





"""simple docstring"""





import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
__A											=		logging.get_logger()






@dataclass
class 					_lowerCAmelCase   :
       """simple docstring"""




       __magic_name__     :nn.Module
       __magic_name__     :List[nn.Module]		  =					field(default_factory=lowerCamelCase__       )
       __magic_name__     :list		  =					field(default_factory=lowerCamelCase__       )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							len(list(m.modules()      )      ) == 1 or isinstance(lowerCAmelCase__    , nn.Convad      ) or isinstance(lowerCAmelCase__    , nn.BatchNormad      )
         if has_not_submodules:
           self.traced.append(lowerCAmelCase__      )






       def __call__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         for m in self.module.modules():
           self.handles.append(m.register_forward_hook(self._forward_hook      )      )
         self.module(lowerCAmelCase__      )
         [x.remove() for x in self.handles]
         return self






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         return list(filter(lambda __UpperCAmelCase      : len(list(x.state_dict().keys()      )      ) > 0    , self.traced      )      )






@dataclass
class 					_lowerCAmelCase   :
       """simple docstring"""




       __magic_name__     :nn.Module
       __magic_name__     :nn.Module
       __magic_name__     :int		  =					1
       __magic_name__     :List		  =					field(default_factory=lowerCamelCase__       )
       __magic_name__     :List		  =					field(default_factory=lowerCamelCase__       )
       __magic_name__     :bool		  =					True






       def __call__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							Tracker(self.dest      )(lowerCAmelCase__      ).parametrized
         lowerCAmelCase__			:Dict											=							Tracker(self.src      )(lowerCAmelCase__      ).parametrized

         lowerCAmelCase__			:Any											=							list(filter(lambda __UpperCAmelCase      : type(lowerCAmelCase__      ) not in self.src_skip    , lowerCAmelCase__      )      )
         lowerCAmelCase__			:Tuple											=							list(filter(lambda __UpperCAmelCase      : type(lowerCAmelCase__      ) not in self.dest_skip    , lowerCAmelCase__      )      )

         if len(lowerCAmelCase__      ) != len(lowerCAmelCase__      ) and self.raise_if_mismatch:
           raise Exception(
               F"Numbers of operations are different. Source module has {len(lowerCAmelCase__      )} operations while"
               F" destination module has {len(lowerCAmelCase__      )}."      )

         for dest_m, src_m in zip(lowerCAmelCase__    , lowerCAmelCase__      ):
           dest_m.load_state_dict(src_m.state_dict()      )
           if self.verbose == 1:
             print(F"Transfered from={src_m} to={dest_m}"      )






class 					_lowerCAmelCase   (     nn.Module       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         super().__init__()

         lowerCAmelCase__			:List[Tuple[str, nn.Module]]											=							[]
         # - get the stem
         feature_blocks.append(('conv1', model.stem)      )
         # - get all the feature blocks
         for k, v in model.trunk_output.named_children():
           assert k.startswith('block'      ), F"Unexpected layer name {k}"
           lowerCAmelCase__			:Any											=							len(lowerCAmelCase__      ) + 1
           feature_blocks.append((F"res{block_index}", v)      )

         lowerCAmelCase__			:Optional[Any]											=							nn.ModuleDict(lowerCAmelCase__      )






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         return get_trunk_forward_outputs(
             lowerCAmelCase__    , out_feat_keys=lowerCAmelCase__    , feature_blocks=self._feature_blocks    , )






class 					_lowerCAmelCase   (     lowerCamelCase__       ):
       """simple docstring"""




       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							x.split('-'      )
         return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]      )






       def __getitem__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         if x not in self:
           lowerCAmelCase__			:Tuple											=							self.convert_name_to_timm(lowerCAmelCase__      )
           lowerCAmelCase__			:int											=							partial(lambda: (timm.create_model(lowerCAmelCase__    , pretrained=lowerCAmelCase__      ).eval(), None)      )

         else:
           lowerCAmelCase__			:Dict											=							super().__getitem__(lowerCAmelCase__      )

         return val






class 					_lowerCAmelCase   (     lowerCamelCase__       ):
       """simple docstring"""




       def __getitem__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         if "seer" in x and "in1k" not in x:
           lowerCAmelCase__			:Optional[int]											=							RegNetModel
         else:
           lowerCAmelCase__			:int											=							RegNetForImageClassification
         return val






def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Optional[Any]:





  """simple docstring"""


  for from_key, to_key in keys:
    lowerCAmelCase__			:Any											=							from_state_dict[from_key].clone()
    print(F"Copied key={from_key} to={to_key}"			)
  return to_state_dict
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE = True     ,   )					->List[str]:





