






"""simple docstring"""



import unittest

from diffusers.models.unet_ad_blocks import *  # noqa F403
from diffusers.utils import torch_device

from .test_unet_blocks_common import UNetBlockTesterMixin







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Optional[Any]             =			DownBlockaD  # noqa F405
			_UpperCAmelCase		:str             =			"down"


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Union[str, Any]									=[-0.0_232, -0.9_869, 0.8_054, -0.0_637, -0.1_688, -1.4_264, 0.4_470, -1.3_394, 0.0_904]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Tuple             =			ResnetDownsampleBlockaD  # noqa F405
			_UpperCAmelCase		:Optional[int]             =			"down"


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_	:							Union[str, Any]									=[0.0_710, 0.2_410, -0.7_320, -1.0_757, -1.1_343, 0.3_540, -0.0_133, -0.2_576, 0.0_948]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:str             =			AttnDownBlockaD  # noqa F405
			_UpperCAmelCase		:Any             =			"down"


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							Union[str, Any]									=[0.0_636, 0.8_964, -0.6_234, -1.0_131, 0.0_844, 0.4_935, 0.3_437, 0.0_911, -0.2_957]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Tuple             =			CrossAttnDownBlockaD  # noqa F405
			_UpperCAmelCase		:List[str]             =			"down"


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_							,						lowerCamelCase_	:							List[str]									=super().prepare_init_args_and_inputs_for_common()
										lowerCamelCase_	:							int									=32
										return init_dict, inputs_dict


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							Optional[Any]									=[0.2_238, -0.7_396, -0.2_255, -0.3_829, 0.1_925, 1.1_665, 0.0_603, -0.7_295, 0.1_983]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Union[str, Any]             =			SimpleCrossAttnDownBlockaD  # noqa F405
			_UpperCAmelCase		:List[Any]             =			"down"


			@property
			def      UpperCAmelCase__						(				self					:  Any ):
										return super().get_dummy_input(include_encoder_hidden_states=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_							,						lowerCamelCase_	:							Optional[int]									=super().prepare_init_args_and_inputs_for_common()
										lowerCamelCase_	:							Optional[int]									=32
										return init_dict, inputs_dict


			@unittest.skipIf(torch_device == "mps"       ,	"MPS result is not consistent" )
			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							Optional[Any]									=[0.7_921, -0.0_992, -0.1_962, -0.7_695, -0.4_242, 0.7_804, 0.4_737, 0.2_765, 0.3_338]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Tuple             =			SkipDownBlockaD  # noqa F405
			_UpperCAmelCase		:Tuple             =			"down"


			@property
			def      UpperCAmelCase__						(				self					:  Any ):
										return super().get_dummy_input(include_skip_sample=snake_case__ )


			def      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_	:							int									=[-0.0_845, -0.2_087, -0.2_465, 0.0_971, 0.1_900, -0.0_484, 0.2_664, 0.4_179, 0.5_069]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Dict             =			AttnSkipDownBlockaD  # noqa F405
			_UpperCAmelCase		:Tuple             =			"down"


			@property
			def      UpperCAmelCase__						(				self					:  Dict ):
										return super().get_dummy_input(include_skip_sample=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Any ):
										lowerCamelCase_	:							str									=[0.5_539, 0.1_609, 0.4_924, 0.0_537, -0.1_995, 0.4_050, 0.0_979, -0.2_721, -0.0_642]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Any             =			DownEncoderBlockaD  # noqa F405
			_UpperCAmelCase		:Any             =			"down"


			@property
			def      UpperCAmelCase__						(				self					:  int ):
										return super().get_dummy_input(include_temb=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							Union[str, Any]									={
										    "in_channels": 32,
										    "out_channels": 32,
										}
										lowerCamelCase_	:							List[str]									=self.dummy_input
										return init_dict, inputs_dict


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							List[str]									=[1.1_102, 0.5_302, 0.4_872, -0.0_023, -0.8_042, 0.0_483, -0.3_489, -0.5_632, 0.7_626]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Tuple             =			AttnDownEncoderBlockaD  # noqa F405
			_UpperCAmelCase		:List[str]             =			"down"


			@property
			def      UpperCAmelCase__						(				self					:  List[Any] ):
										return super().get_dummy_input(include_temb=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_	:							Tuple									={
										    "in_channels": 32,
										    "out_channels": 32,
										}
										lowerCamelCase_	:							str									=self.dummy_input
										return init_dict, inputs_dict


