

"""simple docstring"""





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_=0.999		,  A_="cosine"		,  ):
	if alpha_transform_type == "cosine":

		def alpha_bar_fn(A_				):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(A_				):
			return math.exp(t * -12.0				)

	else:
		raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'				)

	lowerCAmelCase__			:		Optional[int]						       =			[]
	for i in range(lowercase__				):
		lowerCAmelCase__			:		int						       =			i / num_diffusion_timesteps
		lowerCAmelCase__			:		Union[str, Any]						       =			(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(lowercase__				) / alpha_bar_fn(lowercase__				)		,  lowercase__				)				)
	return torch.tensor(lowercase__		,  dtype=torch.floataa				)







class        SCREAMING_SNAKE_CASE      (	a_					,							a_ ):






					"""simple docstring"""



					lowercase__       				= [e.name for e in KarrasDiffusionSchedulers]
					lowercase__       				= 2







					@register_to_config
					def __init__( self    :       Optional[int]     ,lowercase_    :       int = 1_0_0_0     ,lowercase_    :       float = 0.0_0085     ,lowercase_    :       float = 0.012     ,lowercase_    :       str = "linear"     ,lowercase_    :       Optional[Union[np.ndarray, List[float]]] = None     ,lowercase_    :       str = "epsilon"     ,lowercase_    :       str = "linspace"     ,lowercase_    :       int = 0     ,):
						if trained_betas is not None:
							lowerCAmelCase__			:		Optional[Any]						       =			torch.tensor(A_     ,dtype=torch.floataa	)
						elif beta_schedule == "linear":
							lowerCAmelCase__			:		Union[str, Any]						       =			torch.linspace(A_     ,A_     ,A_     ,dtype=torch.floataa	)
						elif beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							lowerCAmelCase__			:		int						       =			(
							    torch.linspace(beta_start**0.5     ,beta_end**0.5     ,A_     ,dtype=torch.floataa	) ** 2
							)
						elif beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							lowerCAmelCase__			:		int						       =			betas_for_alpha_bar(A_	)
						else:
							raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'	)

						lowerCAmelCase__			:		Optional[Any]						       =			1.0 - self.betas
						lowerCAmelCase__			:		str						       =			torch.cumprod(self.alphas     ,dim=0	)

						#  set all values
						self.set_timesteps(A_     ,A_     ,A_	)







					def  __lowerCAmelCase				( self    :       Optional[Any]     ,lowercase_    :       Any     ,lowercase_    :       List[str]=None	):
						if schedule_timesteps is None:
							lowerCAmelCase__			:		Tuple						       =			self.timesteps

						lowerCAmelCase__			:		Dict						       =			(schedule_timesteps == timestep).nonzero()

						# The sigma index that is taken for the **very** first `step`
						# is always the second index (or the last index if there is only 1)
						# This way we can ensure we don't accidentally skip a sigma in
						# case we start in the middle of the denoising schedule (e.g. for image-to-image)
						if len(self._index_counter	) == 0:
							lowerCAmelCase__			:		Dict						       =			1 if len(A_	) > 1 else 0
						else:
							lowerCAmelCase__			:		List[str]						       =			timestep.cpu().item() if torch.is_tensor(A_	) else timestep
							lowerCAmelCase__			:		Union[str, Any]						       =			self._index_counter[timestep_int]

						return indices[pos].item()







					@property
					def  __lowerCAmelCase				( self    :       List[str]	):
						# standard deviation of the initial noise distribution
						if self.config.timestep_spacing in ["linspace", "trailing"]:
							return self.sigmas.max()

						return (self.sigmas.max() ** 2 + 1) ** 0.5







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       torch.FloatTensor     ,lowercase_    :       Union[float, torch.FloatTensor]     ,):
						lowerCAmelCase__			:		Optional[int]						       =			self.index_for_timestep(A_	)

						if self.state_in_first_order:
							lowerCAmelCase__			:		List[Any]						       =			self.sigmas[step_index]
						else:
							lowerCAmelCase__			:		str						       =			self.sigmas_interpol[step_index - 1]

						lowerCAmelCase__			:		List[Any]						       =			sample / ((sigma**2 + 1) ** 0.5)
						return sample







