


from __future__ import annotations

snake_case				:    List[Any]       				=     tuple[int, int, int]
snake_case				:    Union[str, Any]       				=     tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
snake_case				:    Optional[Any]       				=     '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''

# -------------------------- default selection --------------------------
# rotors --------------------------
snake_case				:    Optional[Any]       				=     '''EGZWVONAHDCLFQMSIPJBYUKXTR'''
snake_case				:    str       				=     '''FOBHMDKEXQNRAULPGSJVTYICZW'''
snake_case				:    List[Any]       				=     '''ZJXESIUQLHAVRMDOYGTNFWPBKC'''
# reflector --------------------------
snake_case				:    Dict       				=     {
    '''A''': '''N''',
    '''N''': '''A''',
    '''B''': '''O''',
    '''O''': '''B''',
    '''C''': '''P''',
    '''P''': '''C''',
    '''D''': '''Q''',
    '''Q''': '''D''',
    '''E''': '''R''',
    '''R''': '''E''',
    '''F''': '''S''',
    '''S''': '''F''',
    '''G''': '''T''',
    '''T''': '''G''',
    '''H''': '''U''',
    '''U''': '''H''',
    '''I''': '''V''',
    '''V''': '''I''',
    '''J''': '''W''',
    '''W''': '''J''',
    '''K''': '''X''',
    '''X''': '''K''',
    '''L''': '''Y''',
    '''Y''': '''L''',
    '''M''': '''Z''',
    '''Z''': '''M''',
}

# -------------------------- extra rotors --------------------------
snake_case				:    int       				=     '''RMDJXFUWGISLHVTCQNKYPBEZOA'''
snake_case				:    int       				=     '''SGLCPQWZHKXAREONTFBVIYJUDM'''
snake_case				:    List[str]       				=     '''HVSICLTYKQUBXDWAJZOMFGPREN'''
snake_case				:    Any       				=     '''RZWQHFMVDBKICJLNTUXAGYPSOE'''
snake_case				:    int       				=     '''LFKIJODBEGAMQPXVUHYSTCZRWN'''
snake_case				:    Dict       				=     '''KOAEGVDHXPQZMLFTYWJNBRCIUS'''
def 				__lowerCamelCase						( UpperCAmelCase_						:		RotorPositionT ,  UpperCAmelCase_						:		RotorSelectionT ,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      if (unique_rotsel := len(set(UpperCAmelCase_						)						)) < 3:
            a						:str 							=       F'''Please use 3 unique rotors (not {unique_rotsel})'''
            raise Exception(UpperCAmelCase_						)

      # Checks if rotor positions are valid
      a							,				a							,				a						:Optional[int] 							=       rotpos
      if not 0 < rotorposa <= len(UpperCAmelCase_						):
            a						:int 							=       F'''First rotor position is not within range of 1..26 ({rotorposa}'''
            raise ValueError(UpperCAmelCase_						)
      if not 0 < rotorposa <= len(UpperCAmelCase_						):
            a						:int 							=       F'''Second rotor position is not within range of 1..26 ({rotorposa})'''
            raise ValueError(UpperCAmelCase_						)
      if not 0 < rotorposa <= len(UpperCAmelCase_						):
            a						:Optional[Any] 							=       F'''Third rotor position is not within range of 1..26 ({rotorposa})'''
            raise ValueError(UpperCAmelCase_						)

      # Validates string and returns dict
      a						:Dict 							=       _plugboard(UpperCAmelCase_						)

      return rotpos, rotsel, pbdict
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      if not isinstance(UpperCAmelCase_ ,  UpperCAmelCase_						):
            a						:str 							=       F'''Plugboard setting isn\'t type string ({type(UpperCAmelCase_						)})'''
            raise TypeError(UpperCAmelCase_						)
      elif len(UpperCAmelCase_						) % 2 != 0:
            a						:Any 							=       F'''Odd number of symbols ({len(UpperCAmelCase_						)})'''
            raise Exception(UpperCAmelCase_						)
      elif pbstring == "":
            return {}

      pbstring.replace(''' ''' ,  ''''''						)

      # Checks if all characters are unique
      a						:Tuple 							=       set()
      for i in pbstring:
            if i not in abc:
                  a						:Dict 							=       F'''\'{i}\' not in list of symbols'''
                  raise Exception(UpperCAmelCase_						)
            elif i in tmppbl:
                  a						:List[Any] 							=       F'''Duplicate symbol ({i})'''
                  raise Exception(UpperCAmelCase_						)
            else:
                  tmppbl.add(UpperCAmelCase_						)
      del tmppbl

      # Created the dictionary
      a						:Optional[Any] 							=       {}
      for j in range(0 ,  len(UpperCAmelCase_						) - 1 ,  2						):
            a						:Any 							=       pbstring[j + 1]
            a						:Optional[int] 							=       pbstring[j]

      return pb
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		RotorPositionT ,  UpperCAmelCase_						:		RotorSelectionT = (rotora, rotora, rotora) ,  UpperCAmelCase_						:		str = "" ,  ):




      """simple docstring"""





      a						:Tuple 							=       text.upper()
      a							,				a							,				a						:Union[str, Any] 							=       _validator(
          UpperCAmelCase_ ,  UpperCAmelCase_ ,  plugb.upper()						)

      a							,				a							,				a						:Optional[Any] 							=       rotor_position
      a							,				a							,				a						:List[str] 							=       rotor_selection
      rotorposa -= 1
      rotorposa -= 1
      rotorposa -= 1

      a						:Union[str, Any] 							=       []

      # encryption/decryption process --------------------------
      for symbol in text:
            if symbol in abc:
                  # 1st plugboard --------------------------
                  if symbol in plugboard:
                        a						:List[str] 							=       plugboard[symbol]

