



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							lowerCAmelCase_		(		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			str					       =	(EulerAncestralDiscreteScheduler,)
		SCREAMING_SNAKE_CASE_       :			str					       =	1_0







		def 					a_  (    self  :   List[str]       ,		**UpperCAmelCase_  :   Optional[Any]					)       ->		Tuple:


						'''simple docstring'''







						_UpperCAmelCase						:  List[str]     					=	{
						    '''num_train_timesteps''': 1100,
						    '''beta_start''': 0.0_001,
						    '''beta_end''': 0.02,
						    '''beta_schedule''': '''linear''',
						}

						config.update(**UpperCAmelCase_					)
						return config







		def 					a_  (    self  :   str					)       ->		Dict:


						'''simple docstring'''







						for timesteps in [10, 50, 100, 1000]:
										self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)







		def 					a_  (    self  :   str					)       ->		Optional[Any]:


						'''simple docstring'''







						for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]       ,		[0.0_002, 0.002, 0.02]					):
										self.check_over_configs(beta_start=UpperCAmelCase_       ,		beta_end=UpperCAmelCase_					)







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


						'''simple docstring'''







						for schedule in ["linear", "scaled_linear"]:
										self.check_over_configs(beta_schedule=UpperCAmelCase_					)







		def 					a_  (    self  :   int					)       ->		int:


						'''simple docstring'''







						for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=UpperCAmelCase_					)







		def 					a_  (    self  :   Any					)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  List[str]     					=	self.scheduler_classes[0]
						_UpperCAmelCase						:  Union[str, Any]     					=	self.get_scheduler_config()
						_UpperCAmelCase						:  Tuple     					=	scheduler_class(**UpperCAmelCase_					)

						scheduler.set_timesteps(self.num_inference_steps					)

						_UpperCAmelCase						:  Dict     					=	torch.manual_seed(0					)

						_UpperCAmelCase						:  List[Any]     					=	self.dummy_model()
						_UpperCAmelCase						:  int     					=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						_UpperCAmelCase						:  Any     					=	sample.to(UpperCAmelCase_					)

						for i, t in enumerate(scheduler.timesteps					):
										_UpperCAmelCase						:  Tuple     					=	scheduler.scale_model_input(UpperCAmelCase_       ,		UpperCAmelCase_					)

										_UpperCAmelCase						:  str     					=	model(UpperCAmelCase_       ,		UpperCAmelCase_					)

										_UpperCAmelCase						:  str     					=	scheduler.step(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_       ,		generator=UpperCAmelCase_					)
										_UpperCAmelCase						:  Optional[Any]     					=	output.prev_sample

						_UpperCAmelCase						:  Optional[int]     					=	torch.sum(torch.abs(UpperCAmelCase_					)					)
						_UpperCAmelCase						:  List[Any]     					=	torch.mean(torch.abs(UpperCAmelCase_					)					)

						assert abs(result_sum.item() - 152.3_192					) < 1E-2
						assert abs(result_mean.item() - 0.1_983					) < 1E-3







		def 					a_  (    self  :   Union[str, Any]					)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  List[str]     					=	self.scheduler_classes[0]
						_UpperCAmelCase						:  List[Any]     					=	self.get_scheduler_config(prediction_type='''v_prediction'''					)
						_UpperCAmelCase						:  List[Any]     					=	scheduler_class(**UpperCAmelCase_					)

						scheduler.set_timesteps(self.num_inference_steps					)

						_UpperCAmelCase						:  Any     					=	torch.manual_seed(0					)

						_UpperCAmelCase						:  Union[str, Any]     					=	self.dummy_model()
						_UpperCAmelCase						:  Tuple     					=	self.dummy_sample_deter * scheduler.init_noise_sigma
						_UpperCAmelCase						:  Optional[Any]     					=	sample.to(UpperCAmelCase_					)

						for i, t in enumerate(scheduler.timesteps					):
										_UpperCAmelCase						:  int     					=	scheduler.scale_model_input(UpperCAmelCase_       ,		UpperCAmelCase_					)

										_UpperCAmelCase						:  List[str]     					=	model(UpperCAmelCase_       ,		UpperCAmelCase_					)

