





"""simple docstring"""
import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def      __lowerCamelCase   (							a_     :		str ,   a_     :		Tuple							)			->      Any:
      __SCREAMING_SNAKE_CASE   :int						=  old_name

      if "patch_embed" in old_name:
            __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Union[str, Any]						=  old_name.split('''.'''							)

            if layer == "0":
                  __SCREAMING_SNAKE_CASE   :List[str]						=  old_name.replace('''0''' ,   '''convolution1'''							)
            elif layer == "1":
                  __SCREAMING_SNAKE_CASE   :Optional[int]						=  old_name.replace('''1''' ,   '''batchnorm_before'''							)
            elif layer == "3":
                  __SCREAMING_SNAKE_CASE   :Optional[Any]						=  old_name.replace('''3''' ,   '''convolution2'''							)
            else:
                  __SCREAMING_SNAKE_CASE   :Optional[int]						=  old_name.replace('''4''' ,   '''batchnorm_after'''							)

      if "network" in old_name and re.search(r'''\d\.\d''' ,   a_							):
            __SCREAMING_SNAKE_CASE   :Dict						=  r'''\b\d{2}\b'''
            if bool(re.search(a_ ,   a_							)							):
                  __SCREAMING_SNAKE_CASE   :List[str]						=  re.search(r'''\d\.\d\d.''' ,   a_							).group()
            else:
                  __SCREAMING_SNAKE_CASE   :Dict						=  re.search(r'''\d\.\d.''' ,   a_							).group()
            if int(match[0]							) < 6:
                  __SCREAMING_SNAKE_CASE   :str						=  old_name.replace(a_ ,   ''''''							)
                  __SCREAMING_SNAKE_CASE   :Optional[Any]						=  trimmed_name.replace('''network''' ,   match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]							)
                  __SCREAMING_SNAKE_CASE   :Optional[Any]						=  '''intermediate_stages.''' + trimmed_name
            else:
                  __SCREAMING_SNAKE_CASE   :Any						=  old_name.replace(a_ ,   ''''''							)
                  if int(match[2]							) < num_meta4D_last_stage:
                        __SCREAMING_SNAKE_CASE   :List[str]						=  trimmed_name.replace('''network''' ,   '''meta4D_layers.blocks.''' + match[2]							)
                  else:
                        __SCREAMING_SNAKE_CASE   :List[Any]						=  str(int(match[2]							) - num_meta4D_last_stage							)
                        __SCREAMING_SNAKE_CASE   :Any						=  trimmed_name.replace('''network''' ,   '''meta3D_layers.blocks.''' + layer_index							)
                        if "norm1" in old_name:
                              __SCREAMING_SNAKE_CASE   :Any						=  trimmed_name.replace('''norm1''' ,   '''layernorm1'''							)
                        elif "norm2" in old_name:
                              __SCREAMING_SNAKE_CASE   :str						=  trimmed_name.replace('''norm2''' ,   '''layernorm2'''							)
                        elif "fc1" in old_name:
                              __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  trimmed_name.replace('''fc1''' ,   '''linear_in'''							)
                        elif "fc2" in old_name:
                              __SCREAMING_SNAKE_CASE   :str						=  trimmed_name.replace('''fc2''' ,   '''linear_out'''							)

                  __SCREAMING_SNAKE_CASE   :List[Any]						=  '''last_stage.''' + trimmed_name

      elif "network" in old_name and re.search(r'''.\d.''' ,   a_							):
            __SCREAMING_SNAKE_CASE   :List[str]						=  old_name.replace('''network''' ,   '''intermediate_stages'''							)

      if "fc" in new_name:
            __SCREAMING_SNAKE_CASE   :int						=  new_name.replace('''fc''' ,   '''convolution'''							)
      elif ("norm1" in new_name) and ("layernorm1" not in new_name):
            __SCREAMING_SNAKE_CASE   :str						=  new_name.replace('''norm1''' ,   '''batchnorm_before'''							)
      elif ("norm2" in new_name) and ("layernorm2" not in new_name):
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  new_name.replace('''norm2''' ,   '''batchnorm_after'''							)
      if "proj" in new_name:
            __SCREAMING_SNAKE_CASE   :Tuple						=  new_name.replace('''proj''' ,   '''projection'''							)
      if "dist_head" in new_name:
            __SCREAMING_SNAKE_CASE   :List[Any]						=  new_name.replace('''dist_head''' ,   '''distillation_classifier'''							)
      elif "head" in new_name:
            __SCREAMING_SNAKE_CASE   :Tuple						=  new_name.replace('''head''' ,   '''classifier'''							)
      elif "patch_embed" in new_name:
            __SCREAMING_SNAKE_CASE   :Any						=  '''efficientformer.''' + new_name
      elif new_name == "norm.weight" or new_name == "norm.bias":
            __SCREAMING_SNAKE_CASE   :int						=  new_name.replace('''norm''' ,   '''layernorm'''							)
            __SCREAMING_SNAKE_CASE   :Any						=  '''efficientformer.''' + new_name
      else:
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  '''efficientformer.encoder.''' + new_name

      return new_name



def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		List[Any]							)			->      Optional[Any]:
      for key in checkpoint.copy().keys():
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  checkpoint.pop(a_							)
            __SCREAMING_SNAKE_CASE   :int						=  val

      return checkpoint



def      __lowerCamelCase   (							)			->      Dict:
      __SCREAMING_SNAKE_CASE   :Dict						=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __SCREAMING_SNAKE_CASE   :Dict						=  Image.open(requests.get(a_ ,   stream=a_							).raw							)

      return image



def      __lowerCamelCase   (							a_     :		Path ,   a_     :		Path ,   a_     :		Path ,   a_     :		bool							)			->      Tuple:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.load(a_ ,   map_location='''cpu'''							)['''model''']
      __SCREAMING_SNAKE_CASE   :str						=  EfficientFormerConfig.from_json_file(a_							)
      __SCREAMING_SNAKE_CASE   :Tuple						=  EfficientFormerForImageClassificationWithTeacher(a_							)
      __SCREAMING_SNAKE_CASE   :Any						=  '''_'''.join(checkpoint_path.split('''/'''							)[-1].split('''.'''							)[0].split('''_'''							)[:-1]							)

