
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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


UpperCamelCase_       =	logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class     _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE__							):
				lowerCamelCase_       =     4_2
				lowerCamelCase_       =     None





def 							_lowerCamelCase			(					lowerCamelCase_:  Union[str, Any]				,	lowerCamelCase_:  Dict=0.9_9_9				,	lowerCamelCase_:  Tuple="cosine"				,	):



				'''simple docstring'''







				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowerCamelCase_:  Optional[int]		):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2		) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowerCamelCase_:  Any		):
												return math.exp(t * -1_2.0		)

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

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






class     _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__							):
				lowerCamelCase_       =     [e.name for e in KarrasDiffusionSchedulers]
				lowerCamelCase_       =     1





				@register_to_config
				def __init__(		self :		Tuple					,      snake_case_ :		List[Any] = 1000					,      snake_case_ :		int = 0.00_01					,      snake_case_ :		Optional[Any] = 0.02					,      snake_case_ :		Union[str, Any] = "linear"					,      snake_case_ :		str = None					,      snake_case_ :		List[str] = "epsilon"					,      snake_case_ :		int = "linear"					,      snake_case_ :		Optional[int] = False					,      snake_case_ :		str = "linspace"					,      snake_case_ :		Optional[int] = 0					,      ):


								"""simple docstring"""

								if trained_betas is not None:
												A     :  List[str]   =				torch.tensor(snake_case_					,      dtype=torch.floataa							)
								elif beta_schedule == "linear":
												A     :  List[str]   =				torch.linspace(snake_case_					,      snake_case_					,      snake_case_					,      dtype=torch.floataa							)
								elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												A     :  List[str]   =				(
												    torch.linspace(beta_start**0.5					,      beta_end**0.5					,      snake_case_					,      dtype=torch.floataa							) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												A     :  Any   =				betas_for_alpha_bar(snake_case_							)
								else:
												raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""							)

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

								A     :  Optional[int]   =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
								A     :  int   =				np.concatenate([sigmas[::-1], [0.0]]							).astype(np.floataa							)
								A     :  List[Any]   =				torch.from_numpy(snake_case_							)

								# setable values
								A     :  List[str]   =				None
								A     :  Tuple   =				np.linspace(0					,      num_train_timesteps - 1					,      snake_case_					,      dtype=snake_case_							)[::-1].copy()
								A     :  int   =				torch.from_numpy(snake_case_							)
								A     :  Optional[int]   =				False
								A     :  Optional[Any]   =				use_karras_sigmas





				@property
				def        _UpperCAmelCase						(		self :		List[str]							):


								"""simple docstring"""

								if self.config.timestep_spacing in ["linspace", "trailing"]:
												return self.sigmas.max()

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





				def        _UpperCAmelCase						(		self :		Optional[Any]					,      snake_case_ :		List[Any]					,      snake_case_ :		List[str]							):


								"""simple docstring"""

								if isinstance(snake_case_					,      torch.Tensor							):
												A     :  Optional[Any]   =				timestep.to(self.timesteps.device							)
								A     :  List[str]   =				(self.timesteps == timestep).nonzero().item()
								A     :  int   =				self.sigmas[step_index]

								A     :  Optional[int]   =				sample / ((sigma**2 + 1) ** 0.5)

								A     :  Union[str, Any]   =				True
								return sample





				def        _UpperCAmelCase						(		self :		List[Any]					,      snake_case_ :		Any					,      snake_case_ :		Tuple = None							):


								"""simple docstring"""

								A     :  Tuple   =				num_inference_steps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
												A     :  Optional[Any]   =				np.linspace(0					,      self.config.num_train_timesteps - 1					,      snake_case_					,      dtype=snake_case_							)[
												    ::-1
												].copy()
								elif self.config.timestep_spacing == "leading":
												A     :  Optional[Any]   =				self.config.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
												A     :  List[str]   =				(np.arange(0					,      snake_case_							) * step_ratio).round()[::-1].copy().astype(snake_case_							)
												timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
												A     :  List[Any]   =				self.config.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
												A     :  Any   =				(np.arange(self.config.num_train_timesteps					,      0					,      -step_ratio							)).round().copy().astype(snake_case_							)
												timesteps -= 1
								else:
												raise ValueError(
												    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."""							)

								A     :  Union[str, Any]   =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
								A     :  Optional[Any]   =				np.log(snake_case_							)

								if self.config.interpolation_type == "linear":
												A     :  Any   =				np.interp(snake_case_					,      np.arange(0					,      len(snake_case_							)							)					,      snake_case_							)
								elif self.config.interpolation_type == "log_linear":
												A     :  int   =				torch.linspace(np.log(sigmas[-1]							)					,      np.log(sigmas[0]							)					,      num_inference_steps + 1							).exp()
								else:
												raise ValueError(
												    f"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
												    ''' \'linear\' or \'log_linear\''''							)

								if self.use_karras_sigmas:
												A     :  Dict   =				self._convert_to_karras(in_sigmas=snake_case_					,      num_inference_steps=self.num_inference_steps							)
												A     :  List[Any]   =				np.array([self._sigma_to_t(snake_case_					,      snake_case_							) for sigma in sigmas]							)

