


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()
__a     =			logging.get_logger(__name__)

__a     =			torch.device("""cpu""")
def   _UpperCamelCase				(						)						->Tuple:
   UpperCAmelCase							=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
   UpperCAmelCase							=     Image.open(requests.get(lowerCAmelCase_  ,						stream=lowerCAmelCase_   ).raw   )
   return im
def   _UpperCamelCase				(						lowerCAmelCase_   )						->List[str]:
   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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Dict:
   UpperCAmelCase							=     dct.pop(lowerCAmelCase_   )
   UpperCAmelCase							=     val
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Union[str, Any]:
   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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->int:
   UpperCAmelCase							=     SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   UpperCAmelCase							=     1_0_0_0
   UpperCAmelCase							=     """huggingface/label-files"""
   UpperCAmelCase							=     """imagenet-1k-id2label.json"""
   UpperCAmelCase							=     json.load(open(hf_hub_download(lowerCAmelCase_  ,						lowerCAmelCase_  ,						repo_type="""dataset"""   )  ,						"""r"""   )   )
   UpperCAmelCase							=     {int(lowerCAmelCase_   ): 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							=     [4_8, 5_6, 1_1_2, 2_2_0]

   elif swiftformer_name == "swiftformer_s":
      UpperCAmelCase							=     [3, 3, 9, 6]
      UpperCAmelCase							=     [4_8, 6_4, 1_6_8, 2_2_4]

   elif swiftformer_name == "swiftformer_l1":
      UpperCAmelCase							=     [4, 3, 1_0, 5]
      UpperCAmelCase							=     [4_8, 9_6, 1_9_2, 3_8_4]

   elif swiftformer_name == "swiftformer_l3":
      UpperCAmelCase							=     [4, 4, 1_2, 6]
      UpperCAmelCase							=     [6_4, 1_2_8, 3_2_0, 5_1_2]

   # 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(lowerCAmelCase_  ,						map_location="""cpu"""  ,						check_hash=lowerCAmelCase_   )
      else:
         UpperCAmelCase							=     torch.load(lowerCAmelCase_  ,						map_location="""cpu"""   )
   UpperCAmelCase							=     checkpoint

   UpperCAmelCase							=     create_rename_keys(lowerCAmelCase_   )
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )

   # load HuggingFace model
   UpperCAmelCase							=     SwiftFormerForImageClassification(lowerCAmelCase_   ).eval()
   hf_model.load_state_dict(lowerCAmelCase_   )

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

   # compare outputs from both models
   UpperCAmelCase							=     get_expected_output(lowerCAmelCase_   )
   UpperCAmelCase							=     hf_model(inputs["""pixel_values"""]   ).logits

   assert hf_logits.shape == torch.Size([1, 1_0_0_0]   )
   assert torch.allclose(hf_logits[0, 0:5]  ,						lowerCAmelCase_  ,						atol=1e-3   )

   Path(lowerCAmelCase_   ).mkdir(exist_ok=lowerCAmelCase_   )
   print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""   )
   hf_model.save_pretrained(lowerCAmelCase_   )


if __name__ == "__main__":
 __a     =			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.""")

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








__a     =			[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]
def   _UpperCamelCase				(						lowerCAmelCase_   )						->int:
   UpperCAmelCase							=     {"""I""": 1, """V""": 5, """X""": 1_0, """L""": 5_0, """C""": 1_0_0, """D""": 5_0_0, """M""": 1_0_0_0}
   UpperCAmelCase							=     0
   UpperCAmelCase							=     0
   while place < len(lowerCAmelCase_   ):
      if (place + 1 < len(lowerCAmelCase_   )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total
def   _UpperCamelCase				(						lowerCAmelCase_   )						->str:
   UpperCAmelCase							=     []
   for arabic, roman in ROMAN:
      ((UpperCAmelCase)				,  (UpperCAmelCase))							=     divmod(lowerCAmelCase_  ,						lowerCAmelCase_   )
      result.append(roman * factor   )
      if number == 0:
         break
   return "".join(lowerCAmelCase_   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



