

"""simple docstring"""
import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class        _UpperCamelCase	(						_a	):


  """simple docstring"""





  snake_case_        =    (EulerAncestralDiscreteScheduler,)
  snake_case_        =    1_0



  def 							_UpperCAmelCase      ( self					: List[str]       ,       **snake_case					: Optional[Any]      )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Any        =     {
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.0001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**snake_case      )
         return config



  def 							_UpperCAmelCase      ( self					: Any      )	->			Union[str, Any]:


         '''simple docstring'''





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



  def 							_UpperCAmelCase      ( self					: List[Any]      )	->			int:


         '''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=snake_case       ,       beta_end=snake_case      )



  def 							_UpperCAmelCase      ( self					: Tuple      )	->			Optional[int]:


         '''simple docstring'''





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



  def 							_UpperCAmelCase      ( self					: str      )	->			Dict:


         '''simple docstring'''





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



  def 							_UpperCAmelCase      ( self					: Tuple      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__			:		List[Any]        =     self.scheduler_classes[0]
         __magic_name__			:		Any        =     self.get_scheduler_config()
         __magic_name__			:		Optional[int]        =     scheduler_class(**snake_case      )

         scheduler.set_timesteps(self.num_inference_steps      )

         __magic_name__			:		Any        =     torch.manual_seed(0      )

         __magic_name__			:		List[Any]        =     self.dummy_model()
         __magic_name__			:		Any        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __magic_name__			:		Optional[int]        =     sample.to(snake_case      )

         for i, t in enumerate(scheduler.timesteps      ):
                __magic_name__			:		Optional[Any]        =     scheduler.scale_model_input(snake_case       ,       snake_case      )

                __magic_name__			:		str        =     model(snake_case       ,       snake_case      )

                __magic_name__			:		Optional[Any]        =     scheduler.step(snake_case       ,       snake_case       ,       snake_case       ,       generator=snake_case      )
                __magic_name__			:		Optional[Any]        =     output.prev_sample

         __magic_name__			:		int        =     torch.sum(torch.abs(snake_case      )      )
         __magic_name__			:		List[Any]        =     torch.mean(torch.abs(snake_case      )      )

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



  def 							_UpperCAmelCase      ( self					: Optional[Any]      )	->			int:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     self.scheduler_classes[0]
         __magic_name__			:		Dict        =     self.get_scheduler_config(prediction_type='''v_prediction'''      )
         __magic_name__			:		Union[str, Any]        =     scheduler_class(**snake_case      )

         scheduler.set_timesteps(self.num_inference_steps      )

         __magic_name__			:		str        =     torch.manual_seed(0      )

         __magic_name__			:		int        =     self.dummy_model()
         __magic_name__			:		str        =     self.dummy_sample_deter * scheduler.init_noise_sigma
         __magic_name__			:		Dict        =     sample.to(snake_case      )

         for i, t in enumerate(scheduler.timesteps      ):
                __magic_name__			:		int        =     scheduler.scale_model_input(snake_case       ,       snake_case      )

                __magic_name__			:		List[Any]        =     model(snake_case       ,       snake_case      )

                __magic_name__			:		int        =     scheduler.step(snake_case       ,       snake_case       ,       snake_case       ,       generator=snake_case      )
                __magic_name__			:		Dict        =     output.prev_sample

         __magic_name__			:		str        =     torch.sum(torch.abs(snake_case      )      )
         __magic_name__			:		Tuple        =     torch.mean(torch.abs(snake_case      )      )

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




  def 							_UpperCAmelCase      ( self					: Tuple      )	->			List[str]:


         '''simple docstring'''





         __magic_name__			:		List[Any]        =     self.scheduler_classes[0]
         __magic_name__			:		Any        =     self.get_scheduler_config()
         __magic_name__			:		Dict        =     scheduler_class(**snake_case      )

         scheduler.set_timesteps(self.num_inference_steps       ,       device=snake_case      )
         __magic_name__			:		Any        =     torch.manual_seed(0      )

         __magic_name__			:		str        =     self.dummy_model()
         __magic_name__			:		List[Any]        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __magic_name__			:		Any        =     sample.to(snake_case      )

         for t in scheduler.timesteps:
                __magic_name__			:		str        =     scheduler.scale_model_input(snake_case       ,       snake_case      )

                __magic_name__			:		str        =     model(snake_case       ,       snake_case      )

                __magic_name__			:		List[Any]        =     scheduler.step(snake_case       ,       snake_case       ,       snake_case       ,       generator=snake_case      )
                __magic_name__			:		Union[str, Any]        =     output.prev_sample

         __magic_name__			:		Any        =     torch.sum(torch.abs(snake_case      )      )
         __magic_name__			:		str        =     torch.mean(torch.abs(snake_case      )      )

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




'''simple docstring'''



import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

__UpperCAmelCase     :Tuple												=  [(1,)]




def       _a      (		):





     '''simple docstring'''






     __UpperCAmelCase      :					Any    				=     bool(os.getenv('''RUN_SLOW'''     ,   0    )    )
     if run_slow:
          assert True
     else:
          assert False, "should have been skipped"




def       _a      (		):





     '''simple docstring'''






     __UpperCAmelCase      :					str    				=     bool(os.getenv('''RUN_SLOW'''     ,   0    )    )
     if run_slow and torch_device == "cuda":
          assert True
     else:
          assert False, "should have been skipped"



@require_torch
class       a							(						unittest.TestCase			):


     """simple docstring"""






     @slow
     @require_torch_gpu
     def 					lowerCamelCase__					(							self	:						Tuple     )		->				str:
          check_slow_torch_cuda()

     @require_torch_gpu
     @slow
     def 					lowerCamelCase__					(							self	:						Any     )		->				List[Any]:
          check_slow_torch_cuda()






     @parameterized.expand(snake_case     )
     @slow
     def 					lowerCamelCase__					(							self	:						Union[str, Any]				,					snake_case	:						int=None     )		->				Union[str, Any]:
          check_slow()




@slow
@require_torch_gpu
def       _a      (		):





     '''simple docstring'''






     check_slow_torch_cuda()




@require_torch_gpu
@slow
def       _a      (		):





     '''simple docstring'''






     check_slow_torch_cuda()




@slow
@pytest.mark.parametrize('''param'''     ,   [1]    )
def       _a      (		_lowercase   :      Union[str, Any]    ):





     '''simple docstring'''






     check_slow()




@pytest.mark.parametrize('''param'''     ,   [1]    )
@slow
def       _a      (		_lowercase   :      Dict    ):





     '''simple docstring'''






     check_slow()