					def  __lowerCAmelCase				( self    :       Dict     ,lowercase_    :       int     ,lowercase_    :       Union[str, torch.device] = None     ,lowercase_    :       Optional[int] = None     ,):
						lowerCAmelCase__			:		int						       =			num_inference_steps

						lowerCAmelCase__			:		Optional[int]						       =			num_train_timesteps or self.config.num_train_timesteps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
							lowerCAmelCase__			:		str						       =			np.linspace(0     ,num_train_timesteps - 1     ,A_     ,dtype=A_	)[::-1].copy()
						elif self.config.timestep_spacing == "leading":
							lowerCAmelCase__			:		int						       =			num_train_timesteps // self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							lowerCAmelCase__			:		Optional[int]						       =			(np.arange(0     ,A_	) * step_ratio).round()[::-1].copy().astype(A_	)
							timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
							lowerCAmelCase__			:		int						       =			num_train_timesteps / self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							lowerCAmelCase__			:		int						       =			(np.arange(A_     ,0     ,-step_ratio	)).round().copy().astype(A_	)
							timesteps -= 1
						else:
							raise ValueError(
							    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'	)

						lowerCAmelCase__			:		Union[str, Any]						       =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
						lowerCAmelCase__			:		int						       =			torch.from_numpy(np.log(A_	)	).to(A_	)

						lowerCAmelCase__			:		Tuple						       =			np.interp(A_     ,np.arange(0     ,len(A_	)	)     ,A_	)
						lowerCAmelCase__			:		int						       =			np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
						lowerCAmelCase__			:		Optional[Any]						       =			torch.from_numpy(A_	).to(device=A_	)

						# compute up and down sigmas
						lowerCAmelCase__			:		List[str]						       =			sigmas.roll(-1	)
						lowerCAmelCase__			:		Union[str, Any]						       =			0.0
						lowerCAmelCase__			:		int						       =			(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
						lowerCAmelCase__			:		Dict						       =			(sigmas_next**2 - sigmas_up**2) ** 0.5
						lowerCAmelCase__			:		Any						       =			0.0

						# compute interpolated sigmas
						lowerCAmelCase__			:		Tuple						       =			sigmas.log().lerp(sigmas_down.log()     ,0.5	).exp()
						lowerCAmelCase__			:		str						       =			0.0

						# set sigmas
						lowerCAmelCase__			:		List[str]						       =			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
						lowerCAmelCase__			:		List[str]						       =			torch.cat(
						    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)
						lowerCAmelCase__			:		Any						       =			torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2	), sigmas_up[-1:]]	)
						lowerCAmelCase__			:		List[Any]						       =			torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2	), sigmas_down[-1:]]	)

						if str(A_	).startswith('''mps'''	):
							# mps does not support float64
							lowerCAmelCase__			:		Optional[Any]						       =			torch.from_numpy(A_	).to(A_     ,dtype=torch.floataa	)
						else:
							lowerCAmelCase__			:		List[str]						       =			torch.from_numpy(A_	).to(A_	)

						lowerCAmelCase__			:		str						       =			self.sigma_to_t(A_	).to(A_     ,dtype=timesteps.dtype	)
						lowerCAmelCase__			:		str						       =			torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])     ,dim=-1	).flatten()

						lowerCAmelCase__			:		Union[str, Any]						       =			torch.cat([timesteps[:1], interleaved_timesteps]	)

						lowerCAmelCase__			:		Any						       =			None

						# for exp beta schedules, such as the one for `pipeline_shap_e.py`
						# we need an index counter
						lowerCAmelCase__			:		Tuple						       =			defaultdict(A_	)







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       List[str]	):
						# get log sigma
						lowerCAmelCase__			:		Union[str, Any]						       =			sigma.log()

						# get distribution
						lowerCAmelCase__			:		Dict						       =			log_sigma - self.log_sigmas[:, None]

						# get sigmas range
						lowerCAmelCase__			:		str						       =			dists.ge(0	).cumsum(dim=0	).argmax(dim=0	).clamp(max=self.log_sigmas.shape[0] - 2	)
						lowerCAmelCase__			:		List[str]						       =			low_idx + 1

						lowerCAmelCase__			:		Any						       =			self.log_sigmas[low_idx]
						lowerCAmelCase__			:		Any						       =			self.log_sigmas[high_idx]

