

# 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 			snake_case_     :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     CommonSchedulerState
			SCREAMING_SNAKE_CASE  :     jnp.ndarray

			# setable values
			SCREAMING_SNAKE_CASE  :     jnp.ndarray
			SCREAMING_SNAKE_CASE  :     jnp.ndarray
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       None







			@classmethod
			def        snake_case__(  cls				:       int							,     _UpperCamelCase				:       CommonSchedulerState							,     _UpperCamelCase				:       jnp.ndarray							,     _UpperCamelCase				:       jnp.ndarray							,     _UpperCamelCase				:       jnp.ndarray							,     )   ->List[Any]:
									return cls(
									    common=_UpperCamelCase							,     final_alpha_cumprod=_UpperCamelCase							,     init_noise_sigma=_UpperCamelCase							,     timesteps=_UpperCamelCase							,     )






@dataclass
class 			snake_case_     (	__A      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     DDIMSchedulerState






class 			snake_case_     (	__A			, __A      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Tuple		    =       [e.name for e in FlaxKarrasDiffusionSchedulers]

			SCREAMING_SNAKE_CASE  :     jnp.dtype







			@property
			def        snake_case__(  self				:       Tuple							)   ->int:
									return True







			@register_to_config
			def __init__(  self				:       Dict							,     _UpperCamelCase				:       int = 1_0_0_0							,     _UpperCamelCase				:       float = 0.0001							,     _UpperCamelCase				:       float = 0.02							,     _UpperCamelCase				:       str = "linear"							,     _UpperCamelCase				:       Optional[jnp.ndarray] = None							,     _UpperCamelCase				:       bool = True							,     _UpperCamelCase				:       int = 0							,     _UpperCamelCase				:       str = "epsilon"							,     _UpperCamelCase				:       jnp.dtype = jnp.floataa							,     )   ->List[Any]:
									snake_case_     =			dtype







			def        snake_case__(  self				:       Optional[Any]							,     _UpperCamelCase				:       Optional[CommonSchedulerState] = None							)   ->DDIMSchedulerState:
									if common is None:
															snake_case_     =			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.
									snake_case_     =			(
									    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
									snake_case_     =			jnp.array(1.0							,     dtype=self.dtype							)

									snake_case_     =			jnp.arange(0							,     self.config.num_train_timesteps							).round()[::-1]

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







			def        snake_case__(  self				:       Tuple							,     _UpperCamelCase				:       DDIMSchedulerState							,     _UpperCamelCase				:       jnp.ndarray							,     _UpperCamelCase				:       Optional[int] = None							)   ->jnp.ndarray:
									return sample







			def        snake_case__(  self				:       List[str]							,     _UpperCamelCase				:       DDIMSchedulerState							,     _UpperCamelCase				:       int							,     _UpperCamelCase				:       Tuple = ()							)   ->DDIMSchedulerState:
									snake_case_     =			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
									snake_case_     =			(jnp.arange(0							,     _UpperCamelCase							) * step_ratio).round()[::-1] + self.config.steps_offset

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







			def        snake_case__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       DDIMSchedulerState							,     _UpperCamelCase				:       Dict							,     _UpperCamelCase				:       Optional[int]							)   ->Union[str, Any]:
									snake_case_     =			state.common.alphas_cumprod[timestep]
									snake_case_     =			jnp.where(
									    prev_timestep >= 0							,     state.common.alphas_cumprod[prev_timestep]							,     state.final_alpha_cumprod							)
									snake_case_     =			1 - alpha_prod_t
									snake_case_     =			1 - alpha_prod_t_prev

									snake_case_     =			(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

									return variance







			def        snake_case__(  self				:       Tuple							,     _UpperCamelCase				:       DDIMSchedulerState							,     _UpperCamelCase				:       jnp.ndarray							,     _UpperCamelCase				:       int							,     _UpperCamelCase				:       jnp.ndarray							,     _UpperCamelCase				:       float = 0.0							,     _UpperCamelCase				:       bool = True							,     )   ->Union[FlaxDDIMSchedulerOutput, Tuple]:
									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)
									snake_case_     =			timestep - self.config.num_train_timesteps // state.num_inference_steps

									snake_case_     =			state.common.alphas_cumprod
									snake_case_     =			state.final_alpha_cumprod

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

									snake_case_     =			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":
															snake_case_     =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
															snake_case_     =			model_output
									elif self.config.prediction_type == "sample":
															snake_case_     =			model_output
															snake_case_     =			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
									elif self.config.prediction_type == "v_prediction":
															snake_case_     =			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
															snake_case_     =			(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)
									snake_case_     =			self._get_variance(_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							)
									snake_case_     =			eta * variance ** 0.5

									# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
									snake_case_     =			(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
									snake_case_     =			alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

									if not return_dict:
															return (prev_sample, state)

									return FlaxDDIMSchedulerOutput(prev_sample=_UpperCamelCase							,     state=_UpperCamelCase							)







			def        snake_case__(  self				:       Optional[int]							,     _UpperCamelCase				:       DDIMSchedulerState							,     _UpperCamelCase				:       jnp.ndarray							,     _UpperCamelCase				:       jnp.ndarray							,     _UpperCamelCase				:       jnp.ndarray							,     )   ->jnp.ndarray:
									return add_noise_common(state.common							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							)







			def        snake_case__(  self				:       int							,     _UpperCamelCase				:       DDIMSchedulerState							,     _UpperCamelCase				:       jnp.ndarray							,     _UpperCamelCase				:       jnp.ndarray							,     _UpperCamelCase				:       jnp.ndarray							,     )   ->jnp.ndarray:
									return get_velocity_common(state.common							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							)







			def __len__(  self				:       List[str]							)   ->List[str]:
									return self.config.num_train_timesteps


lowerCAmelCase_ 			=			(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
lowerCAmelCase_ 			=			(-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False

						# Initialization of sum and number of digits.
						snake_case_     =			0
						snake_case_     =			0
						snake_case_     =			n
						# Calculation of digits of the number
						while temp > 0:
												number_of_digits += 1
												temp //= 10
						# Dividing number into separate digits and find Armstrong number
						snake_case_     =			n
						while temp > 0:
												snake_case_     =			temp % 10
												total += rem**number_of_digits
												temp //= 10
						return n == total


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False

						# Init a "histogram" of the digits
						snake_case_     =			[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
						snake_case_     =			0
						snake_case_     =			0
						snake_case_     =			n
						while temp > 0:
												snake_case_,					snake_case_     =			divmod(SCREAMING_SNAKE_CASE__						,	10 )
												digit_histogram[rem] += 1
												digit_total += 1

						for cnt, i in zip(SCREAMING_SNAKE_CASE__						,	range(len(SCREAMING_SNAKE_CASE__ ) ) ):
												total += cnt * i**digit_total

						return n == total


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False
						snake_case_     =			len(str(SCREAMING_SNAKE_CASE__ ) )  # the power that all digits will be raised to
						# check if sum of each digit multiplied expo times is equal to number
						return n == sum(int(SCREAMING_SNAKE_CASE__ ) ** expo for i in str(SCREAMING_SNAKE_CASE__ ) )


def 						__SCREAMING_SNAKE_CASE      ():
						snake_case_     =			int(input('''Enter an integer to see if it is an Armstrong number: ''' ).strip() )
						print(F'''{num} is {'' if armstrong_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )
						print(F'''{num} is {'' if narcissistic_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )
						print(F'''{num} is {'' if pluperfect_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()