                  # rotor ra --------------------------
                  a						:Dict 							=       abc.index(UpperCAmelCase_						) + rotorposa
                  a						:List[str] 							=       rotora[index % len(UpperCAmelCase_						)]

                  # rotor rb --------------------------
                  a						:List[str] 							=       abc.index(UpperCAmelCase_						) + rotorposa
                  a						:Tuple 							=       rotora[index % len(UpperCAmelCase_						)]

                  # rotor rc --------------------------
                  a						:List[Any] 							=       abc.index(UpperCAmelCase_						) + rotorposa
                  a						:List[Any] 							=       rotora[index % len(UpperCAmelCase_						)]

                  # reflector --------------------------
                  # this is the reason you don't need another machine to decipher

                  a						:Optional[Any] 							=       reflector[symbol]

                  # 2nd rotors
                  a						:Dict 							=       abc[rotora.index(UpperCAmelCase_						) - rotorposa]
                  a						:Any 							=       abc[rotora.index(UpperCAmelCase_						) - rotorposa]
                  a						:Optional[int] 							=       abc[rotora.index(UpperCAmelCase_						) - rotorposa]

                  # 2nd plugboard
                  if symbol in plugboard:
                        a						:Optional[Any] 							=       plugboard[symbol]

                  # moves/resets rotor positions
                  rotorposa += 1
                  if rotorposa >= len(UpperCAmelCase_						):
                        a						:str 							=       0
                        rotorposa += 1
                  if rotorposa >= len(UpperCAmelCase_						):
                        a						:List[str] 							=       0
                        rotorposa += 1
                  if rotorposa >= len(UpperCAmelCase_						):
                        a						:Any 							=       0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
            result.append(UpperCAmelCase_						)

      return "".join(UpperCAmelCase_						)


if __name__ == "__main__":
     snake_case				:    int       				=     '''This is my Python script that emulates the Enigma machine from WWII.'''
     snake_case				:    Union[str, Any]       				=     (1, 1, 1)
     snake_case				:    Union[str, Any]       				=     '''pictures'''
     snake_case				:    Optional[int]       				=     (rotora, rotora, rotora)
     snake_case				:    Dict       				=     enigma(message, rotor_pos, rotor_sel, pb)

     print('''Encrypted message:''', en)
     print('''Decrypted message:''', enigma(en, rotor_pos, rotor_sel, pb))









'''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					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					old_name

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

				if layer == "0":
						_UpperCAmelCase						:				List[str]	    =					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						:				Tuple	    =					old_name.replace("4"					,       "batchnorm_after"      )

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

						_UpperCAmelCase						:				Optional[Any]	    =					"last_stage." + trimmed_name

		elif "network" in old_name and re.search(R".\d."					,       __lowerCAmelCase      ):
				_UpperCAmelCase						:				Optional[Any]	    =					old_name.replace("network"					,       "intermediate_stages"      )

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

		return new_name






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		for key in checkpoint.copy().keys():
				_UpperCAmelCase						:				List[Any]	    =					checkpoint.pop(__lowerCAmelCase      )
				_UpperCAmelCase						:				List[Any]	    =					val

		return checkpoint






def        __lowerCAmelCase       ():
		_UpperCAmelCase						:				Optional[int]	    =					"http://images.cocodataset.org/val2017/000000039769.jpg"
		_UpperCAmelCase						:				Tuple	    =					Image.open(requests.get(__lowerCAmelCase					,       stream=__lowerCAmelCase      ).raw      )

		return image






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					torch.load(__lowerCAmelCase					,       map_location="cpu"      )["model"]
		_UpperCAmelCase						:				Dict	    =					EfficientFormerConfig.from_json_file(__lowerCAmelCase      )
		_UpperCAmelCase						:				Optional[Any]	    =					EfficientFormerForImageClassificationWithTeacher(__lowerCAmelCase      )
		_UpperCAmelCase						:				Tuple	    =					"_".join(checkpoint_path.split("/"      )[-1].split("."      )[0].split("_"      )[:-1]      )

		_UpperCAmelCase						:				Union[str, Any]	    =					config.depths[-1] - config.num_metaad_blocks + 1
		_UpperCAmelCase						:				Optional[int]	    =					convert_torch_checkpoint(__lowerCAmelCase					,       __lowerCAmelCase      )

		model.load_state_dict(__lowerCAmelCase      )
		model.eval()

		_UpperCAmelCase						:				Optional[Any]	    =					{
		    "bilinear": PILImageResampling.BILINEAR,
		    "bicubic": PILImageResampling.BICUBIC,
		    "nearest": PILImageResampling.NEAREST,
		}

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

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

		assert torch.allclose(__lowerCAmelCase					,       __lowerCAmelCase      )

		_UpperCAmelCase						:				Union[str, Any]	    =					model(__lowerCAmelCase      )
		_UpperCAmelCase						:				Dict	    =					outputs.logits

		_UpperCAmelCase						:				Optional[int]	    =					(1, 1_000)

		if "l1" in model_name:
				_UpperCAmelCase						:				List[Any]	    =					torch.Tensor(
				    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]      )
				assert torch.allclose(logits[0, :10]					,       __lowerCAmelCase					,       atol=1e-3      )
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				_UpperCAmelCase						:				List[Any]	    =					torch.Tensor(
				    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]      )
				assert torch.allclose(logits[0, :10]					,       __lowerCAmelCase					,       atol=1e-3      )
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				_UpperCAmelCase						:				List[Any]	    =					torch.Tensor(
				    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]      )
				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__":
						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,
						)



