




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     __SCREAMING_SNAKE_CASE							:
					_lowerCamelCase		  =			42

					# setable values
					_lowerCamelCase		  =			42
					_lowerCamelCase		  =			42
					_lowerCamelCase		  =			42
					_lowerCamelCase		  =			None

					# running values
					_lowerCamelCase		  =			None

					@classmethod
					def      lowercase_							(    cls			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




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


@dataclass
class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			42


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase       ,					lowerCamelCase  ):
					_lowerCamelCase		  =			[e.name for e in FlaxKarrasDiffusionSchedulers]

					_lowerCamelCase		  =			42

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return True

					@register_to_config
					def __init__(    self			,	__lowercase = 1_000			,	__lowercase = 0.00_01			,	__lowercase = 0.02			,	__lowercase = "linear"			,	__lowercase = None			,	__lowercase = "epsilon"			,	__lowercase = jnp.floataa			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							dtype

					def      lowercase_							(    self			,	__lowercase = None ):







						'''simple docstring'''




						if common is None:
							lowerCamelCase_							:		List[Any]							       =							CommonSchedulerState.create(self )

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

						# standard deviation of the initial noise distribution
						lowerCamelCase_							:		Optional[Any]							       =							sigmas.max()

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

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						((lowerCamelCase_)       ,      )							:		Union[str, Any]							       =							jnp.where(state.timesteps == timestep			,	size=1 )
						lowerCamelCase_							:		int							       =							step_index[0]

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

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						def lms_derivative(__lowercase ):
							lowerCamelCase_							:		Tuple							       =							1.0
							for k in range(__lowercase ):
								if current_order == k:
									continue
								prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
							return prod

						lowerCamelCase_							:		Tuple							       =							integrate.quad(__lowercase			,	state.sigmas[t]			,	state.sigmas[t + 1]			,	epsrel=1E-4 )[0]

						return integrated_coeff

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase = () ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							jnp.linspace(self.config.num_train_timesteps - 1			,	0			,	__lowercase			,	dtype=self.dtype )

						lowerCamelCase_							:		str							       =							jnp.floor(__lowercase ).astype(jnp.intaa )
						lowerCamelCase_							:		Any							       =							jnp.ceil(__lowercase ).astype(jnp.intaa )

						lowerCamelCase_							:		Any							       =							jnp.mod(__lowercase			,	1.0 )

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

						lowerCamelCase_							:		str							       =							timesteps.astype(jnp.intaa )

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

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

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase = 4			,	__lowercase = True			,	):







						'''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' )

						lowerCamelCase_							:		Tuple							       =							state.sigmas[timestep]

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

						# 3. Compute linear multistep coefficients
						lowerCamelCase_							:		Union[str, Any]							       =							min(timestep + 1			,	__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							[self.get_lms_coefficient(__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ) for curr_order in range(__lowercase )]

						# 4. Compute previous sample based on the derivatives path
						lowerCamelCase_							:		Optional[int]							       =							sample + sum(
						    coeff * derivative for coeff, derivative in zip(__lowercase			,	reversed(state.derivatives ) ) )

						if not return_dict:
							return (prev_sample, state)

						return FlaxLMSSchedulerOutput(prev_sample=__lowercase			,	state=__lowercase )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	):







						'''simple docstring'''




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

						lowerCamelCase_							:		List[str]							       =							original_samples + noise * sigma

						return noisy_samples





					def __len__(    self ):







						'''simple docstring'''




						return self.config.num_train_timesteps






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			(HeunDiscreteScheduler,)
					_lowerCamelCase		  =			10

					def      lowercase_							(    self			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							{
						    'num_train_timesteps': 1_100,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**__lowercase )
						return config

					def      lowercase_							(    self ):







						'''simple docstring'''




						for timesteps in [10, 50, 100, 1_000]:
							self.check_over_configs(num_train_timesteps=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,	[0.00_02, 0.0_02, 0.02] ):
							self.check_over_configs(beta_start=__lowercase			,	beta_end=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




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

					def      lowercase_							(    self ):







						'''simple docstring'''




						for clip_sample_range in [1.0, 2.0, 3.0]:
							self.check_over_configs(clip_sample_range=__lowercase			,	clip_sample=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




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

					def      lowercase_							(    self ):







						'''simple docstring'''




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

						scheduler.set_timesteps(self.num_inference_steps )

						lowerCamelCase_							:		Optional[Any]							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[int]							       =							self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCamelCase_							:		Dict							       =							sample.to(__lowercase )

						for i, t in enumerate(scheduler.timesteps ):
							lowerCamelCase_							:		Any							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		int							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Optional[int]							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Tuple							       =							output.prev_sample

						lowerCamelCase_							:		str							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Tuple							       =							torch.mean(torch.abs(__lowercase ) )

						if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		str							       =							self.get_scheduler_config(prediction_type='v_prediction' )
						lowerCamelCase_							:		Optional[Any]							       =							scheduler_class(**__lowercase )

						scheduler.set_timesteps(self.num_inference_steps )

						lowerCamelCase_							:		Any							       =							self.dummy_model()
						lowerCamelCase_							:		str							       =							self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCamelCase_							:		List[Any]							       =							sample.to(__lowercase )

						for i, t in enumerate(scheduler.timesteps ):
							lowerCamelCase_							:		int							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		int							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Dict							       =							output.prev_sample

						lowerCamelCase_							:		Any							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		List[Any]							       =							torch.mean(torch.abs(__lowercase ) )

						if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 4.6934E-07 ) < 1E-2
							assert abs(result_mean.item() - 6.1112E-10 ) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 4.693428650170972E-07 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		int							       =							self.get_scheduler_config()
						lowerCamelCase_							:		Dict							       =							scheduler_class(**__lowercase )

						scheduler.set_timesteps(self.num_inference_steps			,	device=__lowercase )

						lowerCamelCase_							:		str							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[int]							       =							self.dummy_sample_deter.to(__lowercase ) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
							lowerCamelCase_							:		Tuple							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		List[Any]							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Union[str, Any]							       =							output.prev_sample

						lowerCamelCase_							:		Dict							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Any							       =							torch.mean(torch.abs(__lowercase ) )

						if str(__lowercase ).startswith('cpu' ):
							# The following sum varies between 148 and 156 on mps. Why?
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3
						elif str(__lowercase ).startswith('mps' ):
							# Larger tolerance on mps
							assert abs(result_mean.item() - 0.00_02 ) < 1E-2
						else:
							# CUDA
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		Optional[Any]							       =							self.get_scheduler_config()
						lowerCamelCase_							:		Dict							       =							scheduler_class(**__lowercase			,	use_karras_sigmas=__lowercase )

						scheduler.set_timesteps(self.num_inference_steps			,	device=__lowercase )

						lowerCamelCase_							:		int							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[Any]							       =							self.dummy_sample_deter.to(__lowercase ) * scheduler.init_noise_sigma
						lowerCamelCase_							:		Tuple							       =							sample.to(__lowercase )

						for t in scheduler.timesteps:
							lowerCamelCase_							:		str							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Dict							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Dict							       =							output.prev_sample

						lowerCamelCase_							:		Tuple							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Optional[Any]							       =							torch.mean(torch.abs(__lowercase ) )

						assert abs(result_sum.item() - 0.0_00_15 ) < 1E-2
						assert abs(result_mean.item() - 1.9869554535034695E-07 ) < 1E-2
