'''simple docstring'''

import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   List[Any]   				= None
      UpperCAmelCase_   :   Tuple   				= None




      @property
      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         return self.feat_extract_tester.prepare_feat_extract_dict()




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	self.feature_extraction_class(**self.feat_extract_dict				)
         self.assertTrue(hasattr(__a       ,'''feature_size'''				)				)
         self.assertTrue(hasattr(__a       ,'''sampling_rate'''				)				)
         self.assertTrue(hasattr(__a       ,'''padding_value'''				)				)




      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.feat_extract_tester.prepare_inputs_for_common()
         UpperCAmelCase__				:					Any 					=	self.feature_extraction_class(**self.feat_extract_dict				)
         UpperCAmelCase__				:					Optional[Any] 					=	feat_extract.model_input_names[0]

         UpperCAmelCase__				:					List[str] 					=	BatchFeature({input_name: speech_inputs}				)

         self.assertTrue(all(len(__a				) == len(__a				) for x, y in zip(__a       ,processed_features[input_name]				)				)				)

         UpperCAmelCase__				:					int 					=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__a				)
         UpperCAmelCase__				:					int 					=	BatchFeature({input_name: speech_inputs}       ,tensor_type='''np'''				)

         UpperCAmelCase__				:					Optional[int] 					=	processed_features[input_name]

         if len(batch_features_input.shape				) < 3:
            UpperCAmelCase__				:					Tuple 					=	batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)




      @require_torch
      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__a				)
         UpperCAmelCase__				:					List[Any] 					=	self.feature_extraction_class(**self.feat_extract_dict				)
         UpperCAmelCase__				:					Any 					=	feat_extract.model_input_names[0]

         UpperCAmelCase__				:					List[str] 					=	BatchFeature({input_name: speech_inputs}       ,tensor_type='''pt'''				)

         UpperCAmelCase__				:					Optional[Any] 					=	processed_features[input_name]

         if len(batch_features_input.shape				) < 3:
            UpperCAmelCase__				:					List[Any] 					=	batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)




      @require_tf
      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__a				)
         UpperCAmelCase__				:					str 					=	self.feature_extraction_class(**self.feat_extract_dict				)
         UpperCAmelCase__				:					Optional[int] 					=	feat_extract.model_input_names[0]

         UpperCAmelCase__				:					List[Any] 					=	BatchFeature({input_name: speech_inputs}       ,tensor_type='''tf'''				)

         UpperCAmelCase__				:					Optional[int] 					=	processed_features[input_name]

         if len(batch_features_input.shape				) < 3:
            UpperCAmelCase__				:					Tuple 					=	batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_=False				)      ->      Union[str, Any]:



         '''simple docstring'''



         def _inputs_have_equal_length(lowerCamelCase_				):
            UpperCAmelCase__				:					Optional[int] 					=	len(input[0]				)
            for input_slice in input[1:]:
               if len(__a				) != length:
                  return False
            return True

         def _inputs_are_equal(lowerCamelCase_       ,lowerCamelCase_				):
            if len(__a				) != len(__a				):
               return False

            for input_slice_a, input_slice_a in zip(__a       ,__a				):
               if not np.allclose(np.asarray(__a				)       ,np.asarray(__a				)       ,atol=1e-3				):
                  return False
            return True

         UpperCAmelCase__				:					List[str] 					=	self.feature_extraction_class(**self.feat_extract_dict				)
         UpperCAmelCase__				:					List[str] 					=	self.feat_extract_tester.prepare_inputs_for_common(numpify=__a				)
         UpperCAmelCase__				:					Dict 					=	feat_extract.model_input_names[0]

         UpperCAmelCase__				:					Dict 					=	BatchFeature({input_name: speech_inputs}				)

         UpperCAmelCase__				:					List[Any] 					=	self.feat_extract_tester.seq_length_diff
         UpperCAmelCase__				:					Tuple 					=	self.feat_extract_tester.max_seq_length + pad_diff
         UpperCAmelCase__				:					List[str] 					=	self.feat_extract_tester.min_seq_length
         UpperCAmelCase__				:					Dict 					=	self.feat_extract_tester.batch_size
         UpperCAmelCase__				:					List[Any] 					=	self.feat_extract_tester.feature_size

         # test padding for List[int] + numpy
         UpperCAmelCase__				:					Tuple 					=	feat_extract.pad(__a       ,padding=__a				)
         UpperCAmelCase__				:					List[Any] 					=	input_a[input_name]

