



from __future__ import annotations

__A       =						tuple[int, int, int]
__A       =						tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
__A       =						'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''

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


def 	__a					(   lowerCAmelCase_       : RotorPositionT      ,lowerCAmelCase_       : RotorSelectionT      ,lowerCAmelCase_       : str    )				->			tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:






						'''simple docstring'''



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

						# Checks if rotor positions are valid
						UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       rotpos
						if not 0 < rotorposa <= len(lowerCAmelCase_    ):
												UpperCAmelCase_=       F"""First rotor position is not within range of 1..26 ({rotorposa}"""
												raise ValueError(lowerCAmelCase_    )
						if not 0 < rotorposa <= len(lowerCAmelCase_    ):
												UpperCAmelCase_=       F"""Second rotor position is not within range of 1..26 ({rotorposa})"""
												raise ValueError(lowerCAmelCase_    )
						if not 0 < rotorposa <= len(lowerCAmelCase_    ):
												UpperCAmelCase_=       F"""Third rotor position is not within range of 1..26 ({rotorposa})"""
												raise ValueError(lowerCAmelCase_    )

						# Validates string and returns dict
						UpperCAmelCase_=       _plugboard(lowerCAmelCase_    )

						return rotpos, rotsel, pbdict


def 	__a					(   lowerCAmelCase_       : str    )				->			dict[str, str]:






						'''simple docstring'''



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

						pbstring.replace(""" """      ,""""""    )

						# Checks if all characters are unique
						UpperCAmelCase_=       set()
						for i in pbstring:
												if i not in abc:
																		UpperCAmelCase_=       F"""'{i}' not in list of symbols"""
																		raise Exception(lowerCAmelCase_    )
												elif i in tmppbl:
																		UpperCAmelCase_=       F"""Duplicate symbol ({i})"""
																		raise Exception(lowerCAmelCase_    )
												else:
																		tmppbl.add(lowerCAmelCase_    )
						del tmppbl

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

						return pb


def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : RotorPositionT      ,lowerCAmelCase_       : RotorSelectionT = (rotora, rotora, rotora)      ,lowerCAmelCase_       : str = ""      ,)				->			str:






						'''simple docstring'''



						UpperCAmelCase_=       text.upper()
						UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       _validator(
						    lowerCAmelCase_      ,lowerCAmelCase_      ,plugb.upper()    )

						UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       rotor_position
						UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       rotor_selection
						rotorposa -= 1
						rotorposa -= 1
						rotorposa -= 1

						UpperCAmelCase_=       []

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

																		# rotor ra --------------------------
																		UpperCAmelCase_=       abc.index(lowerCAmelCase_    ) + rotorposa
																		UpperCAmelCase_=       rotora[index % len(lowerCAmelCase_    )]

																		# rotor rb --------------------------
																		UpperCAmelCase_=       abc.index(lowerCAmelCase_    ) + rotorposa
																		UpperCAmelCase_=       rotora[index % len(lowerCAmelCase_    )]

																		# rotor rc --------------------------
																		UpperCAmelCase_=       abc.index(lowerCAmelCase_    ) + rotorposa
																		UpperCAmelCase_=       rotora[index % len(lowerCAmelCase_    )]

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

																		UpperCAmelCase_=       reflector[symbol]

																		# 2nd rotors
																		UpperCAmelCase_=       abc[rotora.index(lowerCAmelCase_    ) - rotorposa]
																		UpperCAmelCase_=       abc[rotora.index(lowerCAmelCase_    ) - rotorposa]
																		UpperCAmelCase_=       abc[rotora.index(lowerCAmelCase_    ) - rotorposa]

																		# 2nd plugboard
																		if symbol in plugboard:
																								UpperCAmelCase_=       plugboard[symbol]

																		# moves/resets rotor positions
																		rotorposa += 1
																		if rotorposa >= len(lowerCAmelCase_    ):
																								UpperCAmelCase_=       0
																								rotorposa += 1
																		if rotorposa >= len(lowerCAmelCase_    ):
																								UpperCAmelCase_=       0
																								rotorposa += 1
																		if rotorposa >= len(lowerCAmelCase_    ):
																								UpperCAmelCase_=       0

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

						return "".join(lowerCAmelCase_    )


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

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









from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: str		       =							["vqvae"]




	def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						AutoencoderKL						,  __UpperCAmelCase     :						UNetaDConditionModel						,  __UpperCAmelCase     :						Mel						,  __UpperCAmelCase     :						Union[DDIMScheduler, DDPMScheduler]						,  )      ->			str:
							super().__init__()
							self.register_modules(unet=__UpperCAmelCase						,  scheduler=__UpperCAmelCase						,  mel=__UpperCAmelCase						,  vqvae=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			int:
							return 50 if isinstance(self.scheduler						,  __UpperCAmelCase					) else 1_000




