





"""simple docstring"""




# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

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




@flax.struct.dataclass
class 				_a		:
				UpperCamelCase			=					42
				UpperCamelCase			=					42

				# setable values
				UpperCamelCase			=					42
				UpperCamelCase			=					42
				UpperCamelCase			=					None






				@classmethod
				def        snake_case			(				cls	:					List[str],				lowerCAmelCase__	:					CommonSchedulerState,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					jnp.ndarray,				)			->   List[str]:




										'''simple docstring'''



										return cls(
										    common=lowerCAmelCase__,				final_alpha_cumprod=lowerCAmelCase__,				init_noise_sigma=lowerCAmelCase__,				timesteps=lowerCAmelCase__,				)




@dataclass
class 				_a		(   _lowerCAmelCase       ):
				UpperCamelCase			=					42




class 				_a		(   _lowerCAmelCase			,       _lowerCAmelCase       ):
				UpperCamelCase			=					[e.name for e in FlaxKarrasDiffusionSchedulers]

				UpperCamelCase			=					42






				@property
				def        snake_case			(				self	:					str							)			->   Optional[Any]:




										'''simple docstring'''



										return True






				@register_to_config
				def __init__(				self	:					Union[str, Any],				lowerCAmelCase__	:					int = 1_0_0_0,				lowerCAmelCase__	:					float = 0.0_001,				lowerCAmelCase__	:					float = 0.02,				lowerCAmelCase__	:					str = "linear",				lowerCAmelCase__	:					Optional[jnp.ndarray] = None,				lowerCAmelCase__	:					bool = True,				lowerCAmelCase__	:					int = 0,				lowerCAmelCase__	:					str = "epsilon",				lowerCAmelCase__	:					jnp.dtype = jnp.floataa,				)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					dtype






				def        snake_case			(				self	:					int,				lowerCAmelCase__	:					Optional[CommonSchedulerState] = None							)			->   DDIMSchedulerState:




										'''simple docstring'''



										if common is None:
																_UpperCamelCase    :      Dict						     =					CommonSchedulerState.create(self							)

										# At every step in ddim, we are looking into the previous alphas_cumprod
										# For the final step, there is no previous alphas_cumprod because we are already at 0
										# `set_alpha_to_one` decides whether we set this parameter simply to one or
										# whether we use the final alpha of the "non-previous" one.
										_UpperCamelCase    :      Dict						     =					(
										    jnp.array(1.0,				dtype=self.dtype							) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
										)

										# standard deviation of the initial noise distribution
										_UpperCamelCase    :      str						     =					jnp.array(1.0,				dtype=self.dtype							)

										_UpperCamelCase    :      Union[str, Any]						     =					jnp.arange(0,				self.config.num_train_timesteps							).round()[::-1]

										return DDIMSchedulerState.create(
										    common=lowerCAmelCase__,				final_alpha_cumprod=lowerCAmelCase__,				init_noise_sigma=lowerCAmelCase__,				timesteps=lowerCAmelCase__,				)






				def        snake_case			(				self	:					Union[str, Any],				lowerCAmelCase__	:					DDIMSchedulerState,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					Optional[int] = None							)			->   jnp.ndarray:




										'''simple docstring'''



										return sample






				def        snake_case			(				self	:					int,				lowerCAmelCase__	:					DDIMSchedulerState,				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					Tuple = ()							)			->   DDIMSchedulerState:




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					self.config.num_train_timesteps // num_inference_steps
										# creates integer timesteps by multiplying by ratio
										# rounding to avoid issues when num_inference_step is power of 3
										_UpperCamelCase    :      Any						     =					(jnp.arange(0,				lowerCAmelCase__							) * step_ratio).round()[::-1] + self.config.steps_offset

										return state.replace(
										    num_inference_steps=lowerCAmelCase__,				timesteps=lowerCAmelCase__,				)






