
import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_FOR_PRETRAINING_MAPPING,
		    MobileBertForMaskedLM,
		    MobileBertForMultipleChoice,
		    MobileBertForNextSentencePrediction,
		    MobileBertForPreTraining,
		    MobileBertForQuestionAnswering,
		    MobileBertForSequenceClassification,
		    MobileBertForTokenClassification,
		    MobileBertModel,
		)





class    __A:

					def __init__(						self       :  Dict ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Optional[Any]=1_3 ,       __UpperCamelCase       :  List[str]=7 ,       __UpperCamelCase       :  Optional[Any]=True ,       __UpperCamelCase       :  str=True ,       __UpperCamelCase       :  Union[str, Any]=True ,       __UpperCamelCase       :  int=True ,       __UpperCamelCase       :  List[Any]=9_9 ,       __UpperCamelCase       :  List[str]=6_4 ,       __UpperCamelCase       :  Union[str, Any]=3_2 ,       __UpperCamelCase       :  Optional[Any]=5 ,       __UpperCamelCase       :  Dict=4 ,       __UpperCamelCase       :  List[str]=3_7 ,       __UpperCamelCase       :  Union[str, Any]="gelu" ,       __UpperCamelCase       :  List[str]=0.1 ,       __UpperCamelCase       :  Union[str, Any]=0.1 ,       __UpperCamelCase       :  Optional[Any]=5_1_2 ,       __UpperCamelCase       :  int=1_6 ,       __UpperCamelCase       :  Optional[int]=2 ,       __UpperCamelCase       :  int=0.02 ,       __UpperCamelCase       :  Any=3 ,       __UpperCamelCase       :  List[str]=4 ,       __UpperCamelCase       :  str=None ,       ):
												lowerCamelCase_					=       parent
												lowerCamelCase_					=       batch_size
												lowerCamelCase_					=       seq_length
												lowerCamelCase_					=       is_training
												lowerCamelCase_					=       use_input_mask
												lowerCamelCase_					=       use_token_type_ids
												lowerCamelCase_					=       use_labels
												lowerCamelCase_					=       vocab_size
												lowerCamelCase_					=       hidden_size
												lowerCamelCase_					=       embedding_size
												lowerCamelCase_					=       num_hidden_layers
												lowerCamelCase_					=       num_attention_heads
												lowerCamelCase_					=       intermediate_size
												lowerCamelCase_					=       hidden_act
												lowerCamelCase_					=       hidden_dropout_prob
												lowerCamelCase_					=       attention_probs_dropout_prob
												lowerCamelCase_					=       max_position_embeddings
												lowerCamelCase_					=       type_vocab_size
												lowerCamelCase_					=       type_sequence_label_size
												lowerCamelCase_					=       initializer_range
												lowerCamelCase_					=       num_labels
												lowerCamelCase_					=       num_choices
												lowerCamelCase_					=       scope

					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size )

												lowerCamelCase_					=       None
												if self.use_input_mask:
																			lowerCamelCase_					=       random_attention_mask([self.batch_size, self.seq_length] )

												lowerCamelCase_					=       None
												if self.use_token_type_ids:
																			lowerCamelCase_					=       ids_tensor([self.batch_size, self.seq_length] ,       self.type_vocab_size )

												lowerCamelCase_					=       None
												lowerCamelCase_					=       None
												lowerCamelCase_					=       None
												if self.use_labels:
																			lowerCamelCase_					=       ids_tensor([self.batch_size] ,       self.type_sequence_label_size )
																			lowerCamelCase_					=       ids_tensor([self.batch_size, self.seq_length] ,       self.num_labels )
																			lowerCamelCase_					=       ids_tensor([self.batch_size] ,       self.num_choices )

												lowerCamelCase_					=       self.get_config()

												return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

					def  lowercase__   (						self       :  List[str] ):
												return MobileBertConfig(
												    vocab_size=self.vocab_size ,       hidden_size=self.hidden_size ,       num_hidden_layers=self.num_hidden_layers ,       num_attention_heads=self.num_attention_heads ,       intermediate_size=self.intermediate_size ,       embedding_size=self.embedding_size ,       hidden_act=self.hidden_act ,       hidden_dropout_prob=self.hidden_dropout_prob ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob ,       max_position_embeddings=self.max_position_embeddings ,       type_vocab_size=self.type_vocab_size ,       is_decoder=__UpperCamelCase ,       initializer_range=self.initializer_range ,       )

