
"""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       A_      (       lowercase    ,			lowercase   )  ->				Optional[Any]:


     """simple docstring"""







     UpperCAmelCase_    :     str            =	old_name

     if "patch_embed" in old_name:
          UpperCAmelCase_       ,UpperCAmelCase_       ,UpperCAmelCase_    :     Optional[Any]            =	old_name.split("""."""   )

          if layer == "0":
               UpperCAmelCase_    :     Tuple            =	old_name.replace("""0"""    ,			"""convolution1"""   )
          elif layer == "1":
               UpperCAmelCase_    :     Dict            =	old_name.replace("""1"""    ,			"""batchnorm_before"""   )
          elif layer == "3":
               UpperCAmelCase_    :     Tuple            =	old_name.replace("""3"""    ,			"""convolution2"""   )
          else:
               UpperCAmelCase_    :     List[str]            =	old_name.replace("""4"""    ,			"""batchnorm_after"""   )

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

               UpperCAmelCase_    :     Union[str, Any]            =	"""last_stage.""" + trimmed_name

     elif "network" in old_name and re.search(R""".\d."""    ,			lowercase   ):
          UpperCAmelCase_    :     Optional[int]            =	old_name.replace("""network"""    ,			"""intermediate_stages"""   )

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

     return new_name




def       A_      (       lowercase    ,			lowercase   )  ->				Tuple:


     """simple docstring"""







     for key in checkpoint.copy().keys():
          UpperCAmelCase_    :     List[Any]            =	checkpoint.pop(lowercase   )
          UpperCAmelCase_    :     List[Any]            =	val

     return checkpoint




def       A_      (       )  ->				Tuple:


     """simple docstring"""







     UpperCAmelCase_    :     List[Any]            =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     UpperCAmelCase_    :     str            =	Image.open(requests.get(lowercase    ,			stream=lowercase   ).raw   )

     return image




def       A_      (       lowercase    ,			lowercase    ,			lowercase    ,			lowercase   )  ->				Any:


     """simple docstring"""







     UpperCAmelCase_    :     Any            =	torch.load(lowercase    ,			map_location="""cpu"""   )["""model"""]
     UpperCAmelCase_    :     Union[str, Any]            =	EfficientFormerConfig.from_json_file(lowercase   )
     UpperCAmelCase_    :     Union[str, Any]            =	EfficientFormerForImageClassificationWithTeacher(lowercase   )
     UpperCAmelCase_    :     List[str]            =	"""_""".join(checkpoint_path.split("""/"""   )[-1].split("""."""   )[0].split("""_"""   )[:-1]   )

     UpperCAmelCase_    :     Optional[int]            =	config.depths[-1] - config.num_metaad_blocks + 1
     UpperCAmelCase_    :     Optional[Any]            =	convert_torch_checkpoint(lowercase    ,			lowercase   )

     model.load_state_dict(lowercase   )
     model.eval()

     UpperCAmelCase_    :     List[str]            =	{
         """bilinear""": PILImageResampling.BILINEAR,
         """bicubic""": PILImageResampling.BICUBIC,
         """nearest""": PILImageResampling.NEAREST,
     }

     # prepare image
     UpperCAmelCase_    :     Optional[Any]            =	prepare_img()
     UpperCAmelCase_    :     Optional[int]            =	256
     UpperCAmelCase_    :     List[str]            =	224
     UpperCAmelCase_    :     Union[str, Any]            =	EfficientFormerImageProcessor(
         size={"""shortest_edge""": image_size}    ,			crop_size={"""height""": crop_size, """width""": crop_size}    ,			resample=pillow_resamplings["""bicubic"""]    ,			)
     UpperCAmelCase_    :     Optional[Any]            =	processor(images=lowercase    ,			return_tensors="""pt"""   ).pixel_values

     # original processing pipeline
     UpperCAmelCase_    :     List[Any]            =	Compose(
         [
             Resize(lowercase    ,			interpolation=pillow_resamplings["""bicubic"""]   ),
             CenterCrop(lowercase   ),
             ToTensor(),
             Normalize(lowercase    ,			lowercase   ),
         ]   )
     UpperCAmelCase_    :     List[str]            =	image_transforms(lowercase   ).unsqueeze(0   )

     assert torch.allclose(lowercase    ,			lowercase   )

     UpperCAmelCase_    :     str            =	model(lowercase   )
     UpperCAmelCase_    :     int            =	outputs.logits

     UpperCAmelCase_    :     Dict            =	(1, 1000)

     if "l1" in model_name:
          UpperCAmelCase_    :     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]    ,			lowercase    ,			atol=1E-3   )
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          UpperCAmelCase_    :     Dict            =	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]    ,			lowercase    ,			atol=1E-3   )
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          UpperCAmelCase_    :     Optional[Any]            =	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(lowercase   ).mkdir(exist_ok=lowercase   )
     model.save_pretrained(lowercase   )
     print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''   )
     processor.save_pretrained(lowercase   )
     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=lowercase    ,			)
          processor.push_to_hub(
              repo_id=f'''Bearnardd/{pytorch_dump_path}'''    ,			commit_message="""Add image processor"""    ,			use_temp_dir=lowercase    ,			)


if __name__ == "__main__":
       lowercase_         =       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)

       lowercase_         =       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       A_      (       lowercase   )  ->				int:


     """simple docstring"""







     return 1 if digit in (0, 1) else (digit * factorial(digit - 1   ))




def       A_      (       lowercase   )  ->				bool:


     """simple docstring"""







     UpperCAmelCase_    :     str            =	0
     UpperCAmelCase_    :     Union[str, Any]            =	number
     while duplicate > 0:
          UpperCAmelCase_       ,UpperCAmelCase_    :     Tuple            =	divmod(lowercase    ,			10   )
          fact_sum += factorial(lowercase   )
     return fact_sum == number


if __name__ == "__main__":
       print("Program to check whether a number is a Krisnamurthy Number or not.")
       lowercase_         =       int(input("Enter number: ").strip())
       print(
           f"""{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."""
       )

