





'''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			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							Dict		)		->  Dict:
   snake_case 						=   old_name

   if "patch_embed" in old_name:
      snake_case						,    snake_case						,    snake_case 						=   old_name.split("""."""		)

      if layer == "0":
         snake_case 						=   old_name.replace("""0"""      ,    """convolution1"""		)
      elif layer == "1":
         snake_case 						=   old_name.replace("""1"""      ,    """batchnorm_before"""		)
      elif layer == "3":
         snake_case 						=   old_name.replace("""3"""      ,    """convolution2"""		)
      else:
         snake_case 						=   old_name.replace("""4"""      ,    """batchnorm_after"""		)

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

         snake_case 						=   """last_stage.""" + trimmed_name

   elif "network" in old_name and re.search(r""".\d."""      ,    __lowerCAmelCase		):
      snake_case 						=   old_name.replace("""network"""      ,    """intermediate_stages"""		)

   if "fc" in new_name:
      snake_case 						=   new_name.replace("""fc"""      ,    """convolution"""		)
   elif ("norm1" in new_name) and ("layernorm1" not in new_name):
      snake_case 						=   new_name.replace("""norm1"""      ,    """batchnorm_before"""		)
   elif ("norm2" in new_name) and ("layernorm2" not in new_name):
      snake_case 						=   new_name.replace("""norm2"""      ,    """batchnorm_after"""		)
   if "proj" in new_name:
      snake_case 						=   new_name.replace("""proj"""      ,    """projection"""		)
   if "dist_head" in new_name:
      snake_case 						=   new_name.replace("""dist_head"""      ,    """distillation_classifier"""		)
   elif "head" in new_name:
      snake_case 						=   new_name.replace("""head"""      ,    """classifier"""		)
   elif "patch_embed" in new_name:
      snake_case 						=   """efficientformer.""" + new_name
   elif new_name == "norm.weight" or new_name == "norm.bias":
      snake_case 						=   new_name.replace("""norm"""      ,    """layernorm"""		)
      snake_case 						=   """efficientformer.""" + new_name
   else:
      snake_case 						=   """efficientformer.encoder.""" + new_name

   return new_name





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Dict		)		->  int:
   for key in checkpoint.copy().keys():
      snake_case 						=   checkpoint.pop(__lowerCAmelCase		)
      snake_case 						=   val

   return checkpoint





def 	__lowerCamelCase			(      )		->  List[str]:
   snake_case 						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case 						=   Image.open(requests.get(__lowerCAmelCase      ,    stream=__lowerCAmelCase		).raw		)

   return image





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							bool		)		->  Union[str, Any]:
   snake_case 						=   torch.load(__lowerCAmelCase      ,    map_location="""cpu"""		)["""model"""]
   snake_case 						=   EfficientFormerConfig.from_json_file(__lowerCAmelCase		)
   snake_case 						=   EfficientFormerForImageClassificationWithTeacher(__lowerCAmelCase		)
   snake_case 						=   """_""".join(checkpoint_path.split("""/"""		)[-1].split("""."""		)[0].split("""_"""		)[:-1]		)

   snake_case 						=   config.depths[-1] - config.num_metaad_blocks + 1
   snake_case 						=   convert_torch_checkpoint(__lowerCAmelCase      ,    __lowerCAmelCase		)

   model.load_state_dict(__lowerCAmelCase		)
   model.eval()

   snake_case 						=   {
       """bilinear""": PILImageResampling.BILINEAR,
       """bicubic""": PILImageResampling.BICUBIC,
       """nearest""": PILImageResampling.NEAREST,
   }

   # prepare image
   snake_case 						=   prepare_img()
   snake_case 						=   2_56
   snake_case 						=   2_24
   snake_case 						=   EfficientFormerImageProcessor(
       size={"""shortest_edge""": image_size}      ,    crop_size={"""height""": crop_size, """width""": crop_size}      ,    resample=pillow_resamplings["""bicubic"""]      ,    )
   snake_case 						=   processor(images=__lowerCAmelCase      ,    return_tensors="""pt"""		).pixel_values

   # original processing pipeline
   snake_case 						=   Compose(
       [
           Resize(__lowerCAmelCase      ,    interpolation=pillow_resamplings["""bicubic"""]		),
           CenterCrop(__lowerCAmelCase		),
           ToTensor(),
           Normalize(__lowerCAmelCase      ,    __lowerCAmelCase		),
       ]		)
   snake_case 						=   image_transforms(__lowerCAmelCase		).unsqueeze(0		)

   assert torch.allclose(__lowerCAmelCase      ,    __lowerCAmelCase		)

   snake_case 						=   model(__lowerCAmelCase		)
   snake_case 						=   outputs.logits

   snake_case 						=   (1, 10_00)

   if "l1" in model_name:
      snake_case 						=   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]      ,    __lowerCAmelCase      ,    atol=1e-3		)
      assert logits.shape == expected_shape
   elif "l3" in model_name:
      snake_case 						=   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]      ,    __lowerCAmelCase      ,    atol=1e-3		)
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      snake_case 						=   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(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
   model.save_pretrained(__lowerCAmelCase		)
   print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''		)
   processor.save_pretrained(__lowerCAmelCase		)
   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=__lowerCAmelCase      ,    )
      processor.push_to_hub(
          repo_id=F'''Bearnardd/{pytorch_dump_path}'''      ,    commit_message="""Add image processor"""      ,    use_temp_dir=__lowerCAmelCase      ,    )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			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)

 _SCREAMING_SNAKE_CASE							     =			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'''




import os
from collections.abc import Iterator





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "."		)		->  Iterator[str]:
   for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase		):
      snake_case 						=   [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
      for filename in filenames:
         if filename == "__init__.py":
            continue
         if os.path.splitext(__lowerCAmelCase		)[1] in (".py", ".ipynb"):
            yield os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		).lstrip("""./"""		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]		)		->  Any:
   return F'''{i * "  "}*''' if i else "\n##"





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							str		)		->  str:
   snake_case 						=   old_path.split(os.sep		)
   for i, new_part in enumerate(new_path.split(os.sep		)		):
      if (i + 1 > len(__lowerCAmelCase		) or old_parts[i] != new_part) and new_part:
         print(F'''{md_prefix(__lowerCAmelCase		)} {new_part.replace("_"      ,    " "		).title()}'''		)
   return new_path





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "."		)		->  None:
   snake_case 						=   """"""
   for filepath in sorted(good_file_paths(__lowerCAmelCase		)		):
      snake_case						,    snake_case 						=   os.path.split(__lowerCAmelCase		)
      if filepath != old_path:
         snake_case 						=   print_path(__lowerCAmelCase      ,    __lowerCAmelCase		)
      snake_case 						=   (filepath.count(os.sep		) + 1) if filepath else 0
      snake_case 						=   F'''{filepath}/{filename}'''.replace(""" """      ,    """%20"""		)
      snake_case 						=   os.path.splitext(filename.replace("""_"""      ,    """ """		).title()		)[0]
      print(F'''{md_prefix(__lowerCAmelCase		)} [{filename}]({url})'''		)


if __name__ == "__main__":
 print_directory_md(".")

