





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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)

@flax.struct.dataclass
class    __A  :
				__A  =					42

				# setable values
				__A  =					42
				__A  =					42
				__A  =					42
				__A  =					None

				# running values
				__A  =					None


				@classmethod
				def _snake_case							(		cls ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				):
								return cls(common=A__ ,			init_noise_sigma=A__ ,			timesteps=A__ ,			sigmas=A__				)




@dataclass
class    __A  (    SCREAMING_SNAKE_CASE__     ):
				__A  =					42




class    __A  (    SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__     ):
				__A  =					[e.name for e in FlaxKarrasDiffusionSchedulers]

				__A  =					42


				@property
				def _snake_case							(		self				):
								return True


				@register_to_config
				def __init__(		self ,			UpperCAmelCase_ = 1000 ,			UpperCAmelCase_ = 0.0_0_0_1 ,			UpperCAmelCase_ = 0.0_2 ,			UpperCAmelCase_ = "linear" ,			UpperCAmelCase_ = None ,			UpperCAmelCase_ = "epsilon" ,			UpperCAmelCase_ = jnp.floataa ,			):
								lowerCamelCase									=dtype


				def _snake_case							(		self ,			UpperCAmelCase_ = None				):
								if common is None:
												lowerCamelCase									=CommonSchedulerState.create(self				)

								lowerCamelCase									=jnp.arange(0 ,			self.config.num_train_timesteps				).round()[::-1]
								lowerCamelCase									=((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

								# standard deviation of the initial noise distribution
								lowerCamelCase									=sigmas.max()

								return LMSDiscreteSchedulerState.create(
								    common=A__ ,			init_noise_sigma=A__ ,			timesteps=A__ ,			sigmas=A__ ,			)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				):
								((lowerCamelCase		)			,			)									=jnp.where(state.timesteps == timestep ,			size=1				)
								lowerCamelCase									=step_index[0]

								lowerCamelCase									=state.sigmas[step_index]
								lowerCamelCase									=sample / ((sigma**2 + 1) ** 0.5)
								return sample


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				):

								def lms_derivative(UpperCAmelCase_				):
												lowerCamelCase									=1.0
												for k in range(A__				):
																if current_order == k:
																				continue
																prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
												return prod

								lowerCamelCase									=integrate.quad(A__ ,			state.sigmas[t] ,			state.sigmas[t + 1] ,			epsrel=1E-4				)[0]

								return integrated_coeff


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ = ()				):
								lowerCamelCase									=jnp.linspace(self.config.num_train_timesteps - 1 ,			0 ,			A__ ,			dtype=self.dtype				)

								lowerCamelCase									=jnp.floor(A__				).astype(jnp.intaa				)
								lowerCamelCase									=jnp.ceil(A__				).astype(jnp.intaa				)

								lowerCamelCase									=jnp.mod(A__ ,			1.0				)

								lowerCamelCase									=((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
								lowerCamelCase									=(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
								lowerCamelCase									=jnp.concatenate([sigmas, jnp.array([0.0] ,			dtype=self.dtype				)]				)

								lowerCamelCase									=timesteps.astype(jnp.intaa				)

								# initial running values
								lowerCamelCase									=jnp.zeros((0,) + shape ,			dtype=self.dtype				)

								return state.replace(
								    timesteps=A__ ,			sigmas=A__ ,			num_inference_steps=A__ ,			derivatives=A__ ,			)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ = 4 ,			UpperCAmelCase_ = True ,			):
								if state.num_inference_steps is None:
												raise ValueError(
												    """Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler"""				)

								lowerCamelCase									=state.sigmas[timestep]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
												lowerCamelCase									=sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
												# * c_out + input * c_skip
												lowerCamelCase									=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
								lowerCamelCase									=(sample - pred_original_sample) / sigma
								lowerCamelCase									=state.replace(derivatives=jnp.append(state.derivatives ,			A__				)				)
								if len(state.derivatives				) > order:
												lowerCamelCase									=state.replace(derivatives=jnp.delete(state.derivatives ,			0				)				)

								# 3. Compute linear multistep coefficients
								lowerCamelCase									=min(timestep + 1 ,			A__				)
								lowerCamelCase									=[self.get_lms_coefficient(A__ ,			A__ ,			A__ ,			A__				) for curr_order in range(A__				)]

								# 4. Compute previous sample based on the derivatives path
								lowerCamelCase									=sample + sum(
								    coeff * derivative for coeff, derivative in zip(A__ ,			reversed(state.derivatives				)				)				)

								if not return_dict:
												return (prev_sample, state)

								return FlaxLMSSchedulerOutput(prev_sample=A__ ,			state=A__				)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			):
								lowerCamelCase									=state.sigmas[timesteps].flatten()
								lowerCamelCase									=broadcast_to_shape_from_left(A__ ,			noise.shape				)

								lowerCamelCase									=original_samples + noise * sigma

								return noisy_samples


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







from __future__ import annotations

import random
import string







class 							lowercase						:




							def __init__(							self  ,A__ = None):
												lowercase		      =		passcode or self.__passcode_creator()
												lowercase		      =		self.__make_key_list()
												lowercase		      =		self.__make_shift_key()




							def __str__(							self):
												return "".join(self.__passcode)




							def 					A__ (							self  ,A__):
												for i in range(1  ,len(A__)  ,2):
																	iterlist[i] *= -1
												return iterlist




							def 					A__ (							self):
												lowercase		      =		string.ascii_letters + string.digits
												lowercase		      =		[random.choice(A__) for _ in range(random.randint(1_0  ,2_0))]
												return password




							def 					A__ (							self):
												lowercase		      =		(
												    string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
												)

												lowercase		      =		[]

												# creates points known as breakpoints to break the key_list_options at those
												# points and pivot each substring
												lowercase		      =		sorted(set(self.__passcode))
												lowercase		      =		[]

												# algorithm for creating a new shuffled list, keys_l, out of key_list_options
												for i in key_list_options:
																	temp_list.extend(A__)

																	# checking breakpoints at which to pivot temporary sublist and add it into
																	# keys_l
																	if i in breakpoints or i == key_list_options[-1]:
																						keys_l.extend(temp_list[::-1])
																						temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
												return keys_l




							def 					A__ (							self):
												lowercase		      =		sum(self.__neg_pos([ord(A__) for x in self.__passcode]))
												return num if num > 0 else len(self.__passcode)




							def 					A__ (							self  ,A__):
												lowercase		      =		''''''

												# decoding shift like Caesar cipher algorithm implementing negative shift or
												# reverse shift or left shift
												for i in encoded_message:
																	lowercase		      =		self.__key_list.index(A__)
																	decoded_message += self.__key_list[
																	    (position - self.__shift_key) % -len(self.__key_list)
																	]

												return decoded_message





							def 					A__ (							self  ,A__):
												lowercase		      =		''''''

												# encoding shift like Caesar cipher algorithm implementing positive shift or
												# forward shift or right shift
												for i in plaintext:
																	lowercase		      =		self.__key_list.index(A__)
																	encoded_message += self.__key_list[
																	    (position + self.__shift_key) % len(self.__key_list)
																	]

												return encoded_message



def 			UpperCamelCase       (				lowerCAmelCase__ = "Hello, this is a modified Caesar cipher"						):
					'''simple docstring'''
					lowercase		      =		ShuffledShiftCipher()
					return cipa.decrypt(cipa.encrypt(lowerCAmelCase__						)						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