      __SCREAMING_SNAKE_CASE   :List[Any]						=  config.depths[-1] - config.num_metaad_blocks + 1
      __SCREAMING_SNAKE_CASE   :Any						=  convert_torch_checkpoint(a_ ,   a_							)

      model.load_state_dict(a_							)
      model.eval()

      __SCREAMING_SNAKE_CASE   :List[Any]						=  {
          '''bilinear''': PILImageResampling.BILINEAR,
          '''bicubic''': PILImageResampling.BICUBIC,
          '''nearest''': PILImageResampling.NEAREST,
      }

      # prepare image
      __SCREAMING_SNAKE_CASE   :int						=  prepare_img()
      __SCREAMING_SNAKE_CASE   :int						=  2_56
      __SCREAMING_SNAKE_CASE   :List[Any]						=  2_24
      __SCREAMING_SNAKE_CASE   :List[Any]						=  EfficientFormerImageProcessor(
          size={'''shortest_edge''': image_size} ,   crop_size={'''height''': crop_size, '''width''': crop_size} ,   resample=pillow_resamplings['''bicubic'''] ,   )
      __SCREAMING_SNAKE_CASE   :int						=  processor(images=a_ ,   return_tensors='''pt'''							).pixel_values

      # original processing pipeline
      __SCREAMING_SNAKE_CASE   :List[Any]						=  Compose(
          [
              Resize(a_ ,   interpolation=pillow_resamplings['''bicubic''']							),
              CenterCrop(a_							),
              ToTensor(),
              Normalize(a_ ,   a_							),
          ]							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  image_transforms(a_							).unsqueeze(0							)

      assert torch.allclose(a_ ,   a_							)

      __SCREAMING_SNAKE_CASE   :str						=  model(a_							)
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  outputs.logits

      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  (1, 10_00)

      if "l1" in model_name:
            __SCREAMING_SNAKE_CASE   :Any						=  torch.Tensor(
                [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]							)
            assert torch.allclose(logits[0, :10] ,   a_ ,   atol=1e-3							)
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            __SCREAMING_SNAKE_CASE   :str						=  torch.Tensor(
                [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]							)
            assert torch.allclose(logits[0, :10] ,   a_ ,   atol=1e-3							)
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            __SCREAMING_SNAKE_CASE   :int						=  torch.Tensor(
                [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]							)
            assert logits.shape == expected_shape
      else:
            raise ValueError(
                f'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''							)

      # Save Checkpoints
      Path(a_							).mkdir(exist_ok=a_							)
      model.save_pretrained(a_							)
      print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''							)
      processor.save_pretrained(a_							)
      print(f'''Processor successfuly saved at {pytorch_dump_path}'''							)

      if push_to_hub:
            print('''Pushing model to the hub...'''							)

            model.push_to_hub(
                repo_id=f'''Bearnardd/{pytorch_dump_path}''' ,   commit_message='''Add model''' ,   use_temp_dir=a_ ,   )
            processor.push_to_hub(
                repo_id=f'''Bearnardd/{pytorch_dump_path}''' ,   commit_message='''Add image processor''' ,   use_temp_dir=a_ ,   )


if __name__ == "__main__":
  lowerCamelCase_								=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--pytorch_model_path",
      default=None,
      type=str,
      required=True,
      help="Path to EfficientFormer pytorch checkpoint.",
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help="The json file for EfficientFormer model config.",
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )

  parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
  parser.add_argument(
      "--no-push_to_hub",
      dest="push_to_hub",
      action="store_false",
      help="Do not push model and image processor to the hub",
  )
  parser.set_defaults(push_to_hub=True)

  lowerCamelCase_								=		parser.parse_args()
  convert_efficientformer_checkpoint(
      checkpoint_path=args.pytorch_model_path,
      efficientformer_config_file=args.config_file,
      pytorch_dump_path=args.pytorch_dump_path,
      push_to_hub=args.push_to_hub,
  )






"""simple docstring"""
def      __lowerCamelCase   (							a_     :		Union[str, Any]							)			->      Optional[int]:
      __SCREAMING_SNAKE_CASE   :List[str]						=  1
      __SCREAMING_SNAKE_CASE   :Dict						=  2
      while i * i <= n:
            __SCREAMING_SNAKE_CASE   :Tuple						=  0
            while n % i == 0:
                  n //= i
                  multiplicity += 1
            n_divisors *= multiplicity + 1
            i += 1
      if n > 1:
            n_divisors *= 2
      return n_divisors



def      __lowerCamelCase   (							)			->      int:
      __SCREAMING_SNAKE_CASE   :Dict						=  1
      __SCREAMING_SNAKE_CASE   :Dict						=  1

      while True:
            i += 1
            t_num += i

            if count_divisors(a_							) > 5_00:
                  break

      return t_num


if __name__ == "__main__":
  print(solution())