         UpperCAmelCase__				:					Dict 					=	feat_extract.pad(__a       ,padding='''longest'''				)
         UpperCAmelCase__				:					List[str] 					=	input_a[input_name]

         UpperCAmelCase__				:					str 					=	feat_extract.pad(__a       ,padding='''max_length'''       ,max_length=len(speech_inputs[-1]				)				)
         UpperCAmelCase__				:					Optional[Any] 					=	input_a[input_name]

         UpperCAmelCase__				:					Union[str, Any] 					=	feat_extract.pad(__a       ,padding='''longest'''       ,return_tensors='''np'''				)
         UpperCAmelCase__				:					List[str] 					=	input_a[input_name]

         # max_length parameter has to be provided when setting `padding="max_length"`
         with self.assertRaises(__a				):
            feat_extract.pad(__a       ,padding='''max_length'''				)[input_name]

         UpperCAmelCase__				:					List[str] 					=	feat_extract.pad(
             __a       ,padding='''max_length'''       ,max_length=__a       ,return_tensors='''np'''				)
         UpperCAmelCase__				:					Optional[int] 					=	input_a[input_name]

         self.assertFalse(_inputs_have_equal_length(__a				)				)
         self.assertTrue(_inputs_have_equal_length(__a				)				)
         self.assertTrue(_inputs_have_equal_length(__a				)				)
         self.assertTrue(_inputs_are_equal(__a       ,__a				)				)
         self.assertTrue(len(input_a[0]				) == pad_min_length				)
         self.assertTrue(len(input_a[1]				) == pad_min_length + pad_diff				)
         self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]				))				)
         self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)				)

         if feature_size > 1:
            self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size				)

         # test padding for `pad_to_multiple_of` for List[int] + numpy
         UpperCAmelCase__				:					List[Any] 					=	feat_extract.pad(__a       ,pad_to_multiple_of=10				)
         UpperCAmelCase__				:					List[str] 					=	input_a[input_name]

         UpperCAmelCase__				:					Optional[int] 					=	feat_extract.pad(__a       ,padding='''longest'''       ,pad_to_multiple_of=10				)
         UpperCAmelCase__				:					Tuple 					=	input_a[input_name]

         UpperCAmelCase__				:					Optional[Any] 					=	feat_extract.pad(
             __a       ,padding='''max_length'''       ,pad_to_multiple_of=10       ,max_length=__a				)
         UpperCAmelCase__				:					List[str] 					=	input_a[input_name]

         UpperCAmelCase__				:					int 					=	feat_extract.pad(
             __a       ,padding='''max_length'''       ,pad_to_multiple_of=10       ,max_length=__a       ,return_tensors='''np'''       ,)
         UpperCAmelCase__				:					Union[str, Any] 					=	input_a[input_name]

         self.assertTrue(all(len(__a				) % 10 == 0 for x in input_a				)				)
         self.assertTrue(_inputs_are_equal(__a       ,__a				)				)

         UpperCAmelCase__				:					Union[str, Any] 					=	pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
         self.assertTrue(all(len(__a				) == expected_mult_pad_length for x in input_a				)				)
         self.assertEqual(input_a.shape[:2]       ,(batch_size, expected_mult_pad_length)				)

         if feature_size > 1:
            self.assertTrue(input_a.shape[2] == feature_size				)

         # Check padding value is correct
         UpperCAmelCase__				:					Union[str, Any] 					=	(np.ones(self.feat_extract_tester.feature_size				) * feat_extract.padding_value).sum()
         self.assertTrue(
             abs(np.asarray(input_a[0]				)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)				)
             < 1e-3				)
         self.assertTrue(
             abs(
                 np.asarray(input_a[1]				)[pad_min_length + pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)				)
             < 1e-3				)
         self.assertTrue(
             abs(
                 np.asarray(input_a[2]				)[pad_min_length + 2 * pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)				)
             < 1e-3				)
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)				) < 1e-3				)
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)				)
             < 1e-3				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_=False				)      ->      List[Any]:



         '''simple docstring'''



         def _inputs_have_equal_length(lowerCamelCase_				):
            UpperCAmelCase__				:					Dict 					=	len(input[0]				)
            for input_slice in input[1:]:
               if len(__a				) != length:
                  return False
            return True

         def _inputs_are_equal(lowerCamelCase_       ,lowerCamelCase_				):
            if len(__a				) != len(__a				):
               return False

            for input_slice_a, input_slice_a in zip(__a       ,__a				):
               if not np.allclose(np.asarray(__a				)       ,np.asarray(__a				)       ,atol=1e-3				):
                  return False
            return True