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_	:							Union[str, Any]									=[0.8_966, -0.1_486, 0.8_568, 0.8_141, -0.9_046, -0.1_342, -0.0_972, -0.7_417, 0.1_538]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:int             =			UNetMidBlockaD  # noqa F405
			_UpperCAmelCase		:Optional[Any]             =			"mid"


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							Optional[int]									={
										    "in_channels": 32,
										    "temb_channels": 128,
										}
										lowerCamelCase_	:							Optional[Any]									=self.dummy_input
										return init_dict, inputs_dict


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							Any									=[-0.1_062, 1.7_248, 0.3_494, 1.4_569, -0.0_910, -1.2_421, -0.9_984, 0.6_736, 1.0_028]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Tuple             =			UNetMidBlockaDCrossAttn  # noqa F405
			_UpperCAmelCase		:str             =			"mid"


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_							,						lowerCamelCase_	:							List[Any]									=super().prepare_init_args_and_inputs_for_common()
										lowerCamelCase_	:							Any									=32
										return init_dict, inputs_dict


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							str									=[0.0_187, 2.4_220, 0.4_484, 1.1_203, -0.6_121, -1.5_122, -0.8_270, 0.7_851, 1.8_335]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:List[Any]             =			UNetMidBlockaDSimpleCrossAttn  # noqa F405
			_UpperCAmelCase		:Optional[int]             =			"mid"


			@property
			def      UpperCAmelCase__						(				self					:  List[Any] ):
										return super().get_dummy_input(include_encoder_hidden_states=snake_case__ )


			def      UpperCAmelCase__						(				self					:  int ):
										lowerCamelCase_							,						lowerCamelCase_	:							Any									=super().prepare_init_args_and_inputs_for_common()
										lowerCamelCase_	:							List[str]									=32
										return init_dict, inputs_dict


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							Any									=[0.7_143, 1.9_974, 0.5_448, 1.3_977, 0.1_282, -1.1_237, -1.4_238, 0.5_530, 0.8_880]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Optional[int]             =			UpBlockaD  # noqa F405
			_UpperCAmelCase		:int             =			"up"


			@property
			def      UpperCAmelCase__						(				self					:  int ):
										return super().get_dummy_input(include_res_hidden_states_tuple=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Any ):
										lowerCamelCase_	:							Tuple									=[-0.2_041, -0.4_165, -0.3_022, 0.0_041, -0.6_628, -0.7_053, 0.1_928, -0.0_325, 0.0_523]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:int             =			ResnetUpsampleBlockaD  # noqa F405
			_UpperCAmelCase		:Any             =			"up"


			@property
			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										return super().get_dummy_input(include_res_hidden_states_tuple=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							Any									=[0.2_287, 0.3_549, -0.1_346, 0.4_797, -0.1_715, -0.9_649, 0.7_305, -0.5_864, -0.6_244]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:List[Any]             =			CrossAttnUpBlockaD  # noqa F405
			_UpperCAmelCase		:Optional[int]             =			"up"


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										return super().get_dummy_input(include_res_hidden_states_tuple=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_							,						lowerCamelCase_	:							List[str]									=super().prepare_init_args_and_inputs_for_common()
										lowerCamelCase_	:							Union[str, Any]									=32
										return init_dict, inputs_dict


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							Union[str, Any]									=[-0.1_403, -0.3_515, -0.0_420, -0.1_425, 0.3_167, 0.5_094, -0.2_181, 0.5_931, 0.5_582]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Optional[Any]             =			SimpleCrossAttnUpBlockaD  # noqa F405
			_UpperCAmelCase		:Optional[Any]             =			"up"


			@property
			def      UpperCAmelCase__						(				self					:  List[str] ):
										return super().get_dummy_input(include_res_hidden_states_tuple=snake_case__       ,	include_encoder_hidden_states=snake_case__ )


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_							,						lowerCamelCase_	:							Any									=super().prepare_init_args_and_inputs_for_common()
										lowerCamelCase_	:							List[Any]									=32
										return init_dict, inputs_dict


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_	:							List[Any]									=[0.2_645, 0.1_480, 0.0_909, 0.8_044, -0.9_758, -0.9_083, 0.0_994, -1.1_453, -0.7_402]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Any             =			AttnUpBlockaD  # noqa F405
			_UpperCAmelCase		:Dict             =			"up"


			@property
			def      UpperCAmelCase__						(				self					:  int ):
										return super().get_dummy_input(include_res_hidden_states_tuple=snake_case__ )