					def  lowercase__   (						self       :  Optional[Any] ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Optional[int] ):
												lowerCamelCase_					=       MobileBertModel(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase )
												lowerCamelCase_					=       model(__UpperCamelCase ,       token_type_ids=__UpperCamelCase )
												lowerCamelCase_					=       model(__UpperCamelCase )

												self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size) )
												self.parent.assertEqual(result.pooler_output.shape ,       (self.batch_size, self.hidden_size) )

					def  lowercase__   (						self       :  Any ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  List[Any] ):
												lowerCamelCase_					=       MobileBertForMaskedLM(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       labels=__UpperCamelCase )
												self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.vocab_size) )

					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Optional[Any] ):
												lowerCamelCase_					=       MobileBertForNextSentencePrediction(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(
												    __UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       labels=__UpperCamelCase ,       )
												self.parent.assertEqual(result.logits.shape ,       (self.batch_size, 2) )

					def  lowercase__   (						self       :  str ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Union[str, Any] ):
												lowerCamelCase_					=       MobileBertForPreTraining(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(
												    __UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       labels=__UpperCamelCase ,       next_sentence_label=__UpperCamelCase ,       )
												self.parent.assertEqual(result.prediction_logits.shape ,       (self.batch_size, self.seq_length, self.vocab_size) )
												self.parent.assertEqual(result.seq_relationship_logits.shape ,       (self.batch_size, 2) )

					def  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Optional[int] ):
												lowerCamelCase_					=       MobileBertForQuestionAnswering(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(
												    __UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       start_positions=__UpperCamelCase ,       end_positions=__UpperCamelCase ,       )
												self.parent.assertEqual(result.start_logits.shape ,       (self.batch_size, self.seq_length) )
												self.parent.assertEqual(result.end_logits.shape ,       (self.batch_size, self.seq_length) )

					def  lowercase__   (						self       :  Union[str, Any] ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Optional[Any] ):
												lowerCamelCase_					=       self.num_labels
												lowerCamelCase_					=       MobileBertForSequenceClassification(__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       labels=__UpperCamelCase )
												self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels) )

					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Tuple ):
												lowerCamelCase_					=       self.num_labels
												lowerCamelCase_					=       MobileBertForTokenClassification(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       labels=__UpperCamelCase )
												self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.num_labels) )

					def  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  Dict ):
												lowerCamelCase_					=       self.num_choices
												lowerCamelCase_					=       MobileBertForMultipleChoice(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       input_ids.unsqueeze(1 ).expand(-1 ,       self.num_choices ,       -1 ).contiguous()
												lowerCamelCase_					=       token_type_ids.unsqueeze(1 ).expand(-1 ,       self.num_choices ,       -1 ).contiguous()
												lowerCamelCase_					=       input_mask.unsqueeze(1 ).expand(-1 ,       self.num_choices ,       -1 ).contiguous()
												lowerCamelCase_					=       model(
												    __UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       labels=__UpperCamelCase ,       )
												self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_choices) )



					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       self.prepare_config_and_inputs()
												(
												    (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, 
												)					=       config_and_inputs
												lowerCamelCase_					=       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
												return config, inputs_dict





@require_torch
class    __A(					UpperCAmelCase  ,		UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     (
					    (
					        MobileBertModel,
					        MobileBertForMaskedLM,
					        MobileBertForMultipleChoice,
					        MobileBertForNextSentencePrediction,
					        MobileBertForPreTraining,
					        MobileBertForQuestionAnswering,
					        MobileBertForSequenceClassification,
					        MobileBertForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE           =     (
					    {
					        '''feature-extraction''': MobileBertModel,
					        '''fill-mask''': MobileBertForMaskedLM,
					        '''question-answering''': MobileBertForQuestionAnswering,
					        '''text-classification''': MobileBertForSequenceClassification,
					        '''token-classification''': MobileBertForTokenClassification,
					        '''zero-shot''': MobileBertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE           =     True

					def  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Union[str, Any]=False ):
												lowerCamelCase_					=       super()._prepare_for_class(__UpperCamelCase ,       __UpperCamelCase ,       return_labels=__UpperCamelCase )

												if return_labels:
																			if model_class in get_values(__UpperCamelCase ):
																										lowerCamelCase_					=       torch.zeros(
																										    (self.model_tester.batch_size, self.model_tester.seq_length) ,       dtype=torch.long ,       device=__UpperCamelCase )
																										lowerCamelCase_					=       torch.zeros(
																										    self.model_tester.batch_size ,       dtype=torch.long ,       device=__UpperCamelCase )
												return inputs_dict