         UpperCAmelCase__				:					Any 					=	self.feature_extraction_class(**self.feat_extract_dict				)
         UpperCAmelCase__				:					Union[str, Any] 					=	self.feat_extract_tester.prepare_inputs_for_common(numpify=__a				)
         UpperCAmelCase__				:					int 					=	feat_extract.model_input_names[0]

         UpperCAmelCase__				:					Optional[Any] 					=	BatchFeature({input_name: speech_inputs}				)

         # truncate to smallest
         UpperCAmelCase__				:					List[str] 					=	feat_extract.pad(
             __a       ,padding='''max_length'''       ,max_length=len(speech_inputs[0]				)       ,truncation=__a				)
         UpperCAmelCase__				:					List[Any] 					=	input_a[input_name]

         UpperCAmelCase__				:					str 					=	feat_extract.pad(__a       ,padding='''max_length'''       ,max_length=len(speech_inputs[0]				)				)
         UpperCAmelCase__				:					Tuple 					=	input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(__a				)				)
         self.assertFalse(_inputs_have_equal_length(__a				)				)

         # truncate to smallest with np
         UpperCAmelCase__				:					Dict 					=	feat_extract.pad(
             __a       ,padding='''max_length'''       ,max_length=len(speech_inputs[0]				)       ,return_tensors='''np'''       ,truncation=__a       ,)
         UpperCAmelCase__				:					Tuple 					=	input_a[input_name]

         UpperCAmelCase__				:					Union[str, Any] 					=	feat_extract.pad(
             __a       ,padding='''max_length'''       ,max_length=len(speech_inputs[0]				)       ,return_tensors='''np'''				)
         UpperCAmelCase__				:					Optional[Any] 					=	input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(__a				)				)
         self.assertTrue(input_a.shape[1] == len(speech_inputs[0]				)				)

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(__a				)				)

         # truncate to middle
         UpperCAmelCase__				:					int 					=	feat_extract.pad(
             __a       ,padding='''max_length'''       ,max_length=len(speech_inputs[1]				)       ,truncation=__a       ,return_tensors='''np'''       ,)
         UpperCAmelCase__				:					Dict 					=	input_a[input_name]

         UpperCAmelCase__				:					Optional[int] 					=	feat_extract.pad(
             __a       ,padding='''max_length'''       ,max_length=len(speech_inputs[1]				)       ,truncation=__a				)
         UpperCAmelCase__				:					str 					=	input_a[input_name]

         UpperCAmelCase__				:					List[str] 					=	feat_extract.pad(
             __a       ,padding='''max_length'''       ,max_length=len(speech_inputs[1]				)       ,return_tensors='''np'''				)
         UpperCAmelCase__				:					Union[str, Any] 					=	input_a[input_name]

         self.assertTrue(input_a.shape[1] == len(speech_inputs[1]				)				)
         self.assertTrue(_inputs_have_equal_length(__a				)				)
         self.assertTrue(_inputs_have_equal_length(__a				)				)
         self.assertTrue(_inputs_are_equal(__a       ,__a				)				)

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(__a				)				)
         self.assertTrue(len(input_a[-1]				) == len(speech_inputs[-1]				)				)

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(__a				):
            feat_extract.pad(__a       ,truncation=__a				)[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(__a				):
            feat_extract.pad(__a       ,padding='''longest'''       ,truncation=__a				)[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(__a				):
            feat_extract.pad(__a       ,padding='''longest'''       ,truncation=__a				)[input_name]

         # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
         with self.assertRaises(__a				):
            feat_extract.pad(__a       ,padding='''max_length'''       ,truncation=__a				)[input_name]

         # test truncation for `pad_to_multiple_of` for List[int] + numpy
         UpperCAmelCase__				:					str 					=	12
         UpperCAmelCase__				:					Any 					=	feat_extract.pad(
             __a       ,padding='''max_length'''       ,max_length=len(speech_inputs[0]				)       ,pad_to_multiple_of=__a       ,truncation=__a       ,)
         UpperCAmelCase__				:					Tuple 					=	input_a[input_name]

