



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Dict        =  (DDPMParallelScheduler,)
       def 					snake_case_       (			self,			**SCREAMING_SNAKE_CASE_       )     ->		Tuple:
          UpperCamelCase    :  Dict							=	{
              'num_train_timesteps': 1000,
              'beta_start': 0.00_01,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
              'variance_type': 'fixed_small',
              'clip_sample': True,
          }

          config.update(**SCREAMING_SNAKE_CASE_       )
          return config
       def 					snake_case_       (			self       )     ->		Tuple:
          for timesteps in [1, 5, 100, 1000]:
             self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1],			[0.0_02, 0.02, 0.2, 2]       ):
             self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_,			beta_end=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          for variance in ["fixed_small", "fixed_large", "other"]:
             self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Tuple:
          for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_       )
          for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                self.check_over_configs(
                    thresholding=SCREAMING_SNAKE_CASE_,			prediction_type=SCREAMING_SNAKE_CASE_,			sample_max_value=SCREAMING_SNAKE_CASE_,			)
       def 					snake_case_       (			self       )     ->		int:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          for t in [0, 500, 999]:
             self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  Any							=	self.scheduler_classes[0]
          UpperCamelCase    :  Tuple							=	self.get_scheduler_config()
          UpperCamelCase    :  str							=	scheduler_class(**SCREAMING_SNAKE_CASE_       )

          assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0_09_79       )       ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.02       )       ) < 1e-5
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  Union[str, Any]							=	self.scheduler_classes[0]
          UpperCamelCase    :  str							=	self.get_scheduler_config()
          UpperCamelCase    :  Optional[Any]							=	scheduler_class(**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Union[str, Any]							=	len(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	self.dummy_model()
          UpperCamelCase    :  List[str]							=	self.dummy_sample_deter
          UpperCamelCase    :  Dict							=	self.dummy_sample_deter + 0.1
          UpperCamelCase    :  Optional[int]							=	self.dummy_sample_deter - 0.1

          UpperCamelCase    :  int							=	samplea.shape[0]
          UpperCamelCase    :  Optional[int]							=	torch.stack([samplea, samplea, samplea],			dim=0       )
          UpperCamelCase    :  int							=	torch.arange(SCREAMING_SNAKE_CASE_       )[0:3, None].repeat(1,			SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  int							=	model(samples.flatten(0,			1       ),			timesteps.flatten(0,			1       )       )
          UpperCamelCase    :  Dict							=	scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE_,			timesteps.flatten(0,			1       ),			samples.flatten(0,			1       )       )

          UpperCamelCase    :  Tuple							=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_       )       )
          UpperCamelCase    :  List[str]							=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_       )       )

          assert abs(result_sum.item() - 11_53.18_33       ) < 1e-2
          assert abs(result_mean.item() - 0.50_05       ) < 1e-3
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Union[str, Any]							=	self.scheduler_classes[0]
          UpperCamelCase    :  Tuple							=	self.get_scheduler_config()
          UpperCamelCase    :  Dict							=	scheduler_class(**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Any							=	len(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	self.dummy_model()
          UpperCamelCase    :  Tuple							=	self.dummy_sample_deter
          UpperCamelCase    :  str							=	torch.manual_seed(0       )

          for t in reversed(range(SCREAMING_SNAKE_CASE_       )       ):
             # 1. predict noise residual
             UpperCamelCase    :  Dict							=	model(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

             # 2. predict previous mean of sample x_t-1
             UpperCamelCase    :  int							=	scheduler.step(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			generator=SCREAMING_SNAKE_CASE_       ).prev_sample

             UpperCamelCase    :  Optional[int]							=	pred_prev_sample

          UpperCamelCase    :  str							=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_       )       )
          UpperCamelCase    :  Optional[Any]							=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_       )       )

          assert abs(result_sum.item() - 2_58.96_06       ) < 1e-2
          assert abs(result_mean.item() - 0.33_72       ) < 1e-3
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  Optional[int]							=	self.scheduler_classes[0]
          UpperCamelCase    :  List[str]							=	self.get_scheduler_config(prediction_type='v_prediction'       )
          UpperCamelCase    :  str							=	scheduler_class(**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Union[str, Any]							=	len(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[Any]							=	self.dummy_model()
          UpperCamelCase    :  Dict							=	self.dummy_sample_deter
          UpperCamelCase    :  List[Any]							=	torch.manual_seed(0       )

          for t in reversed(range(SCREAMING_SNAKE_CASE_       )       ):
             # 1. predict noise residual
             UpperCamelCase    :  Dict							=	model(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