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					DDIMSchedulerState,				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					Dict							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					state.common.alphas_cumprod[timestep]
										_UpperCamelCase    :      Optional[Any]						     =					jnp.where(
										    prev_timestep >= 0,				state.common.alphas_cumprod[prev_timestep],				state.final_alpha_cumprod							)
										_UpperCamelCase    :      Tuple						     =					1 - alpha_prod_t
										_UpperCamelCase    :      List[str]						     =					1 - alpha_prod_t_prev

										_UpperCamelCase    :      Optional[Any]						     =					(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

										return variance






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					DDIMSchedulerState,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					float = 0.0,				lowerCAmelCase__	:					bool = True,				)			->   Union[FlaxDDIMSchedulerOutput, Tuple]:




										'''simple docstring'''



										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'''							)

										# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
										# Ideally, read DDIM paper in-detail understanding

										# Notation (<variable name> -> <name in paper>
										# - pred_noise_t -> e_theta(x_t, t)
										# - pred_original_sample -> f_theta(x_t, t) or x_0
										# - std_dev_t -> sigma_t
										# - eta -> η
										# - pred_sample_direction -> "direction pointing to x_t"
										# - pred_prev_sample -> "x_t-1"

										# 1. get previous step value (=t-1)
										_UpperCamelCase    :      Any						     =					timestep - self.config.num_train_timesteps // state.num_inference_steps

										_UpperCamelCase    :      Union[str, Any]						     =					state.common.alphas_cumprod
										_UpperCamelCase    :      Optional[Any]						     =					state.final_alpha_cumprod

										# 2. compute alphas, betas
										_UpperCamelCase    :      str						     =					alphas_cumprod[timestep]
										_UpperCamelCase    :      List[str]						     =					jnp.where(prev_timestep >= 0,				alphas_cumprod[prev_timestep],				lowerCAmelCase__							)

										_UpperCamelCase    :      List[Any]						     =					1 - alpha_prod_t

										# 3. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										if self.config.prediction_type == "epsilon":
																_UpperCamelCase    :      Tuple						     =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																_UpperCamelCase    :      List[Any]						     =					model_output
										elif self.config.prediction_type == "sample":
																_UpperCamelCase    :      List[Any]						     =					model_output
																_UpperCamelCase    :      Tuple						     =					(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
										elif self.config.prediction_type == "v_prediction":
																_UpperCamelCase    :      int						     =					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																_UpperCamelCase    :      Dict						     =					(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
										else:
																raise ValueError(
																    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
																    ''' `v_prediction`'''							)

										# 4. compute variance: "sigma_t(η)" -> see formula (16)
										# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
										_UpperCamelCase    :      Any						     =					self._get_variance(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__							)
										_UpperCamelCase    :      Union[str, Any]						     =					eta * variance ** 0.5

										# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										_UpperCamelCase    :      str						     =					(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

										# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										_UpperCamelCase    :      Optional[Any]						     =					alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

										if not return_dict:
																return (prev_sample, state)

										return FlaxDDIMSchedulerOutput(prev_sample=lowerCAmelCase__,				state=lowerCAmelCase__							)






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					DDIMSchedulerState,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					jnp.ndarray,				)			->   jnp.ndarray:




										'''simple docstring'''



										return add_noise_common(state.common,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__							)






				def        snake_case			(				self	:					List[Any],				lowerCAmelCase__	:					DDIMSchedulerState,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					jnp.ndarray,				)			->   jnp.ndarray:




										'''simple docstring'''



										return get_velocity_common(state.common,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__							)






				def __len__(				self	:					Union[str, Any]							)			->   int:




										'''simple docstring'''



										return self.config.num_train_timesteps










"""simple docstring"""




def  a_       (							_lowercase     ):
						return 1 if input_a == 0 else 0





def  a_       (							):
						assert not_gate(0     ) == 1
						assert not_gate(1     ) == 0


if __name__ == "__main__":
						print(not_gate(0))
						print(not_gate(1))