         UpperCAmelCase__				:					str 					=	feat_extract.pad(
             __a       ,padding='''max_length'''       ,max_length=len(speech_inputs[0]				)       ,pad_to_multiple_of=__a       ,)
         UpperCAmelCase__				:					Optional[Any] 					=	input_a[input_name]

         # retrieve expected_length as multiple of pad_to_multiple_of
         UpperCAmelCase__				:					str 					=	len(speech_inputs[0]				)
         if expected_length % pad_to_multiple_of != 0:
            UpperCAmelCase__				:					Any 					=	((len(speech_inputs[0]				) // pad_to_multiple_of) + 1) * pad_to_multiple_of

         self.assertTrue(len(input_a[0]				) == expected_length				)
         self.assertTrue(_inputs_have_equal_length(__a				)				)
         self.assertFalse(_inputs_have_equal_length(__a				)				)




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         self._check_padding(numpify=__a				)




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         self._check_padding(numpify=__a				)




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         self._check_truncation(numpify=__a				)




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         self._check_truncation(numpify=__a				)




      @require_torch
      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.feature_extraction_class(**self.feat_extract_dict				)
         UpperCAmelCase__				:					int 					=	self.feat_extract_tester.prepare_inputs_for_common()
         UpperCAmelCase__				:					Any 					=	feat_extract.model_input_names[0]

         UpperCAmelCase__				:					List[Any] 					=	BatchFeature({input_name: speech_inputs}				)

         UpperCAmelCase__				:					int 					=	feat_extract.pad(__a       ,padding='''longest'''       ,return_tensors='''np'''				)[input_name]
         UpperCAmelCase__				:					str 					=	feat_extract.pad(__a       ,padding='''longest'''       ,return_tensors='''pt'''				)[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_pt.numpy().astype(np.floataa				).sum()				) < 1e-2				)




      @require_tf
      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	self.feature_extraction_class(**self.feat_extract_dict				)
         UpperCAmelCase__				:					Dict 					=	self.feat_extract_tester.prepare_inputs_for_common()
         UpperCAmelCase__				:					Tuple 					=	feat_extract.model_input_names[0]

         UpperCAmelCase__				:					Union[str, Any] 					=	BatchFeature({input_name: speech_inputs}				)

         UpperCAmelCase__				:					Any 					=	feat_extract.pad(__a       ,padding='''longest'''       ,return_tensors='''np'''				)[input_name]
         UpperCAmelCase__				:					Union[str, Any] 					=	feat_extract.pad(__a       ,padding='''longest'''       ,return_tensors='''tf'''				)[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_tf.numpy().astype(np.floataa				).sum()				) < 1e-2				)




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	self.feat_extract_dict
         UpperCAmelCase__				:					str 					=	True
         UpperCAmelCase__				:					Union[str, Any] 					=	self.feature_extraction_class(**__a				)
         UpperCAmelCase__				:					Union[str, Any] 					=	self.feat_extract_tester.prepare_inputs_for_common()
         UpperCAmelCase__				:					Dict 					=	[len(__a				) for x in speech_inputs]
         UpperCAmelCase__				:					Optional[Any] 					=	feat_extract.model_input_names[0]

         UpperCAmelCase__				:					int 					=	BatchFeature({input_name: speech_inputs}				)

         UpperCAmelCase__				:					Optional[Any] 					=	feat_extract.pad(__a       ,padding='''longest'''       ,return_tensors='''np'''				)
         self.assertIn('''attention_mask'''       ,__a				)
         self.assertListEqual(list(processed.attention_mask.shape				)       ,list(processed[input_name].shape[:2]				)				)
         self.assertListEqual(processed.attention_mask.sum(-1				).tolist()       ,__a				)




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	self.feat_extract_dict
         UpperCAmelCase__				:					Union[str, Any] 					=	True
         UpperCAmelCase__				:					int 					=	self.feature_extraction_class(**__a				)
         UpperCAmelCase__				:					Union[str, Any] 					=	self.feat_extract_tester.prepare_inputs_for_common()
         UpperCAmelCase__				:					Optional[int] 					=	[len(__a				) for x in speech_inputs]
         UpperCAmelCase__				:					Union[str, Any] 					=	feat_extract.model_input_names[0]

         UpperCAmelCase__				:					Optional[Any] 					=	BatchFeature({input_name: speech_inputs}				)
         UpperCAmelCase__				:					List[str] 					=	min(__a				)