										_UpperCAmelCase						:  Union[str, Any]     					=	scheduler.step(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_       ,		generator=UpperCAmelCase_					)
										_UpperCAmelCase						:  Dict     					=	output.prev_sample

						_UpperCAmelCase						:  Optional[Any]     					=	torch.sum(torch.abs(UpperCAmelCase_					)					)
						_UpperCAmelCase						:  Tuple     					=	torch.mean(torch.abs(UpperCAmelCase_					)					)

						assert abs(result_sum.item() - 108.4_439					) < 1E-2
						assert abs(result_mean.item() - 0.1_412					) < 1E-3







		def 					a_  (    self  :   str					)       ->		Optional[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  int     					=	self.scheduler_classes[0]
						_UpperCAmelCase						:  Optional[Any]     					=	self.get_scheduler_config()
						_UpperCAmelCase						:  List[Any]     					=	scheduler_class(**UpperCAmelCase_					)

						scheduler.set_timesteps(self.num_inference_steps       ,		device=UpperCAmelCase_					)
						_UpperCAmelCase						:  List[str]     					=	torch.manual_seed(0					)

						_UpperCAmelCase						:  List[str]     					=	self.dummy_model()
						_UpperCAmelCase						:  Dict     					=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						_UpperCAmelCase						:  List[str]     					=	sample.to(UpperCAmelCase_					)

						for t in scheduler.timesteps:
										_UpperCAmelCase						:  List[Any]     					=	scheduler.scale_model_input(UpperCAmelCase_       ,		UpperCAmelCase_					)

										_UpperCAmelCase						:  Tuple     					=	model(UpperCAmelCase_       ,		UpperCAmelCase_					)

										_UpperCAmelCase						:  Tuple     					=	scheduler.step(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_       ,		generator=UpperCAmelCase_					)
										_UpperCAmelCase						:  Any     					=	output.prev_sample

						_UpperCAmelCase						:  Optional[int]     					=	torch.sum(torch.abs(UpperCAmelCase_					)					)
						_UpperCAmelCase						:  Optional[Any]     					=	torch.mean(torch.abs(UpperCAmelCase_					)					)

						assert abs(result_sum.item() - 152.3_192					) < 1E-2
						assert abs(result_mean.item() - 0.1_983					) < 1E-3





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 							lowerCAmelCase_		:
		SCREAMING_SNAKE_CASE_       :			CommonSchedulerState

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

		# running values
		SCREAMING_SNAKE_CASE_       :			Optional[jnp.ndarray]					       =	None







		@classmethod
		def 					a_  (    cls  :   List[Any]       ,		UpperCAmelCase_  :   CommonSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray					)       ->		Optional[Any]:


						'''simple docstring'''







						return cls(common=UpperCAmelCase_       ,		init_noise_sigma=UpperCAmelCase_       ,		timesteps=UpperCAmelCase_       ,		sigmas=UpperCAmelCase_					)





@dataclass
class 							lowerCAmelCase_		(		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			LMSDiscreteSchedulerState





class 							lowerCAmelCase_		(		lowercase_							,		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			Union[str, Any]					       =	[e.name for e in FlaxKarrasDiffusionSchedulers]

		SCREAMING_SNAKE_CASE_       :			jnp.dtype







		@property
		def 					a_  (    self  :   Union[str, Any]					)       ->		List[Any]:


						'''simple docstring'''







						return True







		@register_to_config
		def __init__(    self  :   Optional[Any]       ,		UpperCAmelCase_  :   int = 1000       ,		UpperCAmelCase_  :   float = 0.0_001       ,		UpperCAmelCase_  :   float = 0.02       ,		UpperCAmelCase_  :   str = "linear"       ,		UpperCAmelCase_  :   Optional[jnp.ndarray] = None       ,		UpperCAmelCase_  :   str = "epsilon"       ,		UpperCAmelCase_  :   jnp.dtype = jnp.floataa       ,		)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  str     					=	dtype







		def 					a_  (    self  :   Tuple       ,		UpperCAmelCase_  :   Optional[CommonSchedulerState] = None					)       ->		LMSDiscreteSchedulerState:


						'''simple docstring'''







						if common is None:
										_UpperCAmelCase						:  int     					=	CommonSchedulerState.create(self					)

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

						# standard deviation of the initial noise distribution
						_UpperCAmelCase						:  Dict     					=	sigmas.max()

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







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int					)       ->		jnp.ndarray:


						'''simple docstring'''







						((_UpperCAmelCase)					, )						:  Optional[Any]     					=	jnp.where(state.timesteps == timestep       ,		size=1					)
						_UpperCAmelCase						:  List[str]     					=	step_index[0]

						_UpperCAmelCase						:  List[Any]     					=	state.sigmas[step_index]
						_UpperCAmelCase						:  Union[str, Any]     					=	sample / ((sigma**2 + 1) ** 0.5)
						return sample







		def 					a_  (    self  :   List[Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   str       ,		UpperCAmelCase_  :   Union[str, Any]					)       ->		Union[str, Any]:


						'''simple docstring'''







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

						_UpperCAmelCase						:  Optional[Any]     					=	integrate.quad(UpperCAmelCase_       ,		state.sigmas[t]       ,		state.sigmas[t + 1]       ,		epsrel=1E-4					)[0]

						return integrated_coeff







		def 					a_  (    self  :   Dict       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   Tuple = ()					)       ->		LMSDiscreteSchedulerState:


						'''simple docstring'''







						_UpperCAmelCase						:  List[str]     					=	jnp.linspace(self.config.num_train_timesteps - 1       ,		0       ,		UpperCAmelCase_       ,		dtype=self.dtype					)

						_UpperCAmelCase						:  Any     					=	jnp.floor(UpperCAmelCase_					).astype(jnp.intaa					)
						_UpperCAmelCase						:  Optional[Any]     					=	jnp.ceil(UpperCAmelCase_					).astype(jnp.intaa					)

						_UpperCAmelCase						:  str     					=	jnp.mod(UpperCAmelCase_       ,		1.0					)

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

						_UpperCAmelCase						:  int     					=	timesteps.astype(jnp.intaa					)

						# initial running values
						_UpperCAmelCase						:  List[str]     					=	jnp.zeros((0,) + shape       ,		dtype=self.dtype					)

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







		def 					a_  (    self  :   Tuple       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int = 4       ,		UpperCAmelCase_  :   bool = True       ,		)       ->		Union[FlaxLMSSchedulerOutput, 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'''					)

						_UpperCAmelCase						:  List[Any]     					=	state.sigmas[timestep]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
										_UpperCAmelCase						:  Optional[int]     					=	sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
										# * c_out + input * c_skip
										_UpperCAmelCase						:  Tuple     					=	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
						_UpperCAmelCase						:  Tuple     					=	(sample - pred_original_sample) / sigma
						_UpperCAmelCase						:  Tuple     					=	state.replace(derivatives=jnp.append(state.derivatives       ,		UpperCAmelCase_					)					)
						if len(state.derivatives					) > order:
										_UpperCAmelCase						:  Union[str, Any]     					=	state.replace(derivatives=jnp.delete(state.derivatives       ,		0					)					)

						# 3. Compute linear multistep coefficients
						_UpperCAmelCase						:  int     					=	min(timestep + 1       ,		UpperCAmelCase_					)
						_UpperCAmelCase						:  Optional[int]     					=	[self.get_lms_coefficient(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					) for curr_order in range(UpperCAmelCase_					)]

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

						if not return_dict:
										return (prev_sample, state)

						return FlaxLMSSchedulerOutput(prev_sample=UpperCAmelCase_       ,		state=UpperCAmelCase_					)







		def 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		)       ->		jnp.ndarray:


						'''simple docstring'''







						_UpperCAmelCase						:  Union[str, Any]     					=	state.sigmas[timesteps].flatten()
						_UpperCAmelCase						:  Union[str, Any]     					=	broadcast_to_shape_from_left(UpperCAmelCase_       ,		noise.shape					)

						_UpperCAmelCase						:  Any     					=	original_samples + noise * sigma

						return noisy_samples







		def __len__(    self  :   Dict					)       ->		Tuple:


						'''simple docstring'''







						return self.config.num_train_timesteps
