


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)
 __lowercase					:				Optional[int]  	=					(ScoreSdeVeScheduler,)
 __lowercase					:				int  	=					()
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Any         =  4
        lowercase__:  str         =  3
        lowercase__:  List[str]         =  8
        lowercase__:  Union[str, Any]         =  8

        lowercase__:  List[str]         =  torch.rand((batch_size, num_channels, height, width)					)

        return sample
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__:  Any         =  4
        lowercase__:  int         =  3
        lowercase__:  Optional[int]         =  8
        lowercase__:  Optional[Any]         =  8

        lowercase__:  Union[str, Any]         =  batch_size * num_channels * height * width
        lowercase__:  List[Any]         =  torch.arange(lowerCAmelCase__					)
        lowercase__:  Tuple         =  sample.reshape(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
        lowercase__:  Union[str, Any]         =  sample / num_elems
        lowercase__:  Union[str, Any]         =  sample.permute(3							,					0							,					1							,					2					)

        return sample
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        def model(lowerCAmelCase__							,					lowerCAmelCase__							,					*lowerCAmelCase__					):
               return sample * t / (t + 1)

        return model
 def 						SCREAMING_SNAKE_CASE__  (  self							,					**lowerCAmelCase__					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Dict         =  {
            'num_train_timesteps': 2_000,
            'snr': 0.1_5,
            'sigma_min': 0.0_1,
            'sigma_max': 1_348,
            'sampling_eps': 1E-5,
        }

        config.update(**lowerCAmelCase__					)
        return config
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__=0							,					**lowerCAmelCase__					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  dict(self.forward_default_kwargs					)

        for scheduler_class in self.scheduler_classes:
               lowercase__:  Any         =  self.dummy_sample
               lowercase__:  List[Any]         =  0.1 * sample

               lowercase__:  Tuple         =  self.get_scheduler_config(**lowerCAmelCase__					)
               lowercase__:  Optional[Any]         =  scheduler_class(**lowerCAmelCase__					)

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(lowerCAmelCase__					)
                      lowercase__:  List[str]         =  scheduler_class.from_pretrained(lowerCAmelCase__					)

               lowercase__:  Dict         =  scheduler.step_pred(
                   lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					generator=torch.manual_seed(0					)							,					**lowerCAmelCase__					).prev_sample
               lowercase__:  Any         =  new_scheduler.step_pred(
                   lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					generator=torch.manual_seed(0					)							,					**lowerCAmelCase__					).prev_sample

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

               lowercase__:  Optional[int]         =  scheduler.step_correct(lowerCAmelCase__							,					lowerCAmelCase__							,					generator=torch.manual_seed(0					)							,					**lowerCAmelCase__					).prev_sample
               lowercase__:  str         =  new_scheduler.step_correct(
                   lowerCAmelCase__							,					lowerCAmelCase__							,					generator=torch.manual_seed(0					)							,					**lowerCAmelCase__					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler correction are not identical"
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__=0							,					**lowerCAmelCase__					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  str         =  dict(self.forward_default_kwargs					)
        kwargs.update(lowerCAmelCase__					)

        for scheduler_class in self.scheduler_classes:
               lowercase__:  Tuple         =  self.dummy_sample
               lowercase__:  Optional[Any]         =  0.1 * sample

               lowercase__:  int         =  self.get_scheduler_config()
               lowercase__:  Any         =  scheduler_class(**lowerCAmelCase__					)

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(lowerCAmelCase__					)
                      lowercase__:  str         =  scheduler_class.from_pretrained(lowerCAmelCase__					)

               lowercase__:  str         =  scheduler.step_pred(
                   lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					generator=torch.manual_seed(0					)							,					**lowerCAmelCase__					).prev_sample
               lowercase__:  Any         =  new_scheduler.step_pred(
                   lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					generator=torch.manual_seed(0					)							,					**lowerCAmelCase__					).prev_sample

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

               lowercase__:  Dict         =  scheduler.step_correct(lowerCAmelCase__							,					lowerCAmelCase__							,					generator=torch.manual_seed(0					)							,					**lowerCAmelCase__					).prev_sample
               lowercase__:  Tuple         =  new_scheduler.step_correct(
                   lowerCAmelCase__							,					lowerCAmelCase__							,					generator=torch.manual_seed(0					)							,					**lowerCAmelCase__					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler correction are not identical"
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        for timesteps in [10, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]							,					[1, 100, 1_000]					):
               self.check_over_configs(sigma_min=lowerCAmelCase__							,					sigma_max=lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        for t in [0.1, 0.5, 0.7_5]:
               self.check_over_forward(time_step=lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Any:




