



from __future__ import annotations

lowerCamelCase		       =      tuple[int, int, int]
lowerCamelCase		       =      tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
lowerCamelCase		       =      """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""

# -------------------------- default selection --------------------------
# rotors --------------------------
lowerCamelCase		       =      """EGZWVONAHDCLFQMSIPJBYUKXTR"""
lowerCamelCase		       =      """FOBHMDKEXQNRAULPGSJVTYICZW"""
lowerCamelCase		       =      """ZJXESIUQLHAVRMDOYGTNFWPBKC"""
# reflector --------------------------
lowerCamelCase		       =      {
    """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 --------------------------
lowerCamelCase		       =      """RMDJXFUWGISLHVTCQNKYPBEZOA"""
lowerCamelCase		       =      """SGLCPQWZHKXAREONTFBVIYJUDM"""
lowerCamelCase		       =      """HVSICLTYKQUBXDWAJZOMFGPREN"""
lowerCamelCase		       =      """RZWQHFMVDBKICJLNTUXAGYPSOE"""
lowerCamelCase		       =      """LFKIJODBEGAMQPXVUHYSTCZRWN"""
lowerCamelCase		       =      """KOAEGVDHXPQZMLFTYWJNBRCIUS"""






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
				# Checks if there are 3 unique rotors

				if (unique_rotsel := len(set(__lowerCAmelCase ) )) < 3:
								a__	:							Any      						= F'Please use 3 unique rotors (not {unique_rotsel})'
								raise Exception(__lowerCAmelCase )

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

				# Validates string and returns dict
				a__	:							Union[str, Any]      						= _plugboard(__lowerCAmelCase )

				return rotpos, rotsel, pbdict






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						dict[str, str]:

				# tests the input string if it
				# a) is type string
				# b) has even length (so pairs can be made)
				if not isinstance(__lowerCAmelCase	,				__lowerCAmelCase ):
								a__	:							Optional[Any]      						= F'Plugboard setting isn\'t type string ({type(__lowerCAmelCase )})'
								raise TypeError(__lowerCAmelCase )
				elif len(__lowerCAmelCase ) % 2 != 0:
								a__	:							Dict      						= F'Odd number of symbols ({len(__lowerCAmelCase )})'
								raise Exception(__lowerCAmelCase )
				elif pbstring == "":
								return {}

				pbstring.replace(" "	,				"" )

				# Checks if all characters are unique
				a__	:							List[str]      						= set()
				for i in pbstring:
								if i not in abc:
												a__	:							Union[str, Any]      						= F'\'{i}\' not in list of symbols'
												raise Exception(__lowerCAmelCase )
								elif i in tmppbl:
												a__	:							Optional[int]      						= F'Duplicate symbol ({i})'
												raise Exception(__lowerCAmelCase )
								else:
												tmppbl.add(__lowerCAmelCase )
				del tmppbl

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

				return pb






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = (rotora, rotora, rotora)	,				__UpperCamelCase = ""	,				)	->						str:
				a__	:							Optional[Any]      						= text.upper()
				a__	:							Tuple      						= _validator(
				    __lowerCAmelCase	,				__lowerCAmelCase	,				plugb.upper() )

				a__	:							Any      						= rotor_position
				a__	:							List[Any]      						= rotor_selection
				rotorposa -= 1
				rotorposa -= 1
				rotorposa -= 1

				a__	:							Tuple      						= []

				# encryption/decryption process --------------------------
				for symbol in text:
								if symbol in abc:
												# 1st plugboard --------------------------
												if symbol in plugboard:
																a__	:							int      						= plugboard[symbol]

												# rotor ra --------------------------
												a__	:							Any      						= abc.index(__lowerCAmelCase ) + rotorposa
												a__	:							List[str]      						= rotora[index % len(__lowerCAmelCase )]

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

												# rotor rc --------------------------
												a__	:							Any      						= abc.index(__lowerCAmelCase ) + rotorposa
												a__	:							Union[str, Any]      						= rotora[index % len(__lowerCAmelCase )]

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

												a__	:							str      						= reflector[symbol]

												# 2nd rotors
												a__	:							str      						= abc[rotora.index(__lowerCAmelCase ) - rotorposa]
												a__	:							str      						= abc[rotora.index(__lowerCAmelCase ) - rotorposa]
												a__	:							List[str]      						= abc[rotora.index(__lowerCAmelCase ) - rotorposa]

												# 2nd plugboard
												if symbol in plugboard:
																a__	:							str      						= plugboard[symbol]

												# moves/resets rotor positions
												rotorposa += 1
												if rotorposa >= len(__lowerCAmelCase ):
																a__	:							Optional[int]      						= 0
																rotorposa += 1
												if rotorposa >= len(__lowerCAmelCase ):
																a__	:							int      						= 0
																rotorposa += 1
												if rotorposa >= len(__lowerCAmelCase ):
																a__	:							List[Any]      						= 0

