

'''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()
__lowerCamelCase						    =      logging.get_logger(__name__)

__lowerCamelCase						    =      torch.device('''cpu''')
def  UpperCAmelCase__      ( )     ->     List[str]:
       A_		   = """http://images.cocodataset.org/val2017/000000039769.jpg"""
       A_		   = Image.open(requests.get(UpperCAmelCase__,    stream=UpperCAmelCase__			).raw			)
       return im
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       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  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Optional[int]:
       A_		   = dct.pop(UpperCAmelCase__			)
       A_		   = val
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Optional[Any]:
       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__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     List[Any]:
       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(UpperCAmelCase__,    UpperCAmelCase__,    repo_type="""dataset"""			),    """r"""			)			)
       A_		   = {int(UpperCAmelCase__			): 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(UpperCAmelCase__,    map_location="""cpu""",    check_hash=UpperCAmelCase__			)
              else:
                     A_		   = torch.load(UpperCAmelCase__,    map_location="""cpu"""			)
       A_		   = checkpoint

       A_		   = create_rename_keys(UpperCAmelCase__			)
       for rename_key_src, rename_key_dest in rename_keys:
              rename_key(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)

       # load HuggingFace model
       A_		   = SwiftFormerForImageClassification(UpperCAmelCase__			).eval()
       hf_model.load_state_dict(UpperCAmelCase__			)

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

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

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

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


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'''







def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     bool:
       A_		   = len(UpperCAmelCase__			)
       A_		   = [[False] * (required_sum + 1) for _ in range(arr_len + 1			)]

       # for each arr value, a sum of zero(0) can be formed by not taking any element
       # hence True/1
       for i in range(arr_len + 1			):
              A_		   = True

       # sum is not zero and set is empty then false
       for i in range(1,    required_sum + 1			):
              A_		   = False

       for i in range(1,    arr_len + 1			):
              for j in range(1,    required_sum + 1			):
                     if arr[i - 1] > j:
                            A_		   = subset[i - 1][j]
                     if arr[i - 1] <= j:
                            A_		   = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

       return subset[arr_len][required_sum]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