								A     :  str   =				np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
								A     :  List[str]   =				torch.from_numpy(snake_case_							).to(device=snake_case_							)
								if str(snake_case_							).startswith('''mps'''							):
												# mps does not support float64
												A     :  List[Any]   =				torch.from_numpy(snake_case_							).to(snake_case_					,      dtype=torch.floataa							)
								else:
												A     :  int   =				torch.from_numpy(snake_case_							).to(device=snake_case_							)





				def        _UpperCAmelCase						(		self :		Optional[int]					,      snake_case_ :		List[str]					,      snake_case_ :		Tuple							):


								"""simple docstring"""

								A     :  Dict   =				np.log(snake_case_							)

								# get distribution
								A     :  Union[str, Any]   =				log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								A     :  int   =				np.cumsum((dists >= 0)					,      axis=0							).argmax(axis=0							).clip(max=log_sigmas.shape[0] - 2							)
								A     :  str   =				low_idx + 1

								A     :  Union[str, Any]   =				log_sigmas[low_idx]
								A     :  str   =				log_sigmas[high_idx]

								# interpolate sigmas
								A     :  Tuple   =				(low - log_sigma) / (low - high)
								A     :  str   =				np.clip(snake_case_					,      0					,      1							)

								# transform interpolation to time range
								A     :  Any   =				(1 - w) * low_idx + w * high_idx
								A     :  Optional[int]   =				t.reshape(sigma.shape							)
								return t





				def        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		str					,      snake_case_ :		Union[str, Any]							):


								"""simple docstring"""

								A     :  str   =				in_sigmas[-1].item()
								A     :  Optional[Any]   =				in_sigmas[0].item()

								A     :  Optional[Any]   =				7.0  # 7.0 is the value used in the paper
								A     :  Dict   =				np.linspace(0					,      1					,      snake_case_							)
								A     :  str   =				sigma_min ** (1 / rho)
								A     :  Optional[Any]   =				sigma_max ** (1 / rho)
								A     :  Tuple   =				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas





				def        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		str					,      snake_case_ :		int					,      snake_case_ :		List[str]					,      snake_case_ :		int = 0.0					,      snake_case_ :		Tuple = 0.0					,      snake_case_ :		Union[str, Any] = float('''inf'''							)					,      snake_case_ :		Tuple = 1.0					,      snake_case_ :		Any = None					,      snake_case_ :		Dict = True					,      ):


								"""simple docstring"""

								if (
								    isinstance(snake_case_					,      snake_case_							)
								    or isinstance(snake_case_					,      torch.IntTensor							)
								    or isinstance(snake_case_					,      torch.LongTensor							)
								):
												raise ValueError(
												    (
												        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
												        ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
												        ''' one of the `scheduler.timesteps` as a timestep.'''
												    )					,      )

								if not self.is_scale_input_called:
												logger.warning(
												    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
												    '''See `StableDiffusionPipeline` for a usage example.'''							)

								if isinstance(snake_case_					,      torch.Tensor							):
												A     :  int   =				timestep.to(self.timesteps.device							)

								A     :  Union[str, Any]   =				(self.timesteps == timestep).nonzero().item()
								A     :  List[str]   =				self.sigmas[step_index]

								A     :  int   =				min(s_churn / (len(self.sigmas							) - 1)					,      2**0.5 - 1							) if s_tmin <= sigma <= s_tmax else 0.0

								A     :  List[Any]   =				randn_tensor(
								    model_output.shape					,      dtype=model_output.dtype					,      device=model_output.device					,      generator=snake_case_							)

								A     :  List[str]   =				noise * s_noise
								A     :  int   =				sigma * (gamma + 1)

								if gamma > 0:
												A     :  Any   =				sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								# NOTE: "original_sample" should not be an expected prediction_type but is left in for
								# backwards compatibility
								if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
												A     :  str   =				model_output
								elif self.config.prediction_type == "epsilon":
												A     :  Union[str, Any]   =				sample - sigma_hat * model_output
								elif self.config.prediction_type == "v_prediction":
												# * c_out + input * c_skip
												A     :  int   =				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								else:
												raise ValueError(
												    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""							)

								# 2. Convert to an ODE derivative
								A     :  int   =				(sample - pred_original_sample) / sigma_hat

								A     :  Optional[int]   =				self.sigmas[step_index + 1] - sigma_hat

								A     :  int   =				sample + derivative * dt

								if not return_dict:
												return (prev_sample,)

								return EulerDiscreteSchedulerOutput(prev_sample=snake_case_					,      pred_original_sample=snake_case_							)





				def        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		Any					,      snake_case_ :		Any					,      snake_case_ :		Tuple					,      ):


								"""simple docstring"""

