



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 	snake_case_  (				):


       '''simple docstring'''





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






def 	snake_case_  (				A_ :		List[str]						):


       '''simple docstring'''





       if swiftformer_name == "swiftformer_xs":
              return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]						)

       elif swiftformer_name == "swiftformer_s":
              return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]						)

       elif swiftformer_name == "swiftformer_l1":
              return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]						)

       elif swiftformer_name == "swiftformer_l3":
              return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]						)






def 	snake_case_  (				A_ :		List[Any],    A_ :		Optional[int],    A_ :		str						):


       '''simple docstring'''





       _lowerCamelCase						:    Dict         =			dct.pop(__lowerCAmelCase						)
       _lowerCamelCase						:    Optional[Any]         =			val






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





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






@torch.no_grad()
def 	snake_case_  (				A_ :		Optional[Any],    A_ :		str,    A_ :		str						):


       '''simple docstring'''





       _lowerCamelCase						:    List[str]         =			SwiftFormerConfig()

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

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

       elif swiftformer_name == "swiftformer_s":
              _lowerCamelCase						:    Tuple         =			[3, 3, 9, 6]
              _lowerCamelCase						:    Union[str, Any]         =			[48, 64, 1_68, 2_24]

       elif swiftformer_name == "swiftformer_l1":
              _lowerCamelCase						:    List[Any]         =			[4, 3, 10, 5]
              _lowerCamelCase						:    Union[str, Any]         =			[48, 96, 1_92, 3_84]

       elif swiftformer_name == "swiftformer_l3":
              _lowerCamelCase						:    List[Any]         =			[4, 4, 12, 6]
              _lowerCamelCase						:    Any         =			[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'''						):
                     _lowerCamelCase						:    Optional[int]         =			torch.hub.load_state_dict_from_url(__lowerCAmelCase,    map_location='''cpu''',    check_hash=__lowerCAmelCase						)
              else:
                     _lowerCamelCase						:    Dict         =			torch.load(__lowerCAmelCase,    map_location='''cpu'''						)
       _lowerCamelCase						:    Optional[int]         =			checkpoint

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

       # load HuggingFace model
       _lowerCamelCase						:    List[Any]         =			SwiftFormerForImageClassification(__lowerCAmelCase						).eval()
       hf_model.load_state_dict(__lowerCAmelCase						)

       # prepare test inputs
       _lowerCamelCase						:    Tuple         =			prepare_img()
       _lowerCamelCase						:    str         =			ViTImageProcessor.from_pretrained('''preprocessor_config'''						)
       _lowerCamelCase						:    int         =			processor(images=__lowerCAmelCase,    return_tensors='''pt'''						)

       # compare outputs from both models
       _lowerCamelCase						:    List[Any]         =			get_expected_output(__lowerCAmelCase						)
       _lowerCamelCase						:    List[str]         =			hf_model(inputs['''pixel_values''']						).logits

       assert hf_logits.shape == torch.Size([1, 10_00]						)
       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__":
   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)







"""simple docstring"""






import math






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       return math.sqrt(A_						) * math.sqrt(A_						) == num






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       _lowerCamelCase						:    Dict         =			0
       _lowerCamelCase						:    Tuple         =			n
       while left <= right:
              _lowerCamelCase						:    List[str]         =			(left + right) // 2
              if mid**2 == n:
                     return True
              elif mid**2 > n:
                     _lowerCamelCase						:    int         =			mid - 1
              else:
                     _lowerCamelCase						:    Optional[Any]         =			mid + 1
       return False


if __name__ == "__main__":
   import doctest

   doctest.testmod()