  """simple docstring"""


  print(F"Converting {name}..."			)
  with torch.no_grad():
    lowerCAmelCase__			:int											=							from_model_func()
    lowerCAmelCase__			:int											=							our_model_func(_lowerCamelCase			).eval()
    lowerCAmelCase__			:Optional[int]											=							ModuleTransfer(src=_lowerCamelCase     ,   dest=_lowerCamelCase     ,   raise_if_mismatch=_lowerCamelCase			)
    lowerCAmelCase__			:str											=							torch.randn((1, 3, 224, 224)			)
    module_transfer(_lowerCamelCase			)

  if from_state_dict is not None:
    lowerCAmelCase__			:str											=							[]
    # for seer - in1k finetuned we have to manually copy the head
    if "seer" in name and "in1k" in name:
      lowerCAmelCase__			:List[str]											=							[("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
    lowerCAmelCase__			:List[str]											=							manually_copy_vissl_head(_lowerCamelCase     ,   our_model.state_dict()     ,   _lowerCamelCase			)
    our_model.load_state_dict(_lowerCamelCase			)

  lowerCAmelCase__			:str											=							our_model(_lowerCamelCase     ,   output_hidden_states=_lowerCamelCase			)
  lowerCAmelCase__			:str											=							(
      our_outputs.logits if isinstance(_lowerCamelCase     ,   _lowerCamelCase			) else our_outputs.last_hidden_state
  )

  lowerCAmelCase__			:str											=							from_model(_lowerCamelCase			)
  lowerCAmelCase__			:List[str]											=							from_output[-1] if type(_lowerCamelCase			) is list else from_output

  # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
  if "seer" in name and "in1k" in name:
    lowerCAmelCase__			:Dict											=							our_outputs.hidden_states[-1]

  assert torch.allclose(_lowerCamelCase     ,   _lowerCamelCase			), "The model logits don't match the original one."

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / name     ,   commit_message='Add model'     ,   use_temp_dir=_lowerCamelCase     ,   )

    lowerCAmelCase__			:List[Any]											=							224 if """seer""" not in name else 384
    # we can use the convnext one
    lowerCAmelCase__			:Any											=							AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'     ,   size=_lowerCamelCase			)
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / name     ,   commit_message='Add image processor'     ,   use_temp_dir=_lowerCamelCase     ,   )

    print(F"Pushed {name}"			)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE = None     ,   _SCREAMING_SNAKE_CASE = True			)					->List[str]:





  """simple docstring"""


  lowerCAmelCase__			:List[str]											=							"""imagenet-1k-id2label.json"""
  lowerCAmelCase__			:Optional[int]											=							1000
  lowerCAmelCase__			:str											=							(1, num_labels)

  lowerCAmelCase__			:Tuple											=							"""huggingface/label-files"""
  lowerCAmelCase__			:Union[str, Any]											=							num_labels
  lowerCAmelCase__			:Tuple											=							json.load(open(cached_download(hf_hub_url(_lowerCamelCase     ,   _lowerCamelCase     ,   repo_type='dataset'			)			)     ,   'r'			)			)
  lowerCAmelCase__			:List[Any]											=							{int(_lowerCamelCase			): v for k, v in idalabel.items()}

  lowerCAmelCase__			:Dict											=							idalabel
  lowerCAmelCase__			:Union[str, Any]											=							{v: k for k, v in idalabel.items()}

  lowerCAmelCase__			:Optional[int]											=							partial(_lowerCamelCase     ,   num_labels=_lowerCamelCase     ,   idalabel=_lowerCamelCase     ,   labelaid=_lowerCamelCase			)

