


"""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		(		lowercase    :    Union[str, Any]		,				lowercase    :    Tuple					)      ->		Union[str, Any]:


    """simple docstring"""





    snake_case       : Any    =       old_name

    if "patch_embed" in old_name:
        snake_case   ,snake_case   ,snake_case       : Optional[int]    =       old_name.split("."					)

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

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

            snake_case       : str    =       "last_stage." + trimmed_name

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

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

    return new_name







def     __lowerCAmelCase		(		lowercase    :    int		,				lowercase    :    Optional[int]					)      ->		Dict:


    """simple docstring"""





    for key in checkpoint.copy().keys():
        snake_case       : Optional[int]    =       checkpoint.pop(lowercase					)
        snake_case       : Dict    =       val

    return checkpoint







def     __lowerCAmelCase		(		)      ->		Any:


    """simple docstring"""





    snake_case       : Dict    =       "http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case       : Tuple    =       Image.open(requests.get(lowercase		,				stream=lowercase					).raw					)

    return image







def     __lowerCAmelCase		(		lowercase    :    Path		,				lowercase    :    Path		,				lowercase    :    Path		,				lowercase    :    bool					)      ->		Optional[int]:


    """simple docstring"""





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

    snake_case       : Union[str, Any]    =       config.depths[-1] - config.num_metaad_blocks + 1
    snake_case       : Any    =       convert_torch_checkpoint(lowercase		,				lowercase					)

    model.load_state_dict(lowercase					)
    model.eval()

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

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

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

    assert torch.allclose(lowercase		,				lowercase					)

    snake_case       : List[str]    =       model(lowercase					)
    snake_case       : str    =       outputs.logits

    snake_case       : Optional[int]    =       (1, 1000)

    if "l1" in model_name:
        snake_case       : Dict    =       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:
        snake_case       : int    =       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:
        snake_case       : str    =       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__":
  __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)

  __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 string







def     __lowerCAmelCase		(		lowercase    :    str					)      ->		None:


    """simple docstring"""





    for key in range(len(string.ascii_uppercase					)					):
        snake_case       : Dict    =       ""
        for symbol in message:
            if symbol in string.ascii_uppercase:
                snake_case       : Dict    =       string.ascii_uppercase.find(lowercase					)
                snake_case       : Optional[int]    =       num - key
                if num < 0:
                    snake_case       : Optional[int]    =       num + len(string.ascii_uppercase					)
                snake_case       : Dict    =       translated + string.ascii_uppercase[num]
            else:
                snake_case       : Any    =       translated + symbol
        print(F'Decryption using Key #{key}: {translated}'					)







def     __lowerCAmelCase		(		)      ->		None:


    """simple docstring"""





    snake_case       : Dict    =       input("Encrypted message: "					)
    snake_case       : Any    =       message.upper()
    decrypt(lowercase					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()





