"""simple docstring"""


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        __UpperCAmelCase						:
	A__				:       CommonSchedulerState
	A__				:       jnp.ndarray

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

	@classmethod
	def 		_a     (				cls     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
							return cls(
							    common=lowercase_     ,			final_alpha_cumprod=lowercase_     ,			init_noise_sigma=lowercase_     ,			timesteps=lowercase_     ,			)






@dataclass
class        __UpperCAmelCase						(     SCREAMING_SNAKE_CASE_     ):
	A__				:       DDIMSchedulerState






class        __UpperCAmelCase						(     SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_     ):
	A__				:       Optional[Any]  							=	[e.name for e in FlaxKarrasDiffusionSchedulers]

	A__				:       jnp.dtype

	@property
	def 		_a     (				self				):
							return True

	@register_to_config
	def __init__(				self     ,			_lowerCamelCase = 1000     ,			_lowerCamelCase = 0.0_0_0_1     ,			_lowerCamelCase = 0.0_2     ,			_lowerCamelCase = "linear"     ,			_lowerCamelCase = None     ,			_lowerCamelCase = True     ,			_lowerCamelCase = 0     ,			_lowerCamelCase = "epsilon"     ,			_lowerCamelCase = jnp.floataa     ,			):
							lowerCamelCase__ =dtype

	def 		_a     (				self     ,			_lowerCamelCase = None				):
							if common is None:
													lowerCamelCase__ =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.
							lowerCamelCase__ =(
							    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
							lowerCamelCase__ =jnp.array(1.0     ,			dtype=self.dtype				)

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

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

	def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = None				):
							return sample

	def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = ()				):
							lowerCamelCase__ =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
							lowerCamelCase__ =(jnp.arange(0     ,			lowercase_				) * step_ratio).round()[::-1] + self.config.steps_offset

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

	def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				):
							lowerCamelCase__ =state.common.alphas_cumprod[timestep]
							lowerCamelCase__ =jnp.where(
							    prev_timestep >= 0     ,			state.common.alphas_cumprod[prev_timestep]     ,			state.final_alpha_cumprod				)
							lowerCamelCase__ =1 - alpha_prod_t
							lowerCamelCase__ =1 - alpha_prod_t_prev

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

							return variance

	def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = 0.0     ,			_lowerCamelCase = 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"				)

							# 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)
							lowerCamelCase__ =timestep - self.config.num_train_timesteps // state.num_inference_steps

							lowerCamelCase__ =state.common.alphas_cumprod
							lowerCamelCase__ =state.final_alpha_cumprod

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

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

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

							if not return_dict:
													return (prev_sample, state)

							return FlaxDDIMSchedulerOutput(prev_sample=lowercase_     ,			state=lowercase_				)

	def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
							return add_noise_common(state.common     ,			lowercase_     ,			lowercase_     ,			lowercase_				)

	def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
							return get_velocity_common(state.common     ,			lowercase_     ,			lowercase_     ,			lowercase_				)






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



"""simple docstring"""


from __future__ import annotations

from enum import Enum






class        __UpperCAmelCase						(     __lowerCAmelCase     ):
				A__				:       List[Any]  							=	2_4
				A__				:       List[str]  							=	2_1
				A__				:       List[Any]  							=	1_8
				A__				:       Optional[Any]  							=	1_5
				A__				:       List[Any]  							=	1_2
				A__				:       Optional[int]  							=	9
				A__				:       List[Any]  							=	6
				A__				:       int  							=	3
				A__				:       Optional[int]  							=	2
				A__				:       int  							=	1
				A__				:       Optional[int]  							=	-1
				A__				:       Dict  							=	-2
				A__				:       Optional[Any]  							=	-3
				A__				:       str  							=	-6
				A__				:       Union[str, Any]  							=	-9
				A__				:       Optional[Any]  							=	-1_2
				A__				:       int  							=	-1_5
				A__				:       Union[str, Any]  							=	-1_8
				A__				:       Any  							=	-2_1
				A__				:       Union[str, Any]  							=	-2_4






class        __UpperCAmelCase						(     __lowerCAmelCase     ):
				A__				:       Optional[int]  							=	8
				A__				:       Any  							=	7
				A__				:       Any  							=	6
				A__				:       Tuple  							=	5
				A__				:       Optional[Any]  							=	4
				A__				:       str  							=	3
				A__				:       Dict  							=	2
				A__				:       Tuple  							=	1





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   )							-> float:




						'''simple docstring'''



						if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												lowerCamelCase__ =SIUnit[known_prefix.lower()]
						if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												lowerCamelCase__ =SIUnit[unknown_prefix.lower()]
						lowerCamelCase__ =known_amount * (
						    10 ** (known_prefix.value - unknown_prefix.value)
						)
						return unknown_amount





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   )							-> float:




						'''simple docstring'''



						if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												lowerCamelCase__ =BinaryUnit[known_prefix.lower()]
						if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												lowerCamelCase__ =BinaryUnit[unknown_prefix.lower()]
						lowerCamelCase__ =known_amount * (
						    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
						)
						return unknown_amount


if __name__ == "__main__":
	import doctest

	doctest.testmod()