        '''simple docstring'''
        lowercase__:  List[str]         =  dict(self.forward_default_kwargs					)

        lowercase__:  int         =  self.scheduler_classes[0]
        lowercase__:  Optional[Any]         =  self.get_scheduler_config()
        lowercase__:  str         =  scheduler_class(**lowerCAmelCase__					)

        lowercase__:  Union[str, Any]         =  3

        lowercase__:  Optional[Any]         =  self.dummy_model()
        lowercase__:  Optional[Any]         =  self.dummy_sample_deter

        scheduler.set_sigmas(lowerCAmelCase__					)
        scheduler.set_timesteps(lowerCAmelCase__					)
        lowercase__:  Optional[Any]         =  torch.manual_seed(0					)

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

               for _ in range(scheduler.config.correct_steps					):
                      with torch.no_grad():
                             lowercase__:  Union[str, Any]         =  model(lowerCAmelCase__							,					lowerCAmelCase__					)
                      lowercase__:  Dict         =  scheduler.step_correct(lowerCAmelCase__							,					lowerCAmelCase__							,					generator=lowerCAmelCase__							,					**lowerCAmelCase__					).prev_sample

               with torch.no_grad():
                      lowercase__:  Optional[int]         =  model(lowerCAmelCase__							,					lowerCAmelCase__					)

               lowercase__:  int         =  scheduler.step_pred(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					generator=lowerCAmelCase__							,					**lowerCAmelCase__					)
               lowercase__     ,		lowercase__:  List[str]         =  output.prev_sample, output.prev_sample_mean

        lowercase__:  Dict         =  torch.sum(torch.abs(lowerCAmelCase__					)					)
        lowercase__:  Union[str, Any]         =  torch.mean(torch.abs(lowerCAmelCase__					)					)

        assert np.isclose(result_sum.item()							,					1_4_3_7_2_7_5_8_5_2_8.0					)
        assert np.isclose(result_mean.item()							,					1_8_7_1_4_5_3_0.0					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  dict(self.forward_default_kwargs					)

        lowercase__:  List[Any]         =  kwargs.pop('num_inference_steps'							,					lowerCAmelCase__					)

        for scheduler_class in self.scheduler_classes:
               lowercase__:  str         =  self.get_scheduler_config()
               lowercase__:  str         =  scheduler_class(**lowerCAmelCase__					)

               lowercase__:  Optional[Any]         =  self.dummy_sample
               lowercase__:  Union[str, Any]         =  0.1 * sample

               if num_inference_steps is not None and hasattr(lowerCAmelCase__							,					'set_timesteps'					):
                      scheduler.set_timesteps(lowerCAmelCase__					)
               elif num_inference_steps is not None and not hasattr(lowerCAmelCase__							,					'set_timesteps'					):
                      lowercase__:  List[Any]         =  num_inference_steps

               lowercase__:  Tuple         =  scheduler.step_pred(lowerCAmelCase__							,					0							,					lowerCAmelCase__							,					generator=torch.manual_seed(0					)							,					**lowerCAmelCase__					).prev_sample
               lowercase__:  Optional[int]         =  scheduler.step_pred(lowerCAmelCase__							,					1							,					lowerCAmelCase__							,					generator=torch.manual_seed(0					)							,					**lowerCAmelCase__					).prev_sample

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






class     __a     :
 def __init__(  self					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  str         =  []
        lowercase__:  Any         =  0
        lowercase__:  int         =  0
 def __str__(  self					)   ->							Any:




        '''simple docstring'''
        lowercase__:  str         =  '<' + str(self.entries					)[1:-1] + '>'
        return printed
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__					)   ->							List[str]:




        '''simple docstring'''
        self.entries.append(lowerCAmelCase__					)
        lowercase__:  List[str]         =  self.length + 1
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__:  Any         =  self.length - 1
        lowercase__:  Optional[int]         =  self.entries[self.front]
        # self.front-=1
        # self.entries = self.entries[self.front:]
        lowercase__:  int         =  self.entries[1:]
        return dequeued
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__					)   ->							List[str]:




        '''simple docstring'''
        for _ in range(lowerCAmelCase__					):
               self.put(self.get()					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Union[str, Any]:




        '''simple docstring'''
        return self.entries[0]
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        return self.length