						# interpolate sigmas
						lowerCAmelCase__			:		Dict						       =			(low - log_sigma) / (low - high)
						lowerCAmelCase__			:		Optional[Any]						       =			w.clamp(0     ,1	)

						# transform interpolation to time range
						lowerCAmelCase__			:		Union[str, Any]						       =			(1 - w) * low_idx + w * high_idx
						lowerCAmelCase__			:		Optional[Any]						       =			t.view(sigma.shape	)
						return t







					@property
					def  __lowerCAmelCase				( self    :       List[str]	):
						return self.sample is None







					def  __lowerCAmelCase				( self    :       Dict     ,lowercase_    :       Union[torch.FloatTensor, np.ndarray]     ,lowercase_    :       Union[float, torch.FloatTensor]     ,lowercase_    :       Union[torch.FloatTensor, np.ndarray]     ,lowercase_    :       Optional[torch.Generator] = None     ,lowercase_    :       bool = True     ,):
						lowerCAmelCase__			:		Optional[int]						       =			self.index_for_timestep(A_	)

						# advance index counter by 1
						lowerCAmelCase__			:		Optional[Any]						       =			timestep.cpu().item() if torch.is_tensor(A_	) else timestep
						self._index_counter[timestep_int] += 1

						if self.state_in_first_order:
							lowerCAmelCase__			:		List[Any]						       =			self.sigmas[step_index]
							lowerCAmelCase__			:		Tuple						       =			self.sigmas_interpol[step_index]
							lowerCAmelCase__			:		Union[str, Any]						       =			self.sigmas_up[step_index]
							lowerCAmelCase__			:		List[str]						       =			self.sigmas_down[step_index - 1]
						else:
							# 2nd order / KPDM2's method
							lowerCAmelCase__			:		Optional[Any]						       =			self.sigmas[step_index - 1]
							lowerCAmelCase__			:		Any						       =			self.sigmas_interpol[step_index - 1]
							lowerCAmelCase__			:		Optional[int]						       =			self.sigmas_up[step_index - 1]
							lowerCAmelCase__			:		int						       =			self.sigmas_down[step_index - 1]

						# currently only gamma=0 is supported. This usually works best anyways.
						# We can support gamma in the future but then need to scale the timestep before
						# passing it to the model which requires a change in API
						lowerCAmelCase__			:		str						       =			0
						lowerCAmelCase__			:		int						       =			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

						lowerCAmelCase__			:		Tuple						       =			model_output.device
						lowerCAmelCase__			:		List[str]						       =			randn_tensor(model_output.shape     ,dtype=model_output.dtype     ,device=A_     ,generator=A_	)

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
							lowerCAmelCase__			:		int						       =			sigma_hat if self.state_in_first_order else sigma_interpol
							lowerCAmelCase__			:		Tuple						       =			sample - sigma_input * model_output
						elif self.config.prediction_type == "v_prediction":
							lowerCAmelCase__			:		List[str]						       =			sigma_hat if self.state_in_first_order else sigma_interpol
							lowerCAmelCase__			:		Any						       =			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
							    sample / (sigma_input**2 + 1)
							)
						elif self.config.prediction_type == "sample":
							raise NotImplementedError('''prediction_type not implemented yet: sample'''	)
						else:
							raise ValueError(
							    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'	)

						if self.state_in_first_order:
							# 2. Convert to an ODE derivative for 1st order
							lowerCAmelCase__			:		Any						       =			(sample - pred_original_sample) / sigma_hat
							# 3. delta timestep
							lowerCAmelCase__			:		int						       =			sigma_interpol - sigma_hat

