



import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class        _A			(      unittest.TestCase       ):
       # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
       UpperCamelCase__    :       int		   =		(ScoreSdeVeScheduler,)
       UpperCamelCase__    :       List[str]		   =		()



       @property
       def 						_lowerCamelCase     (						self				:	str):


           '''simple docstring'''

           __a					=							4
           __a					=							3
           __a					=							8
           __a					=							8

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

           return sample



       @property
       def 						_lowerCamelCase     (						self				:	Optional[Any]):


           '''simple docstring'''

           __a					=							4
           __a					=							3
           __a					=							8
           __a					=							8

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

           return sample



       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

           def model(__SCREAMING_SNAKE_CASE				:	int	,						__SCREAMING_SNAKE_CASE				:	Any	,						*__SCREAMING_SNAKE_CASE				:	str):
               return sample * t / (t + 1)

           return model



       def 						_lowerCamelCase     (						self				:	List[str]	,						**__SCREAMING_SNAKE_CASE				:	Optional[Any]):


           '''simple docstring'''

           __a					=							{
               'num_train_timesteps': 2_000,
               'snr': 0.15,
               'sigma_min': 0.01,
               'sigma_max': 1_348,
               'sampling_eps': 1E-5,
           }

           config.update(**lowercase_)
           return config



       def 						_lowerCamelCase     (						self				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	List[str]=0	,						**__SCREAMING_SNAKE_CASE				:	Union[str, Any]):


           '''simple docstring'''

           __a					=							dict(self.forward_default_kwargs)

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

               __a					=							self.get_scheduler_config(**lowercase_)
               __a					=							scheduler_class(**lowercase_)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowercase_)
                   __a					=							scheduler_class.from_pretrained(lowercase_)

               __a					=							scheduler.step_pred(
                   lowercase_	,						lowercase_	,						lowercase_	,						generator=torch.manual_seed(0)	,						**lowercase_).prev_sample
               __a					=							new_scheduler.step_pred(
                   lowercase_	,						lowercase_	,						lowercase_	,						generator=torch.manual_seed(0)	,						**lowercase_).prev_sample

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

               __a					=							scheduler.step_correct(lowercase_	,						lowercase_	,						generator=torch.manual_seed(0)	,						**lowercase_).prev_sample
               __a					=							new_scheduler.step_correct(
                   lowercase_	,						lowercase_	,						generator=torch.manual_seed(0)	,						**lowercase_).prev_sample

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



       def 						_lowerCamelCase     (						self				:	Any	,						__SCREAMING_SNAKE_CASE				:	int=0	,						**__SCREAMING_SNAKE_CASE				:	List[Any]):


           '''simple docstring'''

           __a					=							dict(self.forward_default_kwargs)
           kwargs.update(lowercase_)

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

               __a					=							self.get_scheduler_config()
               __a					=							scheduler_class(**lowercase_)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowercase_)
                   __a					=							scheduler_class.from_pretrained(lowercase_)

               __a					=							scheduler.step_pred(
                   lowercase_	,						lowercase_	,						lowercase_	,						generator=torch.manual_seed(0)	,						**lowercase_).prev_sample
               __a					=							new_scheduler.step_pred(
                   lowercase_	,						lowercase_	,						lowercase_	,						generator=torch.manual_seed(0)	,						**lowercase_).prev_sample

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

               __a					=							scheduler.step_correct(lowercase_	,						lowercase_	,						generator=torch.manual_seed(0)	,						**lowercase_).prev_sample
               __a					=							new_scheduler.step_correct(
                   lowercase_	,						lowercase_	,						generator=torch.manual_seed(0)	,						**lowercase_).prev_sample

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



       def 						_lowerCamelCase     (						self				:	Dict):


           '''simple docstring'''

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



       def 						_lowerCamelCase     (						self				:	str):


           '''simple docstring'''

           for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]	,						[1, 100, 1_000]):
               self.check_over_configs(sigma_min=lowercase_	,						sigma_max=lowercase_)



       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

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



       def 						_lowerCamelCase     (						self				:	str):


           '''simple docstring'''

           __a					=							dict(self.forward_default_kwargs)

           __a					=							self.scheduler_classes[0]
           __a					=							self.get_scheduler_config()
           __a					=							scheduler_class(**lowercase_)

           __a					=							3

           __a					=							self.dummy_model()
           __a					=							self.dummy_sample_deter

           scheduler.set_sigmas(lowercase_)
           scheduler.set_timesteps(lowercase_)
           __a					=							torch.manual_seed(0)

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

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

               with torch.no_grad():
                   __a					=							model(lowercase_	,						lowercase_)

               __a					=							scheduler.step_pred(lowercase_	,						lowercase_	,						lowercase_	,						generator=lowercase_	,						**lowercase_)
               __a					=							output.prev_sample, output.prev_sample_mean

           __a					=							torch.sum(torch.abs(lowercase_))
           __a					=							torch.mean(torch.abs(lowercase_))

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





       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           __a					=							dict(self.forward_default_kwargs)

           __a					=							kwargs.pop('''num_inference_steps'''	,						lowercase_)

           for scheduler_class in self.scheduler_classes:
               __a					=							self.get_scheduler_config()
               __a					=							scheduler_class(**lowercase_)

               __a					=							self.dummy_sample
               __a					=							0.1 * sample

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

               __a					=							scheduler.step_pred(lowercase_	,						0	,						lowercase_	,						generator=torch.manual_seed(0)	,						**lowercase_).prev_sample
               __a					=							scheduler.step_pred(lowercase_	,						1	,						lowercase_	,						generator=torch.manual_seed(0)	,						**lowercase_).prev_sample

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













"""simple docstring"""


import torch
from torch import autograd






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		Optional[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float					,      lowercase_	:		bool )		->				List[str]:
   __A						:							str      =						inputs.numel()
   __A						:							str      =						int(0.005 * nb_elems ) + 1
   if sigmoid:
    __A						:							Dict      =						(torch.sigmoid(lowercase_ ) > threshold).type(inputs.type() )
   else:
    __A						:							Tuple      =						(inputs > threshold).type(inputs.type() )
   if mask.sum() < nb_min:
    # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
    __A						:							Any      =						inputs.flatten().kthvalue(max(nb_elems - nb_min					,      1 ) ).values
    __A						:							Union[str, Any]      =						(inputs > k_threshold).type(inputs.type() )
   return mask



  @staticmethod
  def     A_					(lowercase_	:		str					,      lowercase_	:		Any )		->				int:
   return gradOutput, None, None






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		List[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				Dict:
   __A						:							List[Any]      =						inputs.clone()
   __A     ,       __A						:							Optional[int]      =						inputs.flatten().sort(descending=lowercase_ )
   __A						:							Tuple      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							Optional[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Any      =						1
   return mask



  @staticmethod
  def     A_					(lowercase_	:		Tuple					,      lowercase_	:		Optional[Any] )		->				str:
   return gradOutput, None






class __a ( snake_case_					):



  @staticmethod
  def     A_					(lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				List[str]:
   __A						:							List[str]      =						inputs.clone()
   __A     ,       __A						:							List[Any]      =						inputs.abs().flatten().sort(descending=lowercase_ )
   __A						:							Dict      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							List[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Tuple      =						1
   return mask





