
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 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       lowercase							:					List[Any]	     =					SwinConfig()
       lowercase							:					Dict	     =					swin_name.split("""_"""    )

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

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

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

       lowercase							:					Dict	     =					img_size
       lowercase							:					Optional[Any]	     =					num_classes
       lowercase							:					Union[str, Any]	     =					embed_dim
       lowercase							:					Dict	     =					depths
       lowercase							:					Any	     =					num_heads
       lowercase							:					str	     =					window_size

       return config







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

       if name == "norm.weight":
              lowercase							:					List[Any]	     =					"""layernorm.weight"""
       if name == "norm.bias":
              lowercase							:					Optional[int]	     =					"""layernorm.bias"""

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

       return name







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[str]:
       for key in orig_state_dict.copy().keys():
              lowercase							:					Union[str, Any]	     =					orig_state_dict.pop(SCREAMING_SNAKE_CASE__    )

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

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

       return orig_state_dict







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   str:
       lowercase							:					Dict	     =					timm.create_model(SCREAMING_SNAKE_CASE__ ,     pretrained=SCREAMING_SNAKE_CASE__    )
       timm_model.eval()

       lowercase							:					List[Any]	     =					get_swin_config(SCREAMING_SNAKE_CASE__    )
       lowercase							:					str	     =					SwinForImageClassification(SCREAMING_SNAKE_CASE__    )
       model.eval()

       lowercase							:					int	     =					convert_state_dict(timm_model.state_dict() ,     SCREAMING_SNAKE_CASE__    )
       model.load_state_dict(SCREAMING_SNAKE_CASE__    )

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

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

       lowercase							:					int	     =					timm_model(inputs["""pixel_values"""]    )
       lowercase							:					List[Any]	     =					model(**SCREAMING_SNAKE_CASE__    ).logits

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

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

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


if __name__ == "__main__":
  lowercase  : Tuple						   =							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."""
  )

  lowercase  : Dict						   =							parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)


def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   int:
       assert (
           isinstance(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) and number_of_steps > 0
       ), f"number_of_steps needs to be positive integer, your input {number_of_steps}"
       if number_of_steps == 1:
              return 1
       lowercase     ,      lowercase							:					Tuple	     =				1, 1
       for _ in range(number_of_steps - 1    ):
              lowercase     ,      lowercase							:					str	     =				current + previous, current
       return current


if __name__ == "__main__":
  import doctest

  doctest.testmod()