					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       MobileBertModelTester(self )
												lowerCamelCase_					=       ConfigTester(self ,       config_class=__UpperCamelCase ,       hidden_size=3_7 )

					def  lowercase__   (						self       :  List[str] ):
												self.config_tester.run_common_tests()

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_model(*__UpperCamelCase )

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_masked_lm(*__UpperCamelCase )

					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__UpperCamelCase )

					def  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__UpperCamelCase )

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_pretraining(*__UpperCamelCase )

					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_question_answering(*__UpperCamelCase )

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__UpperCamelCase )



					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_token_classification(*__UpperCamelCase )



def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[str]							)	->     Any:
							return torch.tensor(
							    UpperCAmelCase__      ,					dtype=torch.long      ,					device=UpperCAmelCase__      ,					)


lowercase   =	1E-3





@require_torch
@require_sentencepiece
@require_tokenizers
class    __A(					unittest.TestCase							):

					@slow
					def  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       MobileBertModel.from_pretrained("""google/mobilebert-uncased""" ).to(__UpperCamelCase )
												lowerCamelCase_					=       _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]] )
												with torch.no_grad():
																			lowerCamelCase_					=       model(__UpperCamelCase )[0]
												lowerCamelCase_					=       torch.Size((1, 9, 5_1_2) )
												self.assertEqual(output.shape ,       __UpperCamelCase )
												lowerCamelCase_					=       torch.tensor(
												    [
												        [
												            [-2.473_6526E07, 8.269_1656E04, 1.652_1838E05],
												            [-5.754_1704E-01, 3.905_6022E00, 4.401_1507E00],
												            [2.604_7359E00, 1.567_7652E00, -1.732_4188E-01],
												        ]
												    ] ,       device=__UpperCamelCase ,       )

												# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
												# ~1 difference, it's therefore not a good idea to measure using addition.
												# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
												# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
												lowerCamelCase_					=       torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
												lowerCamelCase_					=       torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )

												self.assertTrue(lower_bound and upper_bound )







import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     (KDPMaDiscreteScheduler,)
					SCREAMING_SNAKE_CASE           =     1_0

					def  lowercase__   (						self       :  str ,       **__UpperCamelCase       :  Optional[int] ):
												lowerCamelCase_					=       {
												    """num_train_timesteps""": 1_1_0_0,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												}

												config.update(**__UpperCamelCase )
												return config

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

					def  lowercase__   (						self       :  Dict ):
												for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] ,       [0.0002, 0.002, 0.02] ):
																			self.check_over_configs(beta_start=__UpperCamelCase ,       beta_end=__UpperCamelCase )

					def  lowercase__   (						self       :  Union[str, Any] ):
												for schedule in ["linear", "scaled_linear"]:
																			self.check_over_configs(beta_schedule=__UpperCamelCase )

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

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config(prediction_type="""v_prediction""" )
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

												scheduler.set_timesteps(self.num_inference_steps )

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

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

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

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

												if torch_device in ["cpu", "mps"]:
																			assert abs(result_sum.item() - 4.6934E-07 ) < 1E-2
																			assert abs(result_mean.item() - 6.1112E-10 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 4.693_4286_5017_0972E-07 ) < 1E-2
																			assert abs(result_mean.item() - 0.0002 ) < 1E-3

					def  lowercase__   (						self       :  List[Any] ):
												if torch_device == "mps":
																			return
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config()
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

												scheduler.set_timesteps(self.num_inference_steps )

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

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

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

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

												if torch_device in ["cpu", "mps"]:
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3



					def  lowercase__   (						self       :  Optional[int] ):
												if torch_device == "mps":
																			return
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config()
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

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

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

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

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

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

												if str(__UpperCamelCase ).startswith("""cpu""" ):
																			# The following sum varies between 148 and 156 on mps. Why?
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3