  lowerCAmelCase__			:int											=							{
      """regnet-x-002""": ImageNetPreTrainedConfig(
          depths=[1, 1, 4, 7]     ,   hidden_sizes=[24, 56, 152, 368]     ,   groups_width=8     ,   layer_type='x'			),
      """regnet-x-004""": ImageNetPreTrainedConfig(
          depths=[1, 2, 7, 12]     ,   hidden_sizes=[32, 64, 160, 384]     ,   groups_width=16     ,   layer_type='x'			),
      """regnet-x-006""": ImageNetPreTrainedConfig(
          depths=[1, 3, 5, 7]     ,   hidden_sizes=[48, 96, 240, 528]     ,   groups_width=24     ,   layer_type='x'			),
      """regnet-x-008""": ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 5]     ,   hidden_sizes=[64, 128, 288, 672]     ,   groups_width=16     ,   layer_type='x'			),
      """regnet-x-016""": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 2]     ,   hidden_sizes=[72, 168, 408, 912]     ,   groups_width=24     ,   layer_type='x'			),
      """regnet-x-032""": ImageNetPreTrainedConfig(
          depths=[2, 6, 15, 2]     ,   hidden_sizes=[96, 192, 432, 1008]     ,   groups_width=48     ,   layer_type='x'			),
      """regnet-x-040""": ImageNetPreTrainedConfig(
          depths=[2, 5, 14, 2]     ,   hidden_sizes=[80, 240, 560, 1360]     ,   groups_width=40     ,   layer_type='x'			),
      """regnet-x-064""": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1]     ,   hidden_sizes=[168, 392, 784, 1624]     ,   groups_width=56     ,   layer_type='x'			),
      """regnet-x-080""": ImageNetPreTrainedConfig(
          depths=[2, 5, 15, 1]     ,   hidden_sizes=[80, 240, 720, 1920]     ,   groups_width=120     ,   layer_type='x'			),
      """regnet-x-120""": ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1]     ,   hidden_sizes=[224, 448, 896, 2240]     ,   groups_width=112     ,   layer_type='x'			),
      """regnet-x-160""": ImageNetPreTrainedConfig(
          depths=[2, 6, 13, 1]     ,   hidden_sizes=[256, 512, 896, 2048]     ,   groups_width=128     ,   layer_type='x'			),
      """regnet-x-320""": ImageNetPreTrainedConfig(
          depths=[2, 7, 13, 1]     ,   hidden_sizes=[336, 672, 1344, 2520]     ,   groups_width=168     ,   layer_type='x'			),
      # y variant
      """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]     ,   hidden_sizes=[24, 56, 152, 368]     ,   groups_width=8			),
      """regnet-y-004""": ImageNetPreTrainedConfig(
          depths=[1, 3, 6, 6]     ,   hidden_sizes=[48, 104, 208, 440]     ,   groups_width=8			),
      """regnet-y-006""": ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 4]     ,   hidden_sizes=[48, 112, 256, 608]     ,   groups_width=16			),
      """regnet-y-008""": ImageNetPreTrainedConfig(
          depths=[1, 3, 8, 2]     ,   hidden_sizes=[64, 128, 320, 768]     ,   groups_width=16			),
      """regnet-y-016""": ImageNetPreTrainedConfig(
          depths=[2, 6, 17, 2]     ,   hidden_sizes=[48, 120, 336, 888]     ,   groups_width=24			),
      """regnet-y-032""": ImageNetPreTrainedConfig(
          depths=[2, 5, 13, 1]     ,   hidden_sizes=[72, 216, 576, 1512]     ,   groups_width=24			),
      """regnet-y-040""": ImageNetPreTrainedConfig(
          depths=[2, 6, 12, 2]     ,   hidden_sizes=[128, 192, 512, 1088]     ,   groups_width=64			),
      """regnet-y-064""": ImageNetPreTrainedConfig(
          depths=[2, 7, 14, 2]     ,   hidden_sizes=[144, 288, 576, 1296]     ,   groups_width=72			),
      """regnet-y-080""": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1]     ,   hidden_sizes=[168, 448, 896, 2016]     ,   groups_width=56			),
      """regnet-y-120""": ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1]     ,   hidden_sizes=[224, 448, 896, 2240]     ,   groups_width=112			),
      """regnet-y-160""": ImageNetPreTrainedConfig(
          depths=[2, 4, 11, 1]     ,   hidden_sizes=[224, 448, 1232, 3024]     ,   groups_width=112			),
      """regnet-y-320""": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]     ,   hidden_sizes=[232, 696, 1392, 3712]     ,   groups_width=232			),
      # models created by SEER -> https://arxiv.org/abs/2202.08360
      """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]     ,   hidden_sizes=[232, 696, 1392, 3712]     ,   groups_width=232			),
      """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]     ,   hidden_sizes=[328, 984, 1968, 4920]     ,   groups_width=328			),
      """regnet-y-1280-seer""": RegNetConfig(
          depths=[2, 7, 17, 1]     ,   hidden_sizes=[528, 1056, 2904, 7392]     ,   groups_width=264			),
      """regnet-y-2560-seer""": RegNetConfig(
          depths=[3, 7, 16, 1]     ,   hidden_sizes=[640, 1696, 2544, 5088]     ,   groups_width=640			),
      """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]     ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]     ,   groups_width=1010			),
      # finetuned on imagenet
      """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]     ,   hidden_sizes=[232, 696, 1392, 3712]     ,   groups_width=232			),
      """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]     ,   hidden_sizes=[328, 984, 1968, 4920]     ,   groups_width=328			),
      """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]     ,   hidden_sizes=[528, 1056, 2904, 7392]     ,   groups_width=264			),
      """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[3, 7, 16, 1]     ,   hidden_sizes=[640, 1696, 2544, 5088]     ,   groups_width=640			),
      """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]     ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]     ,   groups_width=1010			),
  }

