



# 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 	__snake_case   :
		SCREAMING_SNAKE_CASE__								=	42

		# setable values
		SCREAMING_SNAKE_CASE__								=	42
		SCREAMING_SNAKE_CASE__								=	42
		SCREAMING_SNAKE_CASE__								=	None
		@classmethod
		def 						SCREAMING_SNAKE_CASE_					(							cls    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						return cls(common=SCREAMING_SNAKE_CASE__    ,init_noise_sigma=SCREAMING_SNAKE_CASE__    ,timesteps=SCREAMING_SNAKE_CASE__						)



@dataclass
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	42



class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	[e.name for e in FlaxKarrasDiffusionSchedulers]

		SCREAMING_SNAKE_CASE__								=	42
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return True
		@register_to_config
		def __init__(							self    ,a_ = 1000    ,a_ = 0.0001    ,a_ = 0.02    ,a_ = "linear"    ,a_ = None    ,a_ = "fixed_small"    ,a_ = True    ,a_ = "epsilon"    ,a_ = jnp.floataa    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  dtype
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_ = None						):







						"""simple docstring"""



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

						# standard deviation of the initial noise distribution
						lowerCAmelCase__		  =  jnp.array(1.0    ,dtype=self.dtype						)

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

						return DDPMSchedulerState.create(
						    common=SCREAMING_SNAKE_CASE__    ,init_noise_sigma=SCREAMING_SNAKE_CASE__    ,timesteps=SCREAMING_SNAKE_CASE__    ,)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_ = None						):







						"""simple docstring"""
						return sample
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_ = ()						):







						"""simple docstring"""
						lowerCAmelCase__		  =  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__		  =  (jnp.arange(0    ,SCREAMING_SNAKE_CASE__						) * step_ratio).round()[::-1]

						return state.replace(
						    num_inference_steps=SCREAMING_SNAKE_CASE__    ,timesteps=SCREAMING_SNAKE_CASE__    ,)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_=None    ,a_=None						):







						"""simple docstring"""
						lowerCAmelCase__		  =  state.common.alphas_cumprod[t]
						lowerCAmelCase__		  =  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__		  =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

						if variance_type is None:
										lowerCAmelCase__		  =  self.config.variance_type

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

						return variance
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,a_ = None    ,a_ = True    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  timestep

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

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

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

						# 6. Add noise
						def random_variance():
										lowerCAmelCase__		  =  jax.random.split(SCREAMING_SNAKE_CASE__    ,num=1						)
										lowerCAmelCase__		  =  jax.random.normal(SCREAMING_SNAKE_CASE__    ,shape=model_output.shape    ,dtype=self.dtype						)
										return (self._get_variance(SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__    ,predicted_variance=SCREAMING_SNAKE_CASE__						) ** 0.5) * noise

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

						lowerCAmelCase__		  =  pred_prev_sample + variance

						if not return_dict:
										return (pred_prev_sample, state)

						return FlaxDDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__    ,state=SCREAMING_SNAKE_CASE__						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,):







						"""simple docstring"""
						return add_noise_common(state.common    ,SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,):







						"""simple docstring"""
						return get_velocity_common(state.common    ,SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__						)



		def __len__(							self						):







						"""simple docstring"""
						return self.config.num_train_timesteps


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Tuple       						=(DEISMultistepScheduler,)
 SCREAMING_SNAKE_CASE_				:					List[str]       						=(("num_inference_steps", 25),)

 def      __lowerCAmelCase  (						self			:			Tuple					, **SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'solver_order': 2,
      }

      config.update(**SCREAMING_SNAKE_CASE__   )
      return config

 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			Dict=0					, **SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )
      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.dummy_sample
      UpperCamelCase						    =					0.1 * sample
      UpperCamelCase						    =					[residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase						    =					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
           # copy over dummy past residuals
           UpperCamelCase						    =					dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__   )
                new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
                # copy over dummy past residuals
                UpperCamelCase						    =					dummy_past_residuals[: new_scheduler.config.solver_order]