         UpperCAmelCase__				:					Optional[Any] 					=	feat_extract.pad(
             __a       ,padding='''max_length'''       ,max_length=__a       ,truncation=__a       ,return_tensors='''np'''				)
         self.assertIn('''attention_mask'''       ,__a				)
         self.assertListEqual(
             list(processed_pad.attention_mask.shape				)       ,[processed_pad[input_name].shape[0], max_length]				)
         self.assertListEqual(
             processed_pad.attention_mask[:, :max_length].sum(-1				).tolist()       ,[max_length for x in speech_inputs]				)






"""simple docstring"""





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = (DPMSolverSDEScheduler,)
		lowercase__         = 10



		def 		UpperCAmelCase				(   self							,						**__a)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''num_train_timesteps''': 11_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''noise_sampler_seed''': 0,
							}

							config.update(**__a)
							return config



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






							'''simple docstring'''
							for timesteps in [10, 50, 1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''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=__a							,						beta_end=__a)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							for schedule in ["linear", "scaled_linear"]:
												self.check_over_configs(beta_schedule=__a)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=__a)



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






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							scheduler.set_timesteps(self.num_inference_steps)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter * scheduler.init_noise_sigma
							_UpperCamelCase									=    sample.to(__a)

							for i, t in enumerate(scheduler.timesteps):
												_UpperCamelCase									=    scheduler.scale_model_input(__a							,						__a)

												_UpperCamelCase									=    model(__a							,						__a)

												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a)
												_UpperCamelCase									=    output.prev_sample

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

							if torch_device in ["mps"]:
												assert abs(result_sum.item() - 167.47_8210_4492_1875) < 1e-2
												assert abs(result_mean.item() - 0.2178_7059_6456_5277) < 1e-3
							elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 171.59_3521_1181_6406) < 1e-2
												assert abs(result_mean.item() - 0.2_2342_9068_9229_9652) < 1e-3
							else:
												assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
												assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config(prediction_type='''v_prediction''')
							_UpperCamelCase									=    scheduler_class(**__a)

							scheduler.set_timesteps(self.num_inference_steps)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter * scheduler.init_noise_sigma
							_UpperCamelCase									=    sample.to(__a)

							for i, t in enumerate(scheduler.timesteps):
												_UpperCamelCase									=    scheduler.scale_model_input(__a							,						__a)

												_UpperCamelCase									=    model(__a							,						__a)

												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a)
												_UpperCamelCase									=    output.prev_sample

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

							if torch_device in ["mps"]:
												assert abs(result_sum.item() - 124.77_1492_0043_9453) < 1e-2
												assert abs(result_mean.item() - 0.1_6226_2890_1481_6284) < 1e-3
							elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 128.1_6633_6059_5703) < 1e-2
												assert abs(result_mean.item() - 0.1_6688_3260_0116_7297) < 1e-3
							else:
												assert abs(result_sum.item() - 119.8_4875_4882_8125) < 1e-2
												assert abs(result_mean.item() - 0.1560_5306_6253_6621) < 1e-3



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							scheduler.set_timesteps(self.num_inference_steps							,						device=__a)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
												_UpperCamelCase									=    scheduler.scale_model_input(__a							,						__a)

												_UpperCamelCase									=    model(__a							,						__a)

												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a)
												_UpperCamelCase									=    output.prev_sample

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

							if torch_device in ["mps"]:
												assert abs(result_sum.item() - 167.46_9573_9746_0938) < 1e-2
												assert abs(result_mean.item() - 0.2_1805_9346_0798_2635) < 1e-3
							elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 171.59_3536_3769_5312) < 1e-2
												assert abs(result_mean.item() - 0.2_2342_9083_8241_5771) < 1e-3
							else:
												assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
												assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3




		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a							,						use_karras_sigmas=__a)

							scheduler.set_timesteps(self.num_inference_steps							,						device=__a)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma
							_UpperCamelCase									=    sample.to(__a)

							for t in scheduler.timesteps:
												_UpperCamelCase									=    scheduler.scale_model_input(__a							,						__a)

												_UpperCamelCase									=    model(__a							,						__a)

												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a)
												_UpperCamelCase									=    output.prev_sample

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

							if torch_device in ["mps"]:
												assert abs(result_sum.item() - 176.66_9741_3574_2188) < 1e-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
							elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 177.63_6535_6445_3125) < 1e-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
							else:
												assert abs(result_sum.item() - 170.3_1352_2338_8672) < 1e-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2