  lowerCAmelCase__			:List[Any]											=							NameToOurModelFuncMap()
  lowerCAmelCase__			:str											=							NameToFromModelFuncMap()
  # add seer weights logic

  def load_using_classy_vision(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			) -> Tuple[nn.Module, Dict]:
    lowerCAmelCase__			:int											=							torch.hub.load_state_dict_from_url(_lowerCamelCase     ,   model_dir=str(_lowerCamelCase			)     ,   map_location='cpu'			)
    lowerCAmelCase__			:List[str]											=							model_func()
    # check if we have a head, if yes add it
    lowerCAmelCase__			:List[Any]											=							files["""classy_state_dict"""]["""base_model"""]["""model"""]
    lowerCAmelCase__			:List[Any]											=							model_state_dict["""trunk"""]
    model.load_state_dict(_lowerCamelCase			)
    return model.eval(), model_state_dict["heads"]

  # pretrained
  lowerCAmelCase__			:Any											=							partial(
      _lowerCamelCase     ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'     ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()			)     ,   )

  lowerCAmelCase__			:str											=							partial(
      _lowerCamelCase     ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'     ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()			)     ,   )

  lowerCAmelCase__			:Optional[int]											=							partial(
      _lowerCamelCase     ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'     ,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()			)     ,   )

  lowerCAmelCase__			:int											=							partial(
      _lowerCamelCase     ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'     ,   lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27     ,   group_width=1010     ,   w_a=1744     ,   w_a=620.83     ,   w_m=2.5_2			)			)			)     ,   )

  # IN1K finetuned
  lowerCAmelCase__			:List[Any]											=							partial(
      _lowerCamelCase     ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()			)     ,   )

  lowerCAmelCase__			:int											=							partial(
      _lowerCamelCase     ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()			)     ,   )

  lowerCAmelCase__			:str											=							partial(
      _lowerCamelCase     ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()			)     ,   )

  lowerCAmelCase__			:Optional[int]											=							partial(
      _lowerCamelCase     ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'     ,   lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27     ,   group_width=1010     ,   w_a=1744     ,   w_a=620.83     ,   w_m=2.5_2			)			)			)     ,   )

  if model_name:
    convert_weight_and_push(
        _lowerCamelCase     ,   names_to_from_model_map[model_name]     ,   names_to_ours_model_map[model_name]     ,   names_to_config[model_name]     ,   _lowerCamelCase     ,   _lowerCamelCase     ,   )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(
          _lowerCamelCase     ,   names_to_from_model_map[model_name]     ,   names_to_ours_model_map[model_name]     ,   _lowerCamelCase     ,   _lowerCamelCase     ,   _lowerCamelCase     ,   )
  return config, expected_shape


if __name__ == "__main__":
  __A											=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default=None,
      type=str,
      help=(
          """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
          """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=Path,
      required=True,
      help="""Path to the output PyTorch model directory.""",
  )
  parser.add_argument(
      """--push_to_hub""",
      default=True,
      type=bool,
      required=False,
      help="""If True, push model and image processor to the hub.""",
  )

  __A											=		parser.parse_args()

  __A											=		args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





'''simple docstring'''




import string


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		Dict           =		""""""
		for i in sequence:
				__SCREAMING_SNAKE_CASE       :		Any           =		ord(_lowerCamelCase					)
				if 65 <= extract <= 90:
						output += chr(1_55 - extract					)
				elif 97 <= extract <= 1_22:
						output += chr(2_19 - extract					)
				else:
						output += i
		return output


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		string.ascii_letters
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
		return "".join(
		    letters_reversed[letters.index(_lowerCamelCase					)] if c in letters else c for c in sequence					)


def 				lowerCAmelCase_   (	):
		from timeit import timeit

		print("""Running performance benchmarks..."""					)
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		"""from string import printable ; from __main__ import atbash, atbash_slow"""
		print(F"> atbash_slow(): {timeit('atbash_slow(printable)'       ,      setup=_lowerCamelCase					)} seconds"					)
		print(F">      atbash(): {timeit('atbash(printable)'       ,      setup=_lowerCamelCase					)} seconds"					)


if __name__ == "__main__":
						for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
												print(f"{example} encrypted in atbash: {atbash(example)}")
						benchmark()