


"""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 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     List[Any]								=							"http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCAmelCase     :     Union[str, Any]								=							Image.open(requests.get(SCREAMING_SNAKE_CASE				,      stream=SCREAMING_SNAKE_CASE				).raw				)
  return im
def 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				):


  '''simple docstring'''







  if swiftformer_name == "swiftformer_xs":
    return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]				)

  elif swiftformer_name == "swiftformer_s":
    return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]				)

  elif swiftformer_name == "swiftformer_l1":
    return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]				)

  elif swiftformer_name == "swiftformer_l3":
    return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Tuple				,      SCREAMING_SNAKE_CASE    :    List[str]				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  lowerCAmelCase     :     int								=							dct.pop(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     List[str]								=							val
def 					a__			(				SCREAMING_SNAKE_CASE    :    Tuple				):


  '''simple docstring'''







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


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  lowerCAmelCase     :     Dict								=							1_0_0_0
  lowerCAmelCase     :     Optional[int]								=							"huggingface/label-files"
  lowerCAmelCase     :     Optional[Any]								=							"imagenet-1k-id2label.json"
  lowerCAmelCase     :     str								=							json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      repo_type="dataset"				)				,      "r"				)				)
  lowerCAmelCase     :     str								=							{int(SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
  lowerCAmelCase     :     List[Any]								=							idalabel
  lowerCAmelCase     :     Union[str, Any]								=							{v: k for k, v in idalabel.items()}

  # size of the architecture
  if swiftformer_name == "swiftformer_xs":
    lowerCAmelCase     :     Tuple								=							[3, 3, 6, 4]
    lowerCAmelCase     :     Tuple								=							[4_8, 5_6, 1_1_2, 2_2_0]

  elif swiftformer_name == "swiftformer_s":
    lowerCAmelCase     :     Optional[int]								=							[3, 3, 9, 6]
    lowerCAmelCase     :     Union[str, Any]								=							[4_8, 6_4, 1_6_8, 2_2_4]

  elif swiftformer_name == "swiftformer_l1":
    lowerCAmelCase     :     Any								=							[4, 3, 1_0, 5]
    lowerCAmelCase     :     Any								=							[4_8, 9_6, 1_9_2, 3_8_4]

  elif swiftformer_name == "swiftformer_l3":
    lowerCAmelCase     :     List[Any]								=							[4, 4, 1_2, 6]
    lowerCAmelCase     :     str								=							[6_4, 1_2_8, 3_2_0, 5_1_2]

  # load state_dict of original model, remove and rename some keys
  if original_ckpt:
    if original_ckpt.startswith("https"				):
      lowerCAmelCase     :     Tuple								=							torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE				,      map_location="cpu"				,      check_hash=SCREAMING_SNAKE_CASE				)
    else:
      lowerCAmelCase     :     Any								=							torch.load(SCREAMING_SNAKE_CASE				,      map_location="cpu"				)
  lowerCAmelCase     :     Tuple								=							checkpoint

  lowerCAmelCase     :     Optional[int]								=							create_rename_keys(SCREAMING_SNAKE_CASE				)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)

  # load HuggingFace model
  lowerCAmelCase     :     Union[str, Any]								=							SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE				).eval()
  hf_model.load_state_dict(SCREAMING_SNAKE_CASE				)

  # prepare test inputs
  lowerCAmelCase     :     List[Any]								=							prepare_img()
  lowerCAmelCase     :     Dict								=							ViTImageProcessor.from_pretrained("preprocessor_config"				)
  lowerCAmelCase     :     int								=							processor(images=SCREAMING_SNAKE_CASE				,      return_tensors="pt"				)

  # compare outputs from both models
  lowerCAmelCase     :     int								=							get_expected_output(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Tuple								=							hf_model(inputs["pixel_values"]				).logits

  assert hf_logits.shape == torch.Size([1, 1_0_0_0]				)
  assert torch.allclose(hf_logits[0, 0:5]				,      SCREAMING_SNAKE_CASE				,      atol=1E-3				)

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


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)






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int ,     __UpperCamelCase    :       int	)							->					str:







  """simple docstring"""





  if a < 0 or b < 0:
    raise ValueError("""the value of both inputs must be positive"""	)

  SCREAMING_SNAKE_CASE__										=      str(bin(__UpperCamelCase	)	)[2:]  # remove the leading "0b"
  SCREAMING_SNAKE_CASE__										=      str(bin(__UpperCamelCase	)	)[2:]  # remove the leading "0b"

  SCREAMING_SNAKE_CASE__										=      max(len(__UpperCamelCase	) ,     len(__UpperCamelCase	)	)

  return "0b" + "".join(
      str(int(char_a != char_b	)	)
      for char_a, char_b in zip(a_binary.zfill(__UpperCamelCase	) ,     b_binary.zfill(__UpperCamelCase	)	)	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
