






def __A    (      __lowerCAmelCase = 600_851_475_143	)->  int:





						"""simple docstring"""



						try:
												_UpperCAmelCase									=			int(__lowerCAmelCase	)
						except (TypeError, ValueError):
												raise TypeError('Parameter n must be int or castable to int.'	)
						if n <= 0:
												raise ValueError('Parameter n must be greater than or equal to one.'	)
						_UpperCAmelCase									=			2
						_UpperCAmelCase									=			0
						if n == 2:
												return 2
						while n > 2:
												while n % i != 0:
																		i += 1
												_UpperCAmelCase									=			i
												while n % i == 0:
																		_UpperCAmelCase									=			n // i
												i += 1
						return int(__lowerCAmelCase	)


if __name__ == "__main__":
		print(F'''{solution() = }''')



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()
lowerCamelCase           =					logging.get_logger(__name__)

lowerCamelCase           =					torch.device('''cpu''')




def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  a__  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
  a__  =Image.open(requests.get(_A	,      stream=_A							).raw							)
  return im




def 			UpperCAmelCase__				(			_A      : Optional[int]							):





  '''simple docstring'''







  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__				(			_A      : List[str]	,      _A      : Union[str, Any]	,      _A      : str							):





  '''simple docstring'''







  a__  =dct.pop(_A							)
  a__  =val




def 			UpperCAmelCase__				(			_A      : Any							):





  '''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 			UpperCAmelCase__				(			_A      : int	,      _A      : Union[str, Any]	,      _A      : int							):





  '''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(_A	,      _A	,      repo_type='''dataset'''							)	,      '''r'''							)							)
  a__  ={int(_A							): 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(_A	,      map_location='''cpu'''	,      check_hash=_A							)
    else:
      a__  =torch.load(_A	,      map_location='''cpu'''							)
  a__  =checkpoint

  a__  =create_rename_keys(_A							)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(_A	,      _A	,      _A							)

  # load HuggingFace model
  a__  =SwiftFormerForImageClassification(_A							).eval()
  hf_model.load_state_dict(_A							)

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

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

  assert hf_logits.shape == torch.Size([1, 10_00]							)
  assert torch.allclose(hf_logits[0, 0:5]	,      _A	,      atol=1E-3							)

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


if __name__ == "__main__":
       lowerCamelCase           =					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.''')

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