

# 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 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						CommonSchedulerState

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


							@classmethod
							def 				__lowercase				(			cls       ,							_a       ,							_a       ,							_a       )    ->    str:
												return cls(common=_a       ,							init_noise_sigma=_a       ,							timesteps=_a       )



@dataclass
class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						DDPMSchedulerState



class 				UpperCAmelCase_		(			__lowercase				,       __lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						int	    =    [e.name for e in FlaxKarrasDiffusionSchedulers]

							UpperCAmelCase__       :						jnp.dtype


							@property
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												return True


							@register_to_config
							def __init__(			self       ,							_a = 1_0_0_0       ,							_a = 0.0001       ,							_a = 0.02       ,							_a = "linear"       ,							_a = None       ,							_a = "fixed_small"       ,							_a = True       ,							_a = "epsilon"       ,							_a = jnp.floataa       ,							)    ->    List[Any]:
												_a	:    List[str]         =		dtype


							def 				__lowercase				(			self       ,							_a = None       )    ->    DDPMSchedulerState:
												if common is None:
																	_a	:    List[str]         =		CommonSchedulerState.create(self       )

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

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

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


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = None       )    ->    jnp.ndarray:
												return sample


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = ()       )    ->    DDPMSchedulerState:
												_a	:    str         =		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
												_a	:    Optional[Any]         =		(jnp.arange(0       ,							_a       ) * step_ratio).round()[::-1]

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


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a=None       ,							_a=None       )    ->    Tuple:
												_a	:    int         =		state.common.alphas_cumprod[t]
												_a	:    List[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
												_a	:    int         =		(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

												if variance_type is None:
																	_a	:    str         =		self.config.variance_type

												# hacks - were probably added for training stability
												if variance_type == "fixed_small":
																	_a	:    List[Any]         =		jnp.clip(_a       ,							a_min=1e-2_0       )
												# for rl-diffuser https://arxiv.org/abs/2205.09991
												elif variance_type == "fixed_small_log":
																	_a	:    int         =		jnp.log(jnp.clip(_a       ,							a_min=1e-2_0       )       )
												elif variance_type == "fixed_large":
																	_a	:    Tuple         =		state.common.betas[t]
												elif variance_type == "fixed_large_log":
																	# Glide max_log
																	_a	:    List[Any]         =		jnp.log(state.common.betas[t]       )
												elif variance_type == "learned":
																	return predicted_variance
												elif variance_type == "learned_range":
																	_a	:    Tuple         =		variance
																	_a	:    str         =		state.common.betas[t]
																	_a	:    Optional[int]         =		(predicted_variance + 1) / 2
																	_a	:    Union[str, Any]         =		frac * max_log + (1 - frac) * min_log

												return variance


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a = None       ,							_a = True       ,							)    ->    Union[FlaxDDPMSchedulerOutput, Tuple]:
												_a	:    List[Any]         =		timestep

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

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

												# 1. compute alphas, betas
												_a	:    Optional[Any]         =		state.common.alphas_cumprod[t]
												_a	:    Any         =		jnp.where(t > 0       ,							state.common.alphas_cumprod[t - 1]       ,							jnp.array(1.0       ,							dtype=self.dtype       )       )
												_a	:    Dict         =		1 - alpha_prod_t
												_a	:    List[Any]         =		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":
																	_a	:    int         =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
												elif self.config.prediction_type == "sample":
																	_a	:    List[Any]         =		model_output
												elif self.config.prediction_type == "v_prediction":
																	_a	:    List[Any]         =		(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:
																	_a	:    Optional[int]         =		jnp.clip(_a       ,							-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
												_a	:    int         =		(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
												_a	:    Tuple         =		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
												_a	:    Any         =		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

												_a	:    Union[str, Any]         =		jnp.where(t > 0       ,							random_variance()       ,							jnp.zeros(model_output.shape       ,							dtype=self.dtype       )       )

												_a	:    List[str]         =		pred_prev_sample + variance

												if not return_dict:
																	return (pred_prev_sample, state)

												return FlaxDDPMSchedulerOutput(prev_sample=_a       ,							state=_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							)    ->    jnp.ndarray:
												return add_noise_common(state.common       ,							_a       ,							_a       ,							_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							)    ->    jnp.ndarray:
												return get_velocity_common(state.common       ,							_a       ,							_a       ,							_a       )




							def __len__(			self       )    ->    Optional[int]:
												return self.config.num_train_timesteps






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[Any]	    =    (DDPMScheduler,)


							def 				__lowercase				(			self       ,							**_a       )    ->    Any:
												_a	:    List[Any]         =		{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''variance_type''': '''fixed_small''',
												    '''clip_sample''': True,
												}

												config.update(**_a       )
												return config


							def 				__lowercase				(			self       )    ->    Any:
												for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_a       )


							def 				__lowercase				(			self       )    ->    List[Any]:
												for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,							[0.002, 0.02, 0.2, 2]       ):
																	self.check_over_configs(beta_start=_a       ,							beta_end=_a       )


							def 				__lowercase				(			self       )    ->    List[str]:
												for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=_a       )


