


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 					snake_case__:







						'''simple docstring'''




						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     lowercase_			(					cls ,    __lowercase ,    __lowercase ,    __lowercase ,    __lowercase				)       ->     int:
										return cls(common=__UpperCamelCase ,    init_noise_sigma=__UpperCamelCase ,    timesteps=__UpperCamelCase ,    sigmas=__UpperCamelCase				)







@dataclass
class 					snake_case__(				__SCREAMING_SNAKE_CASE				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	LMSDiscreteSchedulerState







class 					snake_case__(				__SCREAMING_SNAKE_CASE,    __SCREAMING_SNAKE_CASE				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	Any       =				[e.name for e in FlaxKarrasDiffusionSchedulers]

						SCREAMING_SNAKE_CASE__   :	jnp.dtype




						@property
						def     lowercase_			(					self				)       ->     Optional[int]:
										return True




						@register_to_config
						def __init__(					self ,    __lowercase = 1_0_0_0 ,    __lowercase = 0.00_01 ,    __lowercase = 0.02 ,    __lowercase = "linear" ,    __lowercase = None ,    __lowercase = "epsilon" ,    __lowercase = jnp.floataa ,    )       ->     Dict:
										lowerCAmelCase_			:   Optional[int]     					=					dtype




						def     lowercase_			(					self ,    __lowercase = None				)       ->     Dict:


										if common is None:
														lowerCAmelCase_			:   Union[str, Any]     					=					CommonSchedulerState.create(self				)

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

										# standard deviation of the initial noise distribution
										lowerCAmelCase_			:   Tuple     					=					sigmas.max()

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




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase				)       ->     Tuple:
										((lowerCAmelCase_					)					,						)			:   List[str]     					=					jnp.where(state.timesteps == timestep ,    size=1				)
										lowerCAmelCase_			:   str     					=					step_index[0]

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




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase ,    __lowercase				)       ->     Dict:


										def lms_derivative(__lowercase				):
														lowerCAmelCase_			:   int     					=					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

										lowerCAmelCase_			:   int     					=					integrate.quad(__UpperCamelCase ,    state.sigmas[t] ,    state.sigmas[t + 1] ,    epsrel=1e-4				)[0]

										return integrated_coeff




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase = ()				)       ->     int:
										lowerCAmelCase_			:   List[str]     					=					jnp.linspace(self.config.num_train_timesteps - 1 ,    0 ,    __UpperCamelCase ,    dtype=self.dtype				)

										lowerCAmelCase_			:   Optional[Any]     					=					jnp.floor(__UpperCamelCase				).astype(jnp.intaa				)
										lowerCAmelCase_			:   Optional[Any]     					=					jnp.ceil(__UpperCamelCase				).astype(jnp.intaa				)

										lowerCAmelCase_			:   Optional[int]     					=					jnp.mod(__UpperCamelCase ,    1.0				)

										lowerCAmelCase_			:   Optional[Any]     					=					((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
										lowerCAmelCase_			:   List[str]     					=					(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_			:   str     					=					jnp.zeros((0,) + shape ,    dtype=self.dtype				)

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




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase ,    __lowercase ,    __lowercase = 4 ,    __lowercase = True ,    )       ->     List[str]:


										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_			:   Dict     					=					state.sigmas[timestep]

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

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

										# 4. Compute previous sample based on the derivatives path
										lowerCAmelCase_			:   Tuple     					=					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     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase ,    __lowercase ,    )       ->     List[Any]:
										lowerCAmelCase_			:   Optional[Any]     					=					state.sigmas[timesteps].flatten()
										lowerCAmelCase_			:   List[str]     					=					broadcast_to_shape_from_left(__UpperCamelCase ,    noise.shape				)

										lowerCAmelCase_			:   Any     					=					original_samples + noise * sigma

										return noisy_samples




						def __len__(					self				)       ->     List[str]:
										return self.config.num_train_timesteps

import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 		lowercase_							(							unittest.TestCase					):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 A__    :					int    =     (ScoreSdeVeScheduler,)
 A__    :					Union[str, Any]    =     ()

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							4
        UpperCamelCase_											=							3
        UpperCamelCase_											=							8
        UpperCamelCase_											=							8

        UpperCamelCase_											=							torch.rand((batch_size, num_channels, height, width)  )

        return sample

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							4
        UpperCamelCase_											=							3
        UpperCamelCase_											=							8
        UpperCamelCase_											=							8

        UpperCamelCase_											=							batch_size * num_channels * height * width
        UpperCamelCase_											=							torch.arange(__UpperCamelCase  )
        UpperCamelCase_											=							sample.reshape(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  )
        UpperCamelCase_											=							sample / num_elems
        UpperCamelCase_											=							sample.permute(3  ,						0  ,						1  ,						2  )

        return sample

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        def model(__UpperCamelCase  ,						__UpperCamelCase  ,						*__UpperCamelCase  ):
               return sample * t / (t + 1)

        return model

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							{
            """num_train_timesteps""": 2_0_0_0,
            """snr""": 0.15,
            """sigma_min""": 0.01,
            """sigma_max""": 1_3_4_8,
            """sampling_eps""": 1e-5,
        }

        config.update(**__UpperCamelCase  )
        return config

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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

               UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_correct(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        kwargs.update(__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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

               UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_correct(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for timesteps in [1_0, 1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]  ,						[1, 1_0_0, 1_0_0_0]  ):
               self.check_over_configs(sigma_min=__UpperCamelCase  ,						sigma_max=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							self.scheduler_classes[0]
        UpperCamelCase_											=							self.get_scheduler_config()
        UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

        UpperCamelCase_											=							3

        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter

        scheduler.set_sigmas(__UpperCamelCase  )
        scheduler.set_timesteps(__UpperCamelCase  )
        UpperCamelCase_											=							torch.manual_seed(0  )

        for i, t in enumerate(scheduler.timesteps  ):
               UpperCamelCase_											=							scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps  ):
                      with torch.no_grad():
                             UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

               with torch.no_grad():
                      UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=__UpperCamelCase  ,						**__UpperCamelCase  )
               UpperCamelCase_							,					UpperCamelCase_											=							output.prev_sample, output.prev_sample_mean

        UpperCamelCase_											=							torch.sum(torch.abs(__UpperCamelCase  )  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert np.isclose(result_sum.item()  ,						14_372_758_528.0  )
        assert np.isclose(result_mean.item()  ,						18_714_530.0  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               if num_inference_steps is not None and hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      scheduler.set_timesteps(__UpperCamelCase  )
               elif num_inference_steps is not None and not hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      UpperCamelCase_											=							num_inference_steps

               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						0  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						1  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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