             # 2. predict previous mean of sample x_t-1
             UpperCamelCase    :  Optional[Any]							=	scheduler.step(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			generator=SCREAMING_SNAKE_CASE_       ).prev_sample

             UpperCamelCase    :  str							=	pred_prev_sample

          UpperCamelCase    :  Tuple							=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_       )       )
          UpperCamelCase    :  Optional[Any]							=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_       )       )

          assert abs(result_sum.item() - 2_02.02_96       ) < 1e-2
          assert abs(result_mean.item() - 0.26_31       ) < 1e-3
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  Dict							=	self.scheduler_classes[0]
          UpperCamelCase    :  Optional[int]							=	self.get_scheduler_config()
          UpperCamelCase    :  Union[str, Any]							=	scheduler_class(**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Optional[Any]							=	[100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Optional[Any]							=	scheduler.timesteps

          for i, timestep in enumerate(SCREAMING_SNAKE_CASE_       ):
             if i == len(SCREAMING_SNAKE_CASE_       ) - 1:
                UpperCamelCase    :  Tuple							=	-1
             else:
                UpperCamelCase    :  str							=	timesteps[i + 1]

             UpperCamelCase    :  Any							=	scheduler.previous_timestep(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Union[str, Any]							=	prev_t.item()

             self.assertEqual(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Tuple:
          UpperCamelCase    :  Union[str, Any]							=	self.scheduler_classes[0]
          UpperCamelCase    :  str							=	self.get_scheduler_config()
          UpperCamelCase    :  Optional[Any]							=	scheduler_class(**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Optional[Any]							=	[100, 87, 50, 51, 0]

          with self.assertRaises(SCREAMING_SNAKE_CASE_,			msg='`custom_timesteps` must be in descending order.'       ):
             scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  Any							=	self.scheduler_classes[0]
          UpperCamelCase    :  Union[str, Any]							=	self.get_scheduler_config()
          UpperCamelCase    :  List[Any]							=	scheduler_class(**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  int							=	[100, 87, 50, 1, 0]
          UpperCamelCase    :  Optional[Any]							=	len(SCREAMING_SNAKE_CASE_       )

          with self.assertRaises(SCREAMING_SNAKE_CASE_,			msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'       ):
             scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE_,			timesteps=SCREAMING_SNAKE_CASE_       )






       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Union[str, Any]							=	self.scheduler_classes[0]
          UpperCamelCase    :  Optional[Any]							=	self.get_scheduler_config()
          UpperCamelCase    :  int							=	scheduler_class(**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Union[str, Any]							=	[scheduler.config.num_train_timesteps]

          with self.assertRaises(
              SCREAMING_SNAKE_CASE_,			msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}',			):
             scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_       )








import pytest

from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def 		UpperCamelCase	(   snake_case__       :    str	)	->     Optional[int]:
   monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings'       ,     set()	)
@pytest.fixture
def 		UpperCamelCase	(   snake_case__       :    str	)	->     Any:



   class 				lowerCAmelCase_		:
          def __init__(			self,			SCREAMING_SNAKE_CASE_       )     ->		Optional[int]:
             UpperCamelCase    :  Tuple							=	metric_id






   class 				lowerCAmelCase_		:
          UpperCAmelCase__				:					Optional[int]        =  [MetricMock(a__							) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
          def 					snake_case_       (			self       )     ->		str:
             return self._metrics






   monkeypatch.setattr('datasets.inspect.huggingface_hub'       ,     HfhMock()	)


@pytest.mark.parametrize(
    'func, args'       ,     [(load_metric, ('metrics/mse',)), (list_metrics, ()), (inspect_metric, ('metrics/mse', 'tmp_path'))]	)
def 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    Any       ,     snake_case__       :    List[str]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   if "tmp_path" in args:
      UpperCamelCase    :  Dict							=	tuple(arg if arg != 'tmp_path' else tmp_path for arg in args	)
   with pytest.warns(snake_case__       ,     match='https://huggingface.co/docs/evaluate'	):
      func(*snake_case__	)