							# store for 2nd order step
							lowerCAmelCase__			:		str						       =			sample
							lowerCAmelCase__			:		Any						       =			dt
							lowerCAmelCase__			:		Any						       =			sample + derivative * dt
						else:
							# DPM-Solver-2
							# 2. Convert to an ODE derivative for 2nd order
							lowerCAmelCase__			:		int						       =			(sample - pred_original_sample) / sigma_interpol
							# 3. delta timestep
							lowerCAmelCase__			:		Optional[int]						       =			sigma_down - sigma_hat

							lowerCAmelCase__			:		Dict						       =			self.sample
							lowerCAmelCase__			:		Optional[Any]						       =			None

							lowerCAmelCase__			:		Optional[Any]						       =			sample + derivative * dt
							lowerCAmelCase__			:		Optional[int]						       =			prev_sample + noise * sigma_up

						if not return_dict:
							return (prev_sample,)

						return SchedulerOutput(prev_sample=A_	)







					def  __lowerCAmelCase				( self    :       int     ,lowercase_    :       torch.FloatTensor     ,lowercase_    :       torch.FloatTensor     ,lowercase_    :       torch.FloatTensor     ,):
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						lowerCAmelCase__			:		Optional[Any]						       =			self.sigmas.to(device=original_samples.device     ,dtype=original_samples.dtype	)
						if original_samples.device.type == "mps" and torch.is_floating_point(A_	):
							# mps does not support float64
							lowerCAmelCase__			:		Any						       =			self.timesteps.to(original_samples.device     ,dtype=torch.floataa	)
							lowerCAmelCase__			:		str						       =			timesteps.to(original_samples.device     ,dtype=torch.floataa	)
						else:
							lowerCAmelCase__			:		Dict						       =			self.timesteps.to(original_samples.device	)
							lowerCAmelCase__			:		List[str]						       =			timesteps.to(original_samples.device	)

						lowerCAmelCase__			:		Dict						       =			[self.index_for_timestep(A_     ,A_	) for t in timesteps]

						lowerCAmelCase__			:		int						       =			sigmas[step_indices].flatten()
						while len(sigma.shape	) < len(original_samples.shape	):
							lowerCAmelCase__			:		Union[str, Any]						       =			sigma.unsqueeze(-1	)

						lowerCAmelCase__			:		Union[str, Any]						       =			original_samples + noise * sigma
						return noisy_samples







					def __len__( self    :       int	):
						return self.config.num_train_timesteps


"""simple docstring"""





import random
import sys

__UpperCamelCase						:   Tuple							   =	'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''

def 						__SCREAMING_SNAKE_CASE						(   ):
	lowerCAmelCase__			:		List[str]						       =			input('''Enter message: '''				)
	lowerCAmelCase__			:		Optional[int]						       =			'''LFWOAYUISVKMNXPBDCRJTQEGHZ'''
	lowerCAmelCase__			:		List[Any]						       =			input('''Encrypt/Decrypt [e/d]: '''				)

	check_valid_key(A_				)

	if resp.lower().startswith('''e'''				):
		lowerCAmelCase__			:		Tuple						       =			'''encrypt'''
		lowerCAmelCase__			:		Union[str, Any]						       =			encrypt_message(A_		,  A_				)
	elif resp.lower().startswith('''d'''				):
		lowerCAmelCase__			:		Dict						       =			'''decrypt'''
		lowerCAmelCase__			:		str						       =			decrypt_message(A_		,  A_				)

	print(f'\n{mode.title()}ion: \n{translated}'				)

def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		str						       =			list(A_				)
	lowerCAmelCase__			:		int						       =			list(A_				)
	key_list.sort()
	letters_list.sort()

	if key_list != letters_list:
		sys.exit('''Error in the key or symbol set.'''				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	return translate_message(A_		,  A_		,  '''encrypt'''				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	return translate_message(A_		,  A_		,  '''decrypt'''				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_				):
	lowerCAmelCase__			:		str						       =			''''''
	lowerCAmelCase__			:		List[str]						       =			LETTERS
	lowerCAmelCase__			:		Optional[int]						       =			key

	if mode == "decrypt":
		lowerCAmelCase__       ,lowerCAmelCase__			:		int						       =			chars_b, chars_a

	for symbol in message:
		if symbol.upper() in chars_a:
			lowerCAmelCase__			:		int						       =			chars_a.find(symbol.upper()				)
			if symbol.isupper():
				translated += chars_b[sym_index].upper()
			else:
				translated += chars_b[sym_index].lower()
		else:
			translated += symbol

	return translated

def 						__SCREAMING_SNAKE_CASE						(   ):
	lowerCAmelCase__			:		Optional[int]						       =			list(A_				)
	random.shuffle(A_				)
	return "".join(A_				)


if __name__ == "__main__":
							main()