	@torch.no_grad()
	def __call__(     self     :						List[Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						np.ndarray = None						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = None						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						Union[str, Any]=True						,  )      ->			Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:
							UpperCAmelCase_=       steps or self.get_default_steps()
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size					) == int:
													UpperCAmelCase_=       (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
													UpperCAmelCase_=       randn_tensor(
													    (
													        batch_size,
													        self.unet.config.in_channels,
													        self.unet.config.sample_size[0],
													        self.unet.config.sample_size[1],
													    )						,  generator=__UpperCAmelCase						,  device=self.device						,  )
							UpperCAmelCase_=       noise
							UpperCAmelCase_=       None

							if audio_file is not None or raw_audio is not None:
													self.mel.load_audio(__UpperCAmelCase						,  __UpperCAmelCase					)
													UpperCAmelCase_=       self.mel.audio_slice_to_image(__UpperCAmelCase					)
													UpperCAmelCase_=       np.frombuffer(input_image.tobytes()						,  dtype="""uint8"""					).reshape(
													    (input_image.height, input_image.width)					)
													UpperCAmelCase_=       (input_image / 255) * 2 - 1
													UpperCAmelCase_=       torch.tensor(input_image[np.newaxis, :, :]						,  dtype=torch.float					).to(self.device					)

													if self.vqvae is not None:
																			UpperCAmelCase_=       self.vqvae.encode(torch.unsqueeze(__UpperCAmelCase						,  0					)					).latent_dist.sample(
																			    generator=__UpperCAmelCase					)[0]
																			UpperCAmelCase_=       self.vqvae.config.scaling_factor * input_images

													if start_step > 0:
																			UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  self.scheduler.timesteps[start_step - 1]					)

													UpperCAmelCase_=       (
													    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
													)
													UpperCAmelCase_=       int(mask_start_secs * pixels_per_second					)
													UpperCAmelCase_=       int(mask_end_secs * pixels_per_second					)
													UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  torch.tensor(self.scheduler.timesteps[start_step:]					)					)

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]					)					):
													if isinstance(self.unet						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													else:
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]

													if isinstance(self.scheduler						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  eta=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]
													else:
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]

													if mask is not None:
																			if mask_start > 0:
																									UpperCAmelCase_=       mask[:, step, :, :mask_start]
																			if mask_end > 0:
																									UpperCAmelCase_=       mask[:, step, :, -mask_end:]

							if self.vqvae is not None:
													# 0.18215 was scaling factor used in training to ensure unit variance
													UpperCAmelCase_=       1 / self.vqvae.config.scaling_factor * images
													UpperCAmelCase_=       self.vqvae.decode(__UpperCAmelCase					)["""sample"""]

							UpperCAmelCase_=       (images / 2 + 0.5).clamp(0						,  1					)
							UpperCAmelCase_=       images.cpu().permute(0						,  2						,  3						,  1					).numpy()
							UpperCAmelCase_=       (images * 255).round().astype("""uint8"""					)
							UpperCAmelCase_=       list(
							    (Image.fromarray(_[:, :, 0]					) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(__UpperCAmelCase						,  mode="""RGB"""					).convert("""L"""					) for _ in images)					)

							UpperCAmelCase_=       [self.mel.image_to_audio(__UpperCAmelCase					) for _ in images]
							if not return_dict:
													return images, (self.mel.get_sample_rate(), audios)

							return BaseOutput(**AudioPipelineOutput(np.array(__UpperCAmelCase					)[:, np.newaxis, :]					)						,  **ImagePipelineOutput(__UpperCAmelCase					)					)




	@torch.no_grad()
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						List[Image.Image]						,  __UpperCAmelCase     :						int = 50					)      ->			np.ndarray:
							assert isinstance(self.scheduler						,  __UpperCAmelCase					)
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       np.array(
							    [np.frombuffer(image.tobytes()						,  dtype="""uint8"""					).reshape((1, image.height, image.width)					) for image in images]					)
							UpperCAmelCase_=       (sample / 255) * 2 - 1
							UpperCAmelCase_=       torch.Tensor(__UpperCAmelCase					).to(self.device					)

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,  (0,)					)					):
													UpperCAmelCase_=       t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
													UpperCAmelCase_=       self.scheduler.alphas_cumprod[t]
													UpperCAmelCase_=       (
													    self.scheduler.alphas_cumprod[prev_timestep]
													    if prev_timestep >= 0
													    else self.scheduler.final_alpha_cumprod
													)
													UpperCAmelCase_=       1 - alpha_prod_t
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													UpperCAmelCase_=       (1 - alpha_prod_t_prev) ** 0.5 * model_output
													UpperCAmelCase_=       (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
													UpperCAmelCase_=       sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample




	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						float					)      ->			torch.Tensor:
							UpperCAmelCase_=       acos(torch.dot(torch.flatten(__UpperCAmelCase					)						,  torch.flatten(__UpperCAmelCase					)					) / torch.norm(__UpperCAmelCase					) / torch.norm(__UpperCAmelCase					)					)
							return sin((1 - alpha) * theta					) * xa / sin(__UpperCAmelCase					) + sin(alpha * theta					) * xa / sin(__UpperCAmelCase					)