			@unittest.skipIf(torch_device == "mps"       ,	"MPS result is not consistent" )
			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							int									=[0.0_979, 0.1_326, 0.0_021, 0.0_659, 0.2_249, 0.0_059, 0.1_132, 0.5_952, 0.1_033]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:str             =			SkipUpBlockaD  # noqa F405
			_UpperCAmelCase		:Tuple             =			"up"


			@property
			def      UpperCAmelCase__						(				self					:  Tuple ):
										return super().get_dummy_input(include_res_hidden_states_tuple=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_	:							List[str]									=[-0.0_893, -0.1_234, -0.1_506, -0.0_332, 0.0_123, -0.0_211, 0.0_566, 0.0_143, 0.0_362]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:List[str]             =			AttnSkipUpBlockaD  # noqa F405
			_UpperCAmelCase		:Any             =			"up"


			@property
			def      UpperCAmelCase__						(				self					:  str ):
										return super().get_dummy_input(include_res_hidden_states_tuple=snake_case__ )


			def      UpperCAmelCase__						(				self					:  int ):
										lowerCamelCase_	:							Union[str, Any]									=[0.0_361, 0.0_617, 0.2_787, -0.0_350, 0.0_342, 0.3_421, -0.0_843, 0.0_913, 0.3_015]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:str             =			UpDecoderBlockaD  # noqa F405
			_UpperCAmelCase		:Union[str, Any]             =			"up"


			@property
			def      UpperCAmelCase__						(				self					:  List[str] ):
										return super().get_dummy_input(include_temb=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							Tuple									={"in_channels": 32, "out_channels": 32}

										lowerCamelCase_	:							str									=self.dummy_input
										return init_dict, inputs_dict


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							Optional[Any]									=[0.4_404, 0.1_998, -0.9_886, -0.3_320, -0.3_128, -0.7_034, -0.6_955, -0.2_338, -0.3_137]
										super().test_output(snake_case__ )







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:str             =			AttnUpDecoderBlockaD  # noqa F405
			_UpperCAmelCase		:Optional[Any]             =			"up"


			@property
			def      UpperCAmelCase__						(				self					:  Any ):
										return super().get_dummy_input(include_temb=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Dict									={"in_channels": 32, "out_channels": 32}

										lowerCamelCase_	:							Union[str, Any]									=self.dummy_input
										return init_dict, inputs_dict


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							int									=[0.6_738, 0.4_491, 0.1_055, 1.0_710, 0.7_316, 0.3_339, 0.3_352, 0.1_023, 0.3_568]
										super().test_output(snake_case__ )











"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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        lowercase__      :
			_UpperCAmelCase		:CommonSchedulerState

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


			@classmethod
			def      UpperCAmelCase__						(				cls					:  int       ,	snake_case__					:  CommonSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray ):
										return cls(common=snake_case__       ,	init_noise_sigma=snake_case__       ,	timesteps=snake_case__ )







@dataclass
class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:DDPMSchedulerState







class        lowercase__      (							snake_case__,       snake_case__       ):
			_UpperCAmelCase		:Any             =			[e.name for e in FlaxKarrasDiffusionSchedulers]

			_UpperCAmelCase		:jnp.dtype


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										return True


			@register_to_config
			def __init__(				self					:  Optional[int]       ,	snake_case__					:  int = 1000       ,	snake_case__					:  float = 0.0_001       ,	snake_case__					:  float = 0.02       ,	snake_case__					:  str = "linear"       ,	snake_case__					:  Optional[jnp.ndarray] = None       ,	snake_case__					:  str = "fixed_small"       ,	snake_case__					:  bool = True       ,	snake_case__					:  str = "epsilon"       ,	snake_case__					:  jnp.dtype = jnp.floataa       ,	):
										lowerCamelCase_	:							str									=dtype


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  Optional[CommonSchedulerState] = None ):
										if common is None:
																	lowerCamelCase_	:							int									=CommonSchedulerState.create(self )

										# standard deviation of the initial noise distribution
										lowerCamelCase_	:							Optional[Any]									=jnp.array(1.0       ,	dtype=self.dtype )

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

										return DDPMSchedulerState.create(
										    common=snake_case__       ,	init_noise_sigma=snake_case__       ,	timesteps=snake_case__       ,	)


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[int] = None ):
										return sample


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  int       ,	snake_case__					:  Tuple = () ):
										lowerCamelCase_	:							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
										lowerCamelCase_	:							List[str]									=(jnp.arange(0       ,	snake_case__ ) * step_ratio).round()[::-1]

