



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							(EulerAncestralDiscreteScheduler,)
	__A					=							10




	def   UpperCamelCase_ (self	, **lowerCamelCase_	):
						"""simple docstring"""

						a        =     {
						    "num_train_timesteps": 1100,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						}

						config.update(**lowerCamelCase_	)
						return config




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	, [0.0002, 0.002, 0.02]	):
											self.check_over_configs(beta_start=lowerCamelCase_	, beta_end=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config()
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	)

						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						a        =     sample.to(lowerCamelCase_	)

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

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 152.3192	) < 1E-2
						assert abs(result_mean.item() - 0.1983	) < 1E-3




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config(prediction_type="v_prediction"	)
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	)

						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma
						a        =     sample.to(lowerCamelCase_	)

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

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 108.4439	) < 1E-2
						assert abs(result_mean.item() - 0.1412	) < 1E-3




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config()
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	, device=lowerCamelCase_	)
						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						a        =     sample.to(lowerCamelCase_	)

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

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 152.3192	) < 1E-2
						assert abs(result_mean.item() - 0.1983	) < 1E-3





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left



@flax.struct.dataclass
class 			_lowercase							:





	"""simple docstring"""
	# setable values
	__A					=							None
	__A					=							None
	__A					=							None




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls()




@dataclass
class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							42
	__A					=							42
	__A					=							None




class 			_lowercase							(     lowerCAmelCase,   lowerCAmelCase			):





	"""simple docstring"""




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return True




	@register_to_config
	def __init__(self	, lowerCamelCase_ = 2000	, lowerCamelCase_ = 0.15	, lowerCamelCase_ = 0.01	, lowerCamelCase_ = 1348.0	, lowerCamelCase_ = 1E-5	, lowerCamelCase_ = 1	, ):
						"""simple docstring"""

						pass




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     ScoreSdeVeSchedulerState.create()
						return self.set_sigmas(
						    lowerCamelCase_	, self.config.num_train_timesteps	, self.config.sigma_min	, self.config.sigma_max	, self.config.sampling_eps	, )




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = ()	, lowerCamelCase_ = None	):
						"""simple docstring"""

						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

						a        =     jnp.linspace(1	, lowerCamelCase_	, lowerCamelCase_	)
						return state.replace(timesteps=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, ):
						"""simple docstring"""

						a        =     sigma_min if sigma_min is not None else self.config.sigma_min
						a        =     sigma_max if sigma_max is not None else self.config.sigma_max
						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if state.timesteps is None:
											a        =     self.set_timesteps(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)

						a        =     jnp.exp(jnp.linspace(jnp.log(lowerCamelCase_	)	, jnp.log(lowerCamelCase_	)	, lowerCamelCase_	)	)
						a        =     jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]	)

						return state.replace(discrete_sigmas=lowerCamelCase_	, sigmas=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						return jnp.where(timesteps == 0	, jnp.zeros_like(lowerCamelCase_	)	, state.discrete_sigmas[timesteps - 1]	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						if state.timesteps is None:
											raise ValueError(
											    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"	)

						a        =     timestep * jnp.ones(
						    sample.shape[0]	, )
						a        =     (timestep * (len(state.timesteps	) - 1)).long()

						a        =     state.discrete_sigmas[timesteps]
						a        =     self.get_adjacent_sigma(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)
						a        =     jnp.zeros_like(lowerCamelCase_	)
						a        =     (sigma**2 - adjacent_sigma**2) ** 0.5

						# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
						# also equation 47 shows the analog from SDE models to ancestral sampling methods
						a        =     diffusion.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)
						a        =     sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						a        =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

						if not return_dict:
											return (prev_sample, prev_sample_mean, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, prev_sample_mean=lowerCamelCase_	, state=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						if state.timesteps is None:
											raise ValueError(
											    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"	)

						# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
						# sample noise for correction
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)

						# compute step size from the model_output, the noise, and the snr
						a        =     jnp.linalg.norm(lowerCamelCase_	)
						a        =     jnp.linalg.norm(lowerCamelCase_	)
						a        =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
						a        =     step_size * jnp.ones(sample.shape[0]	)

						# compute corrected sample: model_output term and noise term
						a        =     step_size.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     sample + step_size * model_output
						a        =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
											return (prev_sample, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, state=lowerCamelCase_	)




	def __len__(self	):
						"""simple docstring"""

						return self.config.num_train_timesteps
