





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()
_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				torch.device('cpu')




def  lowerCAmelCase_ (							)     ->		List[Any]:
       UpperCamelCase_          =  "http://images.cocodataset.org/val2017/000000039769.jpg"
       UpperCamelCase_          =  Image.open(requests.get(__snake_case					,		stream=__snake_case  ).raw  )
       return im




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		int:







       if swiftformer_name == "swiftformer_xs":
              return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]  )

       elif swiftformer_name == "swiftformer_s":
              return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]  )

       elif swiftformer_name == "swiftformer_l1":
              return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]  )

       elif swiftformer_name == "swiftformer_l3":
              return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]  )




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Tuple:
       UpperCamelCase_          =  dct.pop(__snake_case  )
       UpperCamelCase_          =  val




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




@torch.no_grad()
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Optional[Any]:
       UpperCamelCase_          =  SwiftFormerConfig()

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

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

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

       elif swiftformer_name == "swiftformer_l1":
              UpperCamelCase_          =  [4, 3, 10, 5]
              UpperCamelCase_          =  [48, 96, 192, 384]

       elif swiftformer_name == "swiftformer_l3":
              UpperCamelCase_          =  [4, 4, 12, 6]
              UpperCamelCase_          =  [64, 128, 320, 512]

       # load state_dict of original model, remove and rename some keys
       if original_ckpt:
              if original_ckpt.startswith("https"  ):
                     UpperCamelCase_          =  torch.hub.load_state_dict_from_url(__snake_case					,		map_location="cpu"					,		check_hash=__snake_case  )
              else:
                     UpperCamelCase_          =  torch.load(__snake_case					,		map_location="cpu"  )
       UpperCamelCase_          =  checkpoint

       UpperCamelCase_          =  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
       UpperCamelCase_          =  SwiftFormerForImageClassification(__snake_case  ).eval()
       hf_model.load_state_dict(__snake_case  )

       # prepare test inputs
       UpperCamelCase_          =  prepare_img()
       UpperCamelCase_          =  ViTImageProcessor.from_pretrained("preprocessor_config"  )
       UpperCamelCase_          =  processor(images=__snake_case					,		return_tensors="pt"  )

       # compare outputs from both models
       UpperCamelCase_          =  get_expected_output(__snake_case  )
       UpperCamelCase_          =  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__":
     _UpperCAmelCase        =				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.')

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










import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Optional[int]:
       return x + 2







class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: Optional[int]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  "x = 3"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 3
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3}		)

              UpperCamelCase_          =  "x = y"
              UpperCamelCase_          =  {"y": 5}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 5, "y": 5}		)

       def lowercase (		self: List[str]		)       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  "y = add_two(x)"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 5}		)

              # Won't work without the tool
              with CaptureStdout() as out:
                     UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result is None
              assert "tried to execute add_two" in out.out

       def lowercase (		self: Union[str, Any]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  "x = 3"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 3
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3}		)

       def lowercase (		self: Dict		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  "test_dict = {'x': x, 'y': add_two(x)}"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 5}		)
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_dict": {"x": 3, "y": 5}}		)

       def lowercase (		self: List[str]		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  "x = 3\ny = 5"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 5}		)

       def lowercase (		self: List[Any]		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  "text = f'This is x: {x}.'"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == "This is x: 3."
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "text": "This is x: 3."}		)

       def lowercase (		self: int		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  "if x <= 3:\n    y = 2\nelse:\n    y = 5"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 2
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 2}		)

              UpperCamelCase_          =  {"x": 8}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 8, "y": 5}		)

       def lowercase (		self: str		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  "test_list = [x, add_two(x)]"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [3, 5]		)
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_list": [3, 5]}		)

       def lowercase (		self: List[str]		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  "y = x"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 3
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 3}		)

       def lowercase (		self: str		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  "test_list = [x, add_two(x)]\ntest_list[1]"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_list": [3, 5]}		)

              UpperCamelCase_          =  "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_dict": {"x": 3, "y": 5}}		)



       def lowercase (		self: Dict		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  "x = 0\nfor i in range(3):\n    x = i"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"range": range}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 2
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 2, "i": 2}		)