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


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  List[Any]=None       ,	snake_case__					:  Any=None ):
										lowerCamelCase_	:							List[str]									=state.common.alphas_cumprod[t]
										lowerCamelCase_	:							Union[str, Any]									=jnp.where(t > 0       ,	state.common.alphas_cumprod[t - 1]       ,	jnp.array(1.0       ,	dtype=self.dtype ) )

										# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
										# and sample from it to get previous sample
										# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
										lowerCamelCase_	:							Tuple									=(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

										if variance_type is None:
																	lowerCamelCase_	:							List[Any]									=self.config.variance_type

										# hacks - were probably added for training stability
										if variance_type == "fixed_small":
																	lowerCamelCase_	:							List[str]									=jnp.clip(snake_case__       ,	a_min=1E-20 )
										# for rl-diffuser https://arxiv.org/abs/2205.09991
										elif variance_type == "fixed_small_log":
																	lowerCamelCase_	:							Dict									=jnp.log(jnp.clip(snake_case__       ,	a_min=1E-20 ) )
										elif variance_type == "fixed_large":
																	lowerCamelCase_	:							Optional[Any]									=state.common.betas[t]
										elif variance_type == "fixed_large_log":
																	# Glide max_log
																	lowerCamelCase_	:							Any									=jnp.log(state.common.betas[t] )
										elif variance_type == "learned":
																	return predicted_variance
										elif variance_type == "learned_range":
																	lowerCamelCase_	:							List[str]									=variance
																	lowerCamelCase_	:							Optional[int]									=state.common.betas[t]
																	lowerCamelCase_	:							Dict									=(predicted_variance + 1) / 2
																	lowerCamelCase_	:							Dict									=frac * max_log + (1 - frac) * min_log

										return variance


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  int       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[jax.random.KeyArray] = None       ,	snake_case__					:  bool = True       ,	):
										lowerCamelCase_	:							Union[str, Any]									=timestep

										if key is None:
																	lowerCamelCase_	:							Dict									=jax.random.PRNGKey(0 )

										if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																	lowerCamelCase_							,						lowerCamelCase_	:							Optional[Any]									=jnp.split(snake_case__       ,	sample.shape[1]       ,	axis=1 )
										else:
																	lowerCamelCase_	:							List[str]									=None

										# 1. compute alphas, betas
										lowerCamelCase_	:							Union[str, Any]									=state.common.alphas_cumprod[t]
										lowerCamelCase_	:							Dict									=jnp.where(t > 0       ,	state.common.alphas_cumprod[t - 1]       ,	jnp.array(1.0       ,	dtype=self.dtype ) )
										lowerCamelCase_	:							Any									=1 - alpha_prod_t
										lowerCamelCase_	:							List[str]									=1 - alpha_prod_t_prev

										# 2. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
										if self.config.prediction_type == "epsilon":
																	lowerCamelCase_	:							int									=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										elif self.config.prediction_type == "sample":
																	lowerCamelCase_	:							List[Any]									=model_output
										elif self.config.prediction_type == "v_prediction":
																	lowerCamelCase_	:							Tuple									=(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
										else:
																	raise ValueError(
																	    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
																	    " for the FlaxDDPMScheduler." )

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
																	lowerCamelCase_	:							List[Any]									=jnp.clip(snake_case__       ,	-1       ,	1 )

										# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										lowerCamelCase_	:							int									=(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
										lowerCamelCase_	:							Optional[Any]									=state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

										# 5. Compute predicted previous sample µ_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										lowerCamelCase_	:							Any									=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

										# 6. Add noise
										def random_variance():
																	lowerCamelCase_	:							Union[str, Any]									=jax.random.split(snake_case__       ,	num=1 )
																	lowerCamelCase_	:							List[Any]									=jax.random.normal(snake_case__       ,	shape=model_output.shape       ,	dtype=self.dtype )
																	return (self._get_variance(snake_case__       ,	snake_case__       ,	predicted_variance=snake_case__ ) ** 0.5) * noise

										lowerCamelCase_	:							Tuple									=jnp.where(t > 0       ,	random_variance()       ,	jnp.zeros(model_output.shape       ,	dtype=self.dtype ) )

										lowerCamelCase_	:							str									=pred_prev_sample + variance

										if not return_dict:
																	return (pred_prev_sample, state)

										return FlaxDDPMSchedulerOutput(prev_sample=snake_case__       ,	state=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return add_noise_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return get_velocity_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


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



