





'''simple docstring'''
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__							:							Optional[int] =logging.get_logger(__name__)

a__							:							Any =torch.device('''cpu''')
def 				lowercase__   (   ) ->      Tuple:



       """simple docstring"""






       __UpperCamelCase       =  'http://images.cocodataset.org/val2017/000000039769.jpg'
       __UpperCamelCase       =  Image.open(requests.get(__lowercase				,  stream=__lowercase     ).raw     )
       return im
def 				lowercase__   (   __lowercase						:  int     ) ->      Tuple:



       """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 				lowercase__   (   __lowercase						:  Optional[Any]				,  __lowercase						:  str				,  __lowercase						:  Dict     ) ->      List[str]:



       """simple docstring"""






       __UpperCamelCase       =  dct.pop(__lowercase     )
       __UpperCamelCase       =  val
def 				lowercase__   (   __lowercase						:  Optional[Any]     ) ->      str:



       """simple docstring"""






       __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 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Tuple				,  __lowercase						:  int     ) ->      Tuple:



       """simple docstring"""






       __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(__lowercase				,  __lowercase				,  repo_type='dataset'     )				,  'r'     )     )
       __UpperCamelCase       =  {int(__lowercase     ): 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(__lowercase				,  map_location='cpu'				,  check_hash=__lowercase     )
              else:
                     __UpperCamelCase       =  torch.load(__lowercase				,  map_location='cpu'     )
       __UpperCamelCase       =  checkpoint

       __UpperCamelCase       =  create_rename_keys(__lowercase     )
       for rename_key_src, rename_key_dest in rename_keys:
              rename_key(__lowercase				,  __lowercase				,  __lowercase     )

       # load HuggingFace model
       __UpperCamelCase       =  SwiftFormerForImageClassification(__lowercase     ).eval()
       hf_model.load_state_dict(__lowercase     )

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

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

       assert hf_logits.shape == torch.Size([1, 1000]     )
       assert torch.allclose(hf_logits[0, 0:5]				,  __lowercase				,  atol=1e-3     )

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


if __name__ == "__main__":
    a__							:							Optional[Any] =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__							:							Union[str, Any] =parser.parse_args()
    convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







'''simple docstring'''
from __future__ import annotations

from typing import Any

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 pass






class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							List[Any]							,	__A       :							Any	):
        __UpperCamelCase       =  data
        __UpperCamelCase       =  None






 def __iter__(       self       :							Optional[Any]	):
        __UpperCamelCase       =  self
        __UpperCamelCase       =  []
        while node:
               if node in visited:
                      raise ContainsLoopError
               visited.append(__A	)
               yield node.data
               __UpperCamelCase       =  node.next_node






 @property
 def        _lowerCamelCase					(       self       :							List[str]	):
        try:
               list(self	)
               return False
        except ContainsLoopError:
               return True


if __name__ == "__main__":
    a__							:							Dict =Node(1)
    a__							:							Optional[int] =Node(2)
    a__							:							List[str] =Node(3)
    a__							:							Optional[int] =Node(4)
    print(root_node.has_loop)  # False
    a__							:							str =root_node.next_node
    print(root_node.has_loop)  # True

    a__							:							Optional[int] =Node(5)
    a__							:							List[Any] =Node(6)
    a__							:							int =Node(5)
    a__							:							Tuple =Node(6)
    print(root_node.has_loop)  # False

    a__							:							str =Node(1)
    print(root_node.has_loop)  # False