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=_a       )


							def 				__lowercase				(			self       )    ->    str:
												for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=_a       )


							def 				__lowercase				(			self       )    ->    Dict:
												self.check_over_configs(thresholding=_a       )
												for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=_a       ,							prediction_type=_a       ,							sample_max_value=_a       ,							)


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(prediction_type=_a       )


							def 				__lowercase				(			self       )    ->    int:
												for t in [0, 5_0_0, 9_9_9]:
																	self.check_over_forward(time_step=_a       )


							def 				__lowercase				(			self       )    ->    int:
												_a	:    int         =		self.scheduler_classes[0]
												_a	:    List[Any]         =		self.get_scheduler_config()
												_a	:    Dict         =		scheduler_class(**_a       )

												assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ) - 0.0_0979       )       ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ) - 0.02       )       ) < 1e-5


							def 				__lowercase				(			self       )    ->    Tuple:
												_a	:    int         =		self.scheduler_classes[0]
												_a	:    int         =		self.get_scheduler_config()
												_a	:    int         =		scheduler_class(**_a       )

												_a	:    Optional[int]         =		len(_a       )

												_a	:    Optional[Any]         =		self.dummy_model()
												_a	:    str         =		self.dummy_sample_deter
												_a	:    Union[str, Any]         =		torch.manual_seed(0       )

												for t in reversed(range(_a       )       ):
																	# 1. predict noise residual
																	_a	:    str         =		model(_a       ,							_a       )

																	# 2. predict previous mean of sample x_t-1
																	_a	:    Optional[int]         =		scheduler.step(_a       ,							_a       ,							_a       ,							generator=_a       ).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	_a	:    List[Any]         =		pred_prev_sample

												_a	:    str         =		torch.sum(torch.abs(_a       )       )
												_a	:    Optional[Any]         =		torch.mean(torch.abs(_a       )       )

												assert abs(result_sum.item() - 258.9606       ) < 1e-2
												assert abs(result_mean.item() - 0.3372       ) < 1e-3


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Optional[int]         =		self.scheduler_classes[0]
												_a	:    Optional[Any]         =		self.get_scheduler_config(prediction_type='''v_prediction'''       )
												_a	:    Union[str, Any]         =		scheduler_class(**_a       )

												_a	:    Dict         =		len(_a       )

												_a	:    int         =		self.dummy_model()
												_a	:    Tuple         =		self.dummy_sample_deter
												_a	:    List[Any]         =		torch.manual_seed(0       )

												for t in reversed(range(_a       )       ):
																	# 1. predict noise residual
																	_a	:    Dict         =		model(_a       ,							_a       )

																	# 2. predict previous mean of sample x_t-1
																	_a	:    int         =		scheduler.step(_a       ,							_a       ,							_a       ,							generator=_a       ).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	_a	:    str         =		pred_prev_sample

												_a	:    str         =		torch.sum(torch.abs(_a       )       )
												_a	:    Tuple         =		torch.mean(torch.abs(_a       )       )

												assert abs(result_sum.item() - 202.0296       ) < 1e-2
												assert abs(result_mean.item() - 0.2631       ) < 1e-3


							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    Union[str, Any]         =		self.scheduler_classes[0]
												_a	:    Tuple         =		self.get_scheduler_config()
												_a	:    Any         =		scheduler_class(**_a       )

												_a	:    Optional[Any]         =		[1_0_0, 8_7, 5_0, 1, 0]

												scheduler.set_timesteps(timesteps=_a       )

												_a	:    Optional[int]         =		scheduler.timesteps

												for i, timestep in enumerate(_a       ):
																	if i == len(_a       ) - 1:
																						_a	:    Dict         =		-1
																	else:
																						_a	:    Tuple         =		timesteps[i + 1]

																	_a	:    Optional[Any]         =		scheduler.previous_timestep(_a       )
																	_a	:    Optional[Any]         =		prev_t.item()

																	self.assertEqual(_a       ,							_a       )


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Dict         =		self.scheduler_classes[0]
												_a	:    List[str]         =		self.get_scheduler_config()
												_a	:    Tuple         =		scheduler_class(**_a       )

												_a	:    str         =		[1_0_0, 8_7, 5_0, 5_1, 0]

												with self.assertRaises(_a       ,							msg='''`custom_timesteps` must be in descending order.'''       ):
																	scheduler.set_timesteps(timesteps=_a       )


							def 				__lowercase				(			self       )    ->    str:
												_a	:    List[str]         =		self.scheduler_classes[0]
												_a	:    List[str]         =		self.get_scheduler_config()
												_a	:    Dict         =		scheduler_class(**_a       )

												_a	:    Union[str, Any]         =		[1_0_0, 8_7, 5_0, 1, 0]
												_a	:    Optional[Any]         =		len(_a       )

												with self.assertRaises(_a       ,							msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''       ):
																	scheduler.set_timesteps(num_inference_steps=_a       ,							timesteps=_a       )




							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Dict         =		self.scheduler_classes[0]
												_a	:    Union[str, Any]         =		self.get_scheduler_config()
												_a	:    int         =		scheduler_class(**_a       )

												_a	:    str         =		[scheduler.config.num_train_timesteps]

												with self.assertRaises(
												    _a       ,							msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''       ,							):
																	scheduler.set_timesteps(timesteps=_a       )



