





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__					:							Any													=     logging.get_logger(__name__)

snake_case__					:							str													=     torch.device('cpu')



def _a				(	)	->						Dict:







      '''simple docstring'''






      __A          =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      __A          =				Image.open(requests.get(snake_case_					,				stream=snake_case_ ).raw )
      return im



def _a				(	lowerCamelCase:   List[str] )	->						str:







      '''simple docstring'''






      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 _a				(	lowerCamelCase:   Tuple					,				lowerCamelCase:   Dict					,				lowerCamelCase:   int )	->						Union[str, Any]:







      '''simple docstring'''






      __A          =				dct.pop(snake_case_ )
      __A          =				val



def _a				(	lowerCamelCase:   Tuple )	->						int:







      '''simple docstring'''






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



@torch.no_grad()
def _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   Optional[Any]					,				lowerCamelCase:   List[Any] )	->						List[str]:







      '''simple docstring'''






      __A          =				SwiftFormerConfig()

      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      __A          =				10_00
      __A          =				"""huggingface/label-files"""
      __A          =				"""imagenet-1k-id2label.json"""
      __A          =				json.load(open(hf_hub_download(snake_case_					,				snake_case_					,				repo_type='''dataset''' )					,				'''r''' ) )
      __A          =				{int(snake_case_ ): v for k, v in idalabel.items()}
      __A          =				idalabel
      __A          =				{v: k for k, v in idalabel.items()}

      # size of the architecture
      if swiftformer_name == "swiftformer_xs":
            __A          =				[3, 3, 6, 4]
            __A          =				[48, 56, 1_12, 2_20]

      elif swiftformer_name == "swiftformer_s":
            __A          =				[3, 3, 9, 6]
            __A          =				[48, 64, 1_68, 2_24]

      elif swiftformer_name == "swiftformer_l1":
            __A          =				[4, 3, 10, 5]
            __A          =				[48, 96, 1_92, 3_84]

      elif swiftformer_name == "swiftformer_l3":
            __A          =				[4, 4, 12, 6]
            __A          =				[64, 1_28, 3_20, 5_12]

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

      __A          =				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          =				SwiftFormerForImageClassification(snake_case_ ).eval()
      hf_model.load_state_dict(snake_case_ )

      # prepare test inputs
      __A          =				prepare_img()
      __A          =				ViTImageProcessor.from_pretrained('''preprocessor_config''' )
      __A          =				processor(images=snake_case_					,				return_tensors='''pt''' )

      # compare outputs from both models
      __A          =				get_expected_output(snake_case_ )
      __A          =				hf_model(inputs['''pixel_values'''] ).logits

      assert hf_logits.shape == torch.Size([1, 10_00] )
      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__					:							Tuple													=     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__					:							Tuple													=     parser.parse_args()
       convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)











def 				lowerCAmelCase_	(     snake_case_     ):
      if number < 0:
            raise ValueError("""number must not be negative"""     )
      return number & (number - 1) == 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

