_						).decode("utf-8"						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





