




'''simple docstring'''

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					Dict    =  (KDPMaAncestralDiscreteScheduler,)
						UpperCAmelCase_		:					Any    =  10






						def       __lowerCAmelCase(   self						:						Optional[int]					,   **__UpperCamelCase						:						Tuple       ):



									'''simple docstring'''






									snake_case__          =						{
									    """num_train_timesteps""": 1_1_0_0,
									    """beta_start""": 0.00_01,
									    """beta_end""": 0.02,
									    """beta_schedule""": """linear""",
									}

									config.update(**__UpperCamelCase       )
									return config






						def       __lowerCAmelCase(   self						:						List[str]       ):



									'''simple docstring'''






									for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,   [0.00_02, 0.0_02, 0.02]       ):
												self.check_over_configs(beta_start=__UpperCamelCase					,   beta_end=__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									if torch_device == "mps":
												return
									snake_case__          =						self.scheduler_classes[0]
									snake_case__          =						self.get_scheduler_config()
									snake_case__          =						scheduler_class(**__UpperCamelCase       )

									scheduler.set_timesteps(self.num_inference_steps       )

									snake_case__          =						torch.manual_seed(0       )

									snake_case__          =						self.dummy_model()
									snake_case__          =						self.dummy_sample_deter * scheduler.init_noise_sigma
									snake_case__          =						sample.to(__UpperCamelCase       )

									for i, t in enumerate(scheduler.timesteps       ):
												snake_case__          =						scheduler.scale_model_input(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						model(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						scheduler.step(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   generator=__UpperCamelCase       )
												snake_case__          =						output.prev_sample

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

									assert abs(result_sum.item() - 1_38_49.38_77       ) < 1E-2
									assert abs(result_mean.item() - 18.03_31       ) < 5E-3






						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									if torch_device == "mps":
												return
									snake_case__          =						self.scheduler_classes[0]
									snake_case__          =						self.get_scheduler_config(prediction_type="""v_prediction"""       )
									snake_case__          =						scheduler_class(**__UpperCamelCase       )

									scheduler.set_timesteps(self.num_inference_steps       )

									snake_case__          =						self.dummy_model()
									snake_case__          =						self.dummy_sample_deter * scheduler.init_noise_sigma
									snake_case__          =						sample.to(__UpperCamelCase       )

									snake_case__          =						torch.manual_seed(0       )

									for i, t in enumerate(scheduler.timesteps       ):
												snake_case__          =						scheduler.scale_model_input(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						model(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						scheduler.step(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   generator=__UpperCamelCase       )
												snake_case__          =						output.prev_sample

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

									assert abs(result_sum.item() - 3_28.99_70       ) < 1E-2
									assert abs(result_mean.item() - 0.42_84       ) < 1E-3







						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






									if torch_device == "mps":
												return
									snake_case__          =						self.scheduler_classes[0]
									snake_case__          =						self.get_scheduler_config()
									snake_case__          =						scheduler_class(**__UpperCamelCase       )

									scheduler.set_timesteps(self.num_inference_steps					,   device=__UpperCamelCase       )
									snake_case__          =						torch.manual_seed(0       )

									snake_case__          =						self.dummy_model()
									snake_case__          =						self.dummy_sample_deter.to(__UpperCamelCase       ) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
												snake_case__          =						scheduler.scale_model_input(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						model(__UpperCamelCase					,   __UpperCamelCase       )

												snake_case__          =						scheduler.step(__UpperCamelCase					,   __UpperCamelCase					,   __UpperCamelCase					,   generator=__UpperCamelCase       )
												snake_case__          =						output.prev_sample

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

									assert abs(result_sum.item() - 1_38_49.38_18       ) < 1E-1
									assert abs(result_mean.item() - 18.03_31       ) < 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

a__         =     [(1,)]




def    snake_case__     (  )			->       Tuple:





			'''simple docstring'''






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




def    snake_case__     (  )			->       Dict:





			'''simple docstring'''






			snake_case__          =						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 __magic_name__(						unittest.TestCase      ):






						@slow
						@require_torch_gpu
						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									check_slow_torch_cuda()






						@require_torch_gpu
						@slow
						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									check_slow_torch_cuda()







						@parameterized.expand(__UpperCamelCase       )
						@slow
						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						int=None       ):



									'''simple docstring'''






									check_slow()







@slow
@require_torch_gpu
def    snake_case__     (  )			->       Tuple:





			'''simple docstring'''






			check_slow_torch_cuda()




@require_torch_gpu
@slow
def    snake_case__     (  )			->       List[Any]:





			'''simple docstring'''






			check_slow_torch_cuda()




@slow
@pytest.mark.parametrize("""param"""       ,      [1]	)
def    snake_case__     (  a	)			->       Union[str, Any]:





			'''simple docstring'''






			check_slow()




@pytest.mark.parametrize("""param"""       ,      [1]	)
@slow
def    snake_case__     (  a	)			->       Dict:





			'''simple docstring'''






			check_slow()