								A     :  Optional[int]   =				self.sigmas.to(device=original_samples.device					,      dtype=original_samples.dtype							)
								if original_samples.device.type == "mps" and torch.is_floating_point(snake_case_							):
												# mps does not support float64
												A     :  int   =				self.timesteps.to(original_samples.device					,      dtype=torch.floataa							)
												A     :  List[Any]   =				timesteps.to(original_samples.device					,      dtype=torch.floataa							)
								else:
												A     :  Optional[int]   =				self.timesteps.to(original_samples.device							)
												A     :  int   =				timesteps.to(original_samples.device							)

								A     :  Optional[Any]   =				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

								A     :  Optional[Any]   =				sigmas[step_indices].flatten()
								while len(sigma.shape							) < len(original_samples.shape							):
												A     :  Tuple   =				sigma.unsqueeze(-1							)

								A     :  List[Any]   =				original_samples + noise * sigma
								return noisy_samples






				def __len__(		self :		Any							):


								"""simple docstring"""

								return self.config.num_train_timesteps

from __future__ import annotations





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  dict[str, str]		):



				'''simple docstring'''







				A ,       A ,       A     :  str   =				'''''', '''''', ''''''
				A     :  Union[str, Any]   =				[]

				for character in message_part:
								tmp.append(character_to_number[character]		)

				for each in tmp:
								one += each[0]
								two += each[1]
								three += each[2]

				return one + two + three





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  dict[str, str]		):



				'''simple docstring'''







				A ,       A     :  Tuple   =				'''''', ''''''
				A     :  str   =				[]

				for character in message_part:
								this_part += character_to_number[character]

				for digit in this_part:
								tmp += digit
								if len(lowerCamelCase_		) == len(lowerCamelCase_		):
												result.append(lowerCamelCase_		)
												A     :  Dict   =				''''''

				return result[0], result[1], result[2]





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  str		):



				'''simple docstring'''







				A     :  Dict   =				alphabet.replace(''' '''				,	''''''		).upper()
				A     :  List[str]   =				message.replace(''' '''				,	''''''		).upper()

				# Check length and characters
				if len(lowerCamelCase_		) != 27:
								raise KeyError('''Length of alphabet has to be 27.'''		)
				for each in message:
								if each not in alphabet:
												raise ValueError('''Each message character has to be included in alphabet!'''		)

    # Generate dictionares
				A     :  str   =				(
				    '''111''',
				    '''112''',
				    '''113''',
				    '''121''',
				    '''122''',
				    '''123''',
				    '''131''',
				    '''132''',
				    '''133''',
				    '''211''',
				    '''212''',
				    '''213''',
				    '''221''',
				    '''222''',
				    '''223''',
				    '''231''',
				    '''232''',
				    '''233''',
				    '''311''',
				    '''312''',
				    '''313''',
				    '''321''',
				    '''322''',
				    '''323''',
				    '''331''',
				    '''332''',
				    '''333''',
				)
				A     :  Union[str, Any]   =				{}
				A     :  int   =				{}
				for letter, number in zip(lowerCamelCase_				,	lowerCamelCase_		):
								A     :  Optional[Any]   =				number
								A     :  Union[str, Any]   =				letter

				return message, alphabet, character_to_number, number_to_character





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."				,	lowerCamelCase_:  int = 5		):



				'''simple docstring'''







				A ,       A ,       A ,       A     :  Optional[int]   =				__prepare(
				    lowerCamelCase_				,	lowerCamelCase_		)
				A ,       A     :  int   =				'''''', ''''''

				for i in range(0				,	len(lowerCamelCase_		) + 1				,	lowerCamelCase_		):
								encrypted_numeric += __encrypt_part(
								    message[i : i + period]				,	lowerCamelCase_		)

				for i in range(0				,	len(lowerCamelCase_		)				,	3		):
								encrypted += number_to_character[encrypted_numeric[i : i + 3]]

				return encrypted





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."				,	lowerCamelCase_:  int = 5		):



				'''simple docstring'''







				A ,       A ,       A ,       A     :  Optional[int]   =				__prepare(
				    lowerCamelCase_				,	lowerCamelCase_		)
				A     :  Tuple   =				[]
				A     :  Optional[Any]   =				''''''

				for i in range(0				,	len(lowerCamelCase_		) + 1				,	lowerCamelCase_		):
								A ,       A ,       A     :  List[Any]   =				__decrypt_part(message[i : i + period]				,	lowerCamelCase_		)

								for j in range(0				,	len(lowerCamelCase_		)		):
												decrypted_numeric.append(a[j] + b[j] + c[j]		)

				for each in decrypted_numeric:
								decrypted += number_to_character[each]

				return decrypted


if __name__ == "__main__":
					UpperCamelCase_  =	"DEFEND THE EAST WALL OF THE CASTLE."
					UpperCamelCase_  =	encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
					UpperCamelCase_  =	decrypt_message(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
					print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')