           UpperCamelCase    ,		UpperCamelCase						    =					sample, sample
           for t in range(SCREAMING_SNAKE_CASE__					, time_step + scheduler.config.solver_order + 1   ):
                UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample
                UpperCamelCase						    =					new_scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample

                assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=0					, **SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )
      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.dummy_sample
      UpperCamelCase						    =					0.1 * sample
      UpperCamelCase						    =					[residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase						    =					self.get_scheduler_config()
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

           # copy over dummy past residuals (must be after setting timesteps)
           UpperCamelCase						    =					dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__   )
                # copy over dummy past residuals
                new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

                # copy over dummy past residual (must be after setting timesteps)
                UpperCamelCase						    =					dummy_past_residuals[: new_scheduler.config.solver_order]

           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample
           UpperCamelCase						    =					new_scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample

           assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			List[Any]=None					, **SCREAMING_SNAKE_CASE__			:			Union[str, Any]   ):






      """simple docstring"""



      if scheduler is None:
           UpperCamelCase						    =					self.scheduler_classes[0]
           UpperCamelCase						    =					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					self.scheduler_classes[0]
      UpperCamelCase						    =					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					10
      UpperCamelCase						    =					self.dummy_model()
      UpperCamelCase						    =					self.dummy_sample_deter
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

      for i, t in enumerate(scheduler.timesteps   ):
           UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ).prev_sample

      return sample

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )

      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase						    =					self.get_scheduler_config()
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

           UpperCamelCase						    =					self.dummy_sample
           UpperCamelCase						    =					0.1 * sample

           if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE__					, 'set_timesteps'   ):
                scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
           elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE__					, 'set_timesteps'   ):
                UpperCamelCase						    =					num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           UpperCamelCase						    =					[residual + 0.2, residual + 0.15, residual + 0.10]
           UpperCamelCase						    =					dummy_past_residuals[: scheduler.config.solver_order]

           UpperCamelCase						    =					scheduler.timesteps[5]
           UpperCamelCase						    =					scheduler.timesteps[6]

           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample
           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample

           self.assertEqual(output_a.shape					, sample.shape   )
           self.assertEqual(output_a.shape					, output_a.shape   )

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					DEISMultistepScheduler(**self.get_scheduler_config()   )
      UpperCamelCase						    =					self.full_loop(scheduler=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.23916   ) < 1e-3

      UpperCamelCase						    =					DPMSolverSinglestepScheduler.from_config(scheduler.config   )
      UpperCamelCase						    =					DPMSolverMultistepScheduler.from_config(scheduler.config   )
      UpperCamelCase						    =					UniPCMultistepScheduler.from_config(scheduler.config   )
      UpperCamelCase						    =					DEISMultistepScheduler.from_config(scheduler.config   )

      UpperCamelCase						    =					self.full_loop(scheduler=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.23916   ) < 1e-3

 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      for timesteps in [25, 50, 1_00, 9_99, 10_00]:
           self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__   )
      for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
                for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              thresholding=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, sample_max_value=SCREAMING_SNAKE_CASE__					, algorithm_type='deis'					, solver_order=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
                for order in [1, 2, 3]:
                     for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              solver_order=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, algorithm_type=SCREAMING_SNAKE_CASE__					, )
                          UpperCamelCase						    =					self.full_loop(
                              solver_order=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, algorithm_type=SCREAMING_SNAKE_CASE__					, )
                          assert not torch.isnan(SCREAMING_SNAKE_CASE__   ).any(), "Samples have nan numbers"

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__   )
      self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
           self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__					, time_step=0   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.full_loop()
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.23916   ) < 1e-3

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      UpperCamelCase						    =					self.full_loop(prediction_type='v_prediction'   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.091   ) < 1e-3






 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.scheduler_classes[0]
      UpperCamelCase						    =					self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE__					, dynamic_thresholding_ratio=0   )
      UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					10
      UpperCamelCase						    =					self.dummy_model()
      UpperCamelCase						    =					self.dummy_sample_deter.half()
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

      for i, t in enumerate(scheduler.timesteps   ):
           UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ).prev_sample

      assert sample.dtype == torch.floataa


