




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case            = logging.get_logger(__name__)

_snake_case            = torch.device("cpu")







def        lowerCAmelCase_			(  ):
      _A							:  List[str]    =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _A							:  Optional[Any]    =		Image.open(requests.get(snake_case_,stream=snake_case_  ).raw  )
      return im







def        lowerCAmelCase_			(  snake_case_  ):
      if swiftformer_name == "swiftformer_xs":
            return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01]  )

      elif swiftformer_name == "swiftformer_s":
            return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01]  )

      elif swiftformer_name == "swiftformer_l1":
            return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02]  )

      elif swiftformer_name == "swiftformer_l3":
            return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02]  )







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  Tuple    =		dct.pop(snake_case_  )
      _A							:  Optional[int]    =		val







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  int    =		[]
      for k in state_dict.keys():
            _A							:  Dict    =		k
            if ".pwconv" in k:
                  _A							:  Dict    =		k_new.replace(""".pwconv""",""".point_wise_conv"""  )
            if ".dwconv" in k:
                  _A							:  int    =		k_new.replace(""".dwconv""",""".depth_wise_conv"""  )
            if ".Proj." in k:
                  _A							:  Tuple    =		k_new.replace(""".Proj.""",""".proj."""  )
            if "patch_embed" in k_new:
                  _A							:  str    =		k_new.replace("""patch_embed""","""swiftformer.patch_embed.patch_embedding"""  )
            if "network" in k_new:
                  _A							:  Optional[int]    =		k_new.split("""."""  )
                  if ls[2].isdigit():
                        _A							:  Any    =		"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]  )
                  else:
                        _A							:  int    =		k_new.replace("""network""","""swiftformer.encoder.network"""  )
            rename_keys.append((k, k_new)  )
      return rename_keys







@torch.no_grad()
def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  List[Any]    =		SwiftFormerConfig()

      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      _A							:  Dict    =		1000
      _A							:  Optional[int]    =		"""huggingface/label-files"""
      _A							:  Optional[Any]    =		"""imagenet-1k-id2label.json"""
      _A							:  Optional[int]    =		json.load(open(hf_hub_download(snake_case_,snake_case_,repo_type="""dataset"""  ),"""r"""  )  )
      _A							:  Any    =		{int(snake_case_  ): v for k, v in idalabel.items()}
      _A							:  Optional[Any]    =		idalabel
      _A							:  Optional[Any]    =		{v: k for k, v in idalabel.items()}

      # size of the architecture
      if swiftformer_name == "swiftformer_xs":
            _A							:  Dict    =		[3, 3, 6, 4]
            _A							:  Optional[Any]    =		[48, 56, 112, 220]

      elif swiftformer_name == "swiftformer_s":
            _A							:  List[str]    =		[3, 3, 9, 6]
            _A							:  List[str]    =		[48, 64, 168, 224]

      elif swiftformer_name == "swiftformer_l1":
            _A							:  Optional[Any]    =		[4, 3, 10, 5]
            _A							:  Dict    =		[48, 96, 192, 384]

      elif swiftformer_name == "swiftformer_l3":
            _A							:  List[str]    =		[4, 4, 12, 6]
            _A							:  Any    =		[64, 128, 320, 512]

      # load state_dict of original model, remove and rename some keys
      if original_ckpt:
            if original_ckpt.startswith("""https"""  ):
                  _A							:  Any    =		torch.hub.load_state_dict_from_url(snake_case_,map_location="""cpu""",check_hash=snake_case_  )
            else:
                  _A							:  Union[str, Any]    =		torch.load(snake_case_,map_location="""cpu"""  )
      _A							:  List[str]    =		checkpoint

      _A							:  List[Any]    =		create_rename_keys(snake_case_  )
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(snake_case_,snake_case_,snake_case_  )

      # load HuggingFace model
      _A							:  Union[str, Any]    =		SwiftFormerForImageClassification(snake_case_  ).eval()
      hf_model.load_state_dict(snake_case_  )

      # prepare test inputs
      _A							:  Tuple    =		prepare_img()
      _A							:  Dict    =		ViTImageProcessor.from_pretrained("""preprocessor_config"""  )
      _A							:  Optional[Any]    =		processor(images=snake_case_,return_tensors="""pt"""  )

      # compare outputs from both models
      _A							:  Optional[int]    =		get_expected_output(snake_case_  )
      _A							:  Union[str, Any]    =		hf_model(inputs["""pixel_values"""]  ).logits

      assert hf_logits.shape == torch.Size([1, 1000]  )
      assert torch.allclose(hf_logits[0, 0:5],snake_case_,atol=1e-3  )

      Path(snake_case_  ).mkdir(exist_ok=snake_case_  )
      print(f'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''  )
      hf_model.save_pretrained(snake_case_  )


if __name__ == "__main__":
  _snake_case            = argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--swiftformer_name",
      default="swiftformer_xs",
      choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
      type=str,
      help="Name of the SwiftFormer model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default="./converted_outputs/",
      type=str,
      help="Path to the output PyTorch model directory.",
  )
  parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

  _snake_case            = parser.parse_args()
  convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







from __future__ import annotations







class 		lowercase							:

     def __init__(						self	,  _a = 0       )		->							str:
           _A							:  Any    =		key

     def       a__			(						self	,  _a	,  _a       )		->							list[str]:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  Any    =		key or self.__key or 1

           # make sure key is an appropriate size
           key %= 255

           return [chr(ord(_a       ) ^ key       ) for ch in content]

     def       a__			(						self	,  _a	,  _a       )		->							list[str]:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[Any]    =		key or self.__key or 1

           # make sure key is an appropriate size
           key %= 255

           return [chr(ord(_a       ) ^ key       ) for ch in content]

     def       a__			(						self	,  _a	,  _a = 0       )		->							str:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[Any]    =		key or self.__key or 1

           # make sure key can be any size
           while key > 255:
                 key -= 255

           # This will be returned
           _A							:  List[str]    =		""""""

           for ch in content:
                 ans += chr(ord(_a       ) ^ key       )

           return ans

     def       a__			(						self	,  _a	,  _a = 0       )		->							str:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[str]    =		key or self.__key or 1

           # make sure key can be any size
           while key > 255:
                 key -= 255

           # This will be returned
           _A							:  List[str]    =		""""""

           for ch in content:
                 ans += chr(ord(_a       ) ^ key       )

           return ans

     def       a__			(						self	,  _a	,  _a = 0       )		->							bool:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           try:
                 with open(_a       ) as fin, open("""encrypt.out"""	,  """w+"""       ) as fout:
                       # actual encrypt-process
                       for line in fin:
                             fout.write(self.encrypt_string(_a	,  _a       )       )

           except OSError:
                 return False

           return True



     def       a__			(						self	,  _a	,  _a       )		->							bool:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           try:
                 with open(_a       ) as fin, open("""decrypt.out"""	,  """w+"""       ) as fout:
                       # actual encrypt-process
                       for line in fin:
                             fout.write(self.decrypt_string(_a	,  _a       )       )

           except OSError:
                 return False

           return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")