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

				return "".join(__lowerCAmelCase )


if __name__ == "__main__":
						lowerCamelCase		       =      """This is my Python script that emulates the Enigma machine from WWII."""
						lowerCamelCase		       =      (1, 1, 1)
						lowerCamelCase		       =      """pictures"""
						lowerCamelCase		       =      (rotora, rotora, rotora)
						lowerCamelCase		       =      enigma(message, rotor_pos, rotor_sel, pb)

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







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     str:
					# load base model
					SCREAMING_SNAKE_CASE							:     Dict						=				StableDiffusionPipeline.from_pretrained(__lowerCAmelCase      ,	torch_dtype=torch.floataa   )

					# load LoRA weight from .safetensors
					SCREAMING_SNAKE_CASE							:     Any						=				load_file(__lowerCAmelCase   )

					SCREAMING_SNAKE_CASE							:     Optional[int]						=				[]

					# directly update weight in diffusers model
					for key in state_dict:
										# it is suggested to print out the key, it usually will be something like below
										# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

										# as we have set the alpha beforehand, so just skip
										if ".alpha" in key or key in visited:
															continue

										if "text" in key:
															SCREAMING_SNAKE_CASE							:     Dict						=				key.split('.'   )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'   )[-1].split('_'   )
															SCREAMING_SNAKE_CASE							:     Tuple						=				pipeline.text_encoder
										else:
															SCREAMING_SNAKE_CASE							:     Any						=				key.split('.'   )[0].split(LORA_PREFIX_UNET + '_'   )[-1].split('_'   )
															SCREAMING_SNAKE_CASE							:     Dict						=				pipeline.unet

										# find the target layer
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				layer_infos.pop(0   )
										while len(__lowerCAmelCase   ) > -1:
															try:
																				SCREAMING_SNAKE_CASE							:     List[Any]						=				curr_layer.__getattr__(__lowerCAmelCase   )
																				if len(__lowerCAmelCase   ) > 0:
																									SCREAMING_SNAKE_CASE							:     Optional[int]						=				layer_infos.pop(0   )
																				elif len(__lowerCAmelCase   ) == 0:
																									break
															except Exception:
																				if len(__lowerCAmelCase   ) > 0:
																									temp_name += "_" + layer_infos.pop(0   )
																				else:
																									SCREAMING_SNAKE_CASE							:     List[str]						=				layer_infos.pop(0   )

										SCREAMING_SNAKE_CASE							:     str						=				[]
										if "lora_down" in key:
															pair_keys.append(key.replace('lora_down'      ,	'lora_up'   )   )
															pair_keys.append(__lowerCAmelCase   )
										else:
															pair_keys.append(__lowerCAmelCase   )
															pair_keys.append(key.replace('lora_up'      ,	'lora_down'   )   )

										# update weight
										if len(state_dict[pair_keys[0]].shape   ) == 4:
															SCREAMING_SNAKE_CASE							:     Any						=				state_dict[pair_keys[0]].squeeze(3   ).squeeze(2   ).to(torch.floataa   )
															SCREAMING_SNAKE_CASE							:     str						=				state_dict[pair_keys[1]].squeeze(3   ).squeeze(2   ).to(torch.floataa   )
															curr_layer.weight.data += alpha * torch.mm(__lowerCAmelCase      ,	__lowerCAmelCase   ).unsqueeze(2   ).unsqueeze(3   )
										else:
															SCREAMING_SNAKE_CASE							:     List[Any]						=				state_dict[pair_keys[0]].to(torch.floataa   )
															SCREAMING_SNAKE_CASE							:     Optional[int]						=				state_dict[pair_keys[1]].to(torch.floataa   )
															curr_layer.weight.data += alpha * torch.mm(__lowerCAmelCase      ,	__lowerCAmelCase   )

										# update visited list
										for item in pair_keys:
															visited.append(__lowerCAmelCase   )

					return pipeline


if __name__ == "__main__":
			_lowerCamelCase    :		Tuple		      =    argparse.ArgumentParser()

			parser.add_argument(
			    """--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
			)
			parser.add_argument(
			    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
			)
			parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
			parser.add_argument(
			    """--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
			)
			parser.add_argument(
			    """--lora_prefix_text_encoder""",
			    default="""lora_te""",
			    type=str,
			    help="""The prefix of text encoder weight in safetensors""",
			)
			parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
			parser.add_argument(
			    """--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
			)
			parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")

			_lowerCamelCase    :		List[Any]		      =    parser.parse_args()

			_lowerCamelCase    :		List[str]		      =    args.base_model_path
			_lowerCamelCase    :		str		      =    args.checkpoint_path
			_lowerCamelCase    :		Dict		      =    args.dump_path
			_lowerCamelCase    :		Any		      =    args.lora_prefix_unet
			_lowerCamelCase    :		List[Any]		      =    args.lora_prefix_text_encoder
			_lowerCamelCase    :		List[Any]		      =    args.alpha

			_lowerCamelCase    :		Optional[Any]		      =    convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

			_lowerCamelCase    :		Optional[Any]		      =    pipe.to(args.device)